Matthis Manthe
email: matthis.manthe@insa-lyon.fr

Stefan Duffner
email: stefan.duffner@insa-lyon.fr

Carole Lartizien
email: carole.lartizien@creatis.insa-lyon.fr

Whole-brain radiomics for clustered federated personalization in brain tumor segmentation

Keywords: Federated learning, Federated personalization, Segmentation, Brain tumor segmentation

Federated learning and its application to medical image segmentation have recently become a popular research topic. This training paradigm suffers from statistical heterogeneity between participating institutions' local datasets, incurring convergence slowdown as well as potential accuracy loss compared to classical training. To mitigate this effect, federated personalization emerged as the federated optimization of one model per institution. We propose a novel personalization algorithm tailored to the feature shift induced by the usage of different scanners and acquisition parameters by different institutions. This method is the first to account for both inter and intra-institution feature shift (multiple scanners used in a single institution). It is based on the computation, within each centre, of a series of radiomic features capturing the global texture of each 3D image volume, followed by a clustering analysis pooling all feature vectors transferred from the local institutions to the central server. Each computed clustered decentralized dataset (potentially including data from different institutions) then serves to finetune a global model obtained through classical federated learning. We validate our approach on the Federated Brain Tumor Segmentation 2022 Challenge dataset (FeTS2022). Our code is available at (https:// github.com/MatthisManthe/radiomics_CFFL).

Introduction

Deep learning methods have shown significant success on a variety of medical image segmentation tasks [START_REF] Liu | A Review of Deep-Learning-Based Medical Image Segmentation Methods[END_REF][START_REF] Micha L Futrega | Optimized U-Net for Brain Tumor Segmentation[END_REF]. These methods require a large quantity of data to perform well. The construction of inter-institution datasets is constrained by data regulations and overall sensitivity of health data.

Federated learning has been intensively studied within the last years in medical imaging [START_REF] Li | Privacy-Preserving Federated Brain Tumour Segmentation[END_REF][START_REF] Liu | FedDG: Federated Domain Generalization on Medical Image Segmentation via Episodic Learning in Continuous Frequency Space[END_REF][START_REF] Xu | Closing the generalization gap of cross-silo federated medical image segmentation[END_REF]. This paradigm designates training of a machine learning model on a decentralized dataset, enabling the collaboration of different institutions to train a model without sharing data. Convergence speed and final accuracy of federally trained models can however be weakened by the statistical heterogeneity of local datasets. In that sense, multiple ideas have been proposed to improve robustness of the standard Federated Averaging (FedAvg) algorithm [START_REF] Mcmahan | Communication-Efficient Learning of Deep Networks from Decentralized Data[END_REF] reducing the required number of communication rounds and bringing models' accuracy closer to centralized performance [START_REF] Li | Federated Optimization in Heterogeneous Networks[END_REF][START_REF] Sai | SCAFFOLD: Stochastic Controlled Averaging for Federated Learning[END_REF]Wang et al., 2020b;[START_REF] Tang | Virtual Homogeneity Learning: Defending against Data Heterogeneity in Federated Learning[END_REF].

Personalized federated learning has been recently introduced as training one model per specific institution while benefiting from others. Main axes of research in this domain revolve around training one model per participating institution through adaptations of meta-learning [START_REF] Fallah | Personalized Federated Learning with Theoretical Guarantees: A Model-Agnostic Meta-Learning Approach[END_REF][START_REF] Alp | Debiasing Model Updates for Improving Personalized Federated Training[END_REF], multi-task learning [START_REF] Marfoq | Federated Multi-Task Learning under a Mixture of Distributions[END_REF], leveraging partial model sharing (Arivazhagan et al., 2019;[START_REF] Pillutla | Federated Learning with Partial Model Personalization[END_REF], local finetuning [START_REF] Li | Ditto: Fair and Robust Federated Learning Through Personalization[END_REF][START_REF] Yu | Salvaging Federated Learning by Local Adaptation[END_REF] or hypernetworks [START_REF] Shamsian | Personalized Federated Learning using Hypernetworks[END_REF]. Clustered federated learning has also been proposed [START_REF] Ghosh | An efficient framework for clustered federated learning[END_REF][START_REF] Sattler | Clustered Federated Learning: Model-Agnostic Distributed Multitask Optimization Under Privacy Constraints[END_REF] as clustering institutions with similar local distribution and building one model per cluster.

We propose a novel personalization technique tailored to medical image segmentation. In realistic applications of federated learning, participating institutions use different acquisition methods (scanners, acquisition parameters, ...) inducing a feature shift between local datasets. We focus on developing a method specifically for this type of heterogeneity. Furthermore, state-of-the-art methods hypothesize homogeneous local distribution associated to an institution, which is not necessarily the case as an institution can use multiple scanners or vary the acquisition methods depending on the situation. We introduce the idea of sample-level clustered federated learning accounting for both inter and intra-institution heterogeneity while limiting the amount of transmitted information, preserving as much as possible data privacy. Our method enables to build a model for each isolated type of image volume appearance. We apply and validate our approach on the task of brain tumor segmentation based on the FeTS challenge dataset (based on BraTS challenge dataset) in which both inter and intra-institution feature shifts could be verified.

Method

An overview of the proposed method is depicted on Figure 1. Each institution computes a set of features (first and second order intensity statistics) on each multimodal volume, and sends these feature vectors to the server. The latter normalizes each feature of each received vector and computes clusters in this normalized radiomic feature space. In parallel, classical FedAvg is performed for a certain amount of communication rounds to build an initial global model. Then, FedAvg is performed for each cluster with the previously federally trained model as initialization, giving one final model per cluster of samples with homogeneous texture.

Formally, let K be the number of institutions, each with a local dataset

D k := {(x k,i , y k,i )} n k i=1 with x k,i ∈ X = R m×h×w×d the multimodal MRI scan to segment, y k,i ∈ Y = {0, 1} l×h×w×d
its associated multi-label ground-truth segmentation map, n k the local dataset size of institution k and N = K k=1 n k the total number of samples. We note w ∈ W = R p the parameters of the neural network to be optimized for the downstream task.

Radiomic features extraction Each institution extracts a set of radiomic features (firstorder and texture features) from each modality and volume. Features of different modalities for a same patient are concatenated into a single feature vector. As opposed to classical approaches with radiomics which try to characterize the texture of a tumor (Shur et al., 2021), we compute radiomic features on whole-brain masks on each modality, thus mainly on brain regions which should have a similar appearance with similar acquisition protocols (scanner, parameters, etc.). Formally, for each sample x k,i of institution k a feature vector f k,i ∈ R R is computed and transmitted to the server.

Server-side clustering After retrieving the feature vector associated to each volume of each institution, the server normalizes each feature of each vector. For a given feature with index r, we note f r its value in the feature vector f . In this study, the chosen normalization follows Equation (1) with P r min and P r max being two chosen percentiles on the pooled set of features across all institutions.

f r k,i = max(0, min(1, f r k,i -P r min P r max -P r min )) (1)
To account for highly correlated features, a principal component analysis (PCA) is applied on the set of normalized feature vectors, followed by the computation of C clusters by fitting a Gaussian mixture model (GMM). For training and inference purpose, the normalization parameters, PCA model and GMM are returned to each institution. We note Cluster : R R → {1, ..., C} the application of this clustering process on features extracted from a volume and Dc :=

K k=1 {(x k,i , y k,i ) | Cluster( fk,i ) = c, i ≤ n k }
the set of samples assigned to cluster c decentralized over the K institutions. We note n c,k the number of samples of institution k assigned to cluster c, and N c = K k=1 n c,k the total size of Dc .

Global federated learning initialization Following [START_REF] Mcmahan | Communication-Efficient Learning of Deep Networks from Decentralized Data[END_REF], we use FedAvg to compute an initial global model w init . Given a loss function l : W × X × Y → R, the classical federated objective can be defined as:

w * = argmin w∈W K k=1 n k i=1 l(w, x k,i , y k,i ) .
(2)

At each communication round t, each institution performs E local epoch(s) of stochastic gradient descent (SGD) starting from the current global model w t , giving K updates ∆w t k = w t k -w t to aggregate. The server-side aggregation step is a weighted averaging of these updates

w t+1 = w t + K k=1 n k N ∆w t k .
Clustered federated finetuning Given the clustering model computed at server-side, we are now able to define a novel clustered federated learning objective:

{w * c } C c=1 = argmin {wc} C c=1 ∈W C K k=1 n k i=1 l(w Cluster( fk,i ) , x k,i , y k,i ) , (3)
where w c is the parameter set of the global federated model finetuned on dataset Dc of cluster c. Note that this objective is different from the one defined in recent clustered federated learning approaches such as [START_REF] Ghosh | An efficient framework for clustered federated learning[END_REF] as we compute clusters at sample level, not at institution level, enabling to account for an intra-institution heterogeneity. We Nc ∆w t c,k . Inference Given a new sample x ∈ X to segment, the whole clustering pipeline is applied to determine which of the C cluster models must be used. That is, radiomic features f are extracted from x , normalized following Equation (1), reduced through the computed PCA model, assigned to cluster c using the GMM and segmented with model w c .

Experiments

Datasets

Experiments were led using The MICCAI's Federated Brain Tumor Segmentation 2022 Challenge dataset (FeTS2022) [START_REF] Bakas | Advancing The Cancer Genome Atlas glioma MRI collections with expert segmentation labels and radiomic features[END_REF][START_REF] Pati | The Federated Tumor Segmentation (FeTS) Challenge[END_REF][START_REF] Reina | OpenFL: An open-source framework for Federated Learning[END_REF]. This dataset is based on the Brain Tumor Segmentation 2021 Challenge dataset (BraTS2021). Consisting of 1251 multi-modal brain MRI scans (T1, T1ce, T2 and FLAIR) of size 240 × 240 × 155 with isotropic 1mm 3 voxel size along with their multi-label tumor segmentation masks including 3 labels, namely enhancing tumor (ET), tumor core (TC) and whole tumor (WT). The real-world partitioning along the 23 acquiring institutions is provided in addition to the samples, enabling to simulate federated learning. Institutions' datasets sizes are very heterogeneous (cf Appendix A.1) with ∼61% of institutions owning less than 15 samples each. As we do not have the scanner information for every sample, an intra-institution feature shift may exist. This dataset served to evaluate the performance of the proposed clustered federated personalization method and compare it to state-of-the-art methods.

Dataset splitting In all of the experiments, a ∼70-15-15% train -validation -test split was followed, giving 833 training, 218 validation and 200 test samples. We computed such split institution-wise, giving a local training, validation and test dataset per institution. We refer to the global train, validation and test sets as the aggregation of each local set, preserving the representation of each institution in each partition. Due to the computational cost of training models on FeTS2022 and the limited amount of samples (< 5) provided by multiple institutions, we did not perform cross-validation.

Image volume pre-processing Each volume was resized to the bounding box containing all brain voxels, padded to a minimum size of 128 on each dimension if necessary. Each modality of each volume's intensities was then standardized to a zero-mean one-variance gaussian distribution to eliminate the feature shift due to absolute intensity values of scanners.

Radiomic feature-based clustering

Feature extraction and processing Ninety-three features including first-order statistics as well as higher order statistical features capturing textural information were extracted per modality using Pyradiomics [START_REF] Joost | Computational Radiomics System to Decode the Radiographic Phenotype[END_REF], thus leading to a feature vector of dimension 372. The estimation of these textural features derives from the computation of matrices describing the spatial and intensity relationship between each individual pixel and its neighbors in the image, including GLCM, GLRLM, GLSZM, NGTDM and GLDM [START_REF] Shur | Radiomics in oncology: A practical guide[END_REF]. These matrices require to discretize intensity values of the original images. In this study, we chose an absolute discretization technique based on fixing the histogram bin size to 0.09 for feature extraction. Features were extracted on whole brain masks. A list of the extracted features is provided in Appendix B.1. We first normalized the feature vectors following Equation (1) and setting P r min as the 2nd-centile and P r max as the 98th-centile. Dimension of the normalized feature vectors were then reduced to 30 through PCA, preserving 96.58% of the variance. We validated the radiomic features extraction on The Calgary-Campinas-359 (CC359) dataset (cf Appendix C).

Clustering We fitted a GMM by setting the number of clusters to 10 on the FeTS2022 samples with tied covariance matrix to account for the limited amount of samples compared to the number of remaining feature dimensions. The performance of the proposed radiomicbased clustering method is assessed visually based on the comparison of 2 t-SNE plots of the radiomic feature distribution where each sample is colored either by the label of its belonging institution or by the label of the cluster it was assigned to through the GMM.

Clustered federated finetuning (CFFT)

We evaluated the performance of the proposed federated personalization method for the multi-class segmentation task of the FeTS2022 brain MRI dataset based on standard DICE score and 95% Hausdorff distances.

Model architecture and data preprocessing The backbone architecture used in all experiments is a small-sized 3D U-Net (cf Appendix D) trained on 3D patches of size 128 × 128 × 128 with a batch size of 1. We used instance normalization without any learned parameters to make the model as robust as possible to any feature shift. If not specified, training includes data augmentation focused on reducing the feature shift (gaussian noise, gaussian smoothing, intensity scaling and gamma contrast adjustment).

Comparison with baseline methods We validated our approach against different baselines. We first trained a global model on the pooled FeTS2022 dataset (e.g. BraTS2021) with SGD, referred to Centralized in the following. FedAvg was used as the baseline global federated optimization algorithm, as described in Section 2. As a personalized FL baseline, we finetuned the FedAvg final model on each local dataset with SGD; this method is referred to as Local Finetuning. We then experimented with two versions of our CFFT method : the proposed CFFT version preserving the privacy of each clustered dataset Dc by finetuning with FedAvg and an ideal version referred to as CFFT ideal , which consists in finetuning on the pooled datasets Dc of each cluster with SGD.

Training hyperparameters In Centralized, Local Finetuning and pooled finetuning of CFFT ideal , a learning rate of 0.02 gave the best results. For federated counterparts, a learning rate of 0.05 was used. A weight decay of 10 -5 was used in all experiments, motivated by state-of-the-art publications (Wang et al., 2020a;[START_REF] Yuan | What do we mean by generalization in federated learning?[END_REF]

Results and Discussion

Performance of the radiomic features based clustering

We show on Figure 2(a) a t-SNE plot of the radiomic features computed on sample images of FeTS2022. It highlights both inter and intra-institution feature shift in this dataset. We do not have access to the scanner type (vendor, magnetic field) or acquisition parameter to establish further correlation with the observed clusters, but a visual analysis of some example 3D images belonging to different clusters highlights some pattern discrepancies. For example, within institution 4, we can distinguish two types of multi-modal volume appearance (cf Appendix B.2), confirming the existence of intra-institution feature shift, which is well captured by the proposed radiomic based GMM model (Figure 2(b)). Although out of the scope of this paper, we emphasize that feature normalization enabled to spot some outlier volumes (cf Appendix E).

Performance of the clustered federated finetuning (CFFT) method

We show in Table 1 andTable 2 Centralized on average. This was shown significant based on a two-tailed Wilcoxon signedrank test for both metrics with p < 0.01. All personalization methods also significantly improve the FedAvg DICE score in a similar fashion, with CFFT ideal giving a slight edge on average 95% Hausdorff distance. Note that CFFT ideal performs better than its federated counterpart CFFT as we could expect. The goal of this preliminary method is to produce clusters of sample with homogeneous appearance and texture. In that sense, the gap on average metrics between CFFT ideal and CFFT is smaller that between Centralized and FedAvg, motivating a reduction of the shift between institutions taking part in FedAvg in each cluster. Moreover, Figure 3 shows the label distributions of each computed cluster. They are relatively homogeneous, confirming the focus on the feature shift. Other types of shift such as label or concept shifts can be present in the dataset, and we give a hint on their existence in Appendix A.1 with relatively heterogeneous label distributions per institution. Our method's performance is on par with Local Finetuning while restricting its effect on feature shift. We emphasize that the proposed paradigm of targeting an identified type of shift would potentially generalize better to unseen data. On a more general note, the choice of the number of clusters is important. Using too few does not capture feature shift, using too many overfits on clustered training data. Ten clusters gave the best validation results. We must acknowledge the relatively high variability of clustering results, possibly due to the high number of dimensions remaining after PCA to preserve sufficient variance. It must also be noted that the high heterogeneity of the local dataset sizes poses problems to assess the performance of personalized methods, with some institutions only owning one or two test samples (cf Appendix A.2). Finally, we hypothesize in this preliminary work that the computed radiomic features are safe to share with a server. We provide a basic validation of the impracticability of reconstructing a volume based on the 93 features extracted per modality in Appendix F, but it remains an open question as to how these features could be leveraged by an attack against our method. Thus, this preliminary work could be improved by studying the compatibility of the framework with known privacy-preserving federated learning techniques such as differential privacy or homomorphic encryption, or developing a federated framework including PCA and clustering. Limiting the amount of computed features to the most relevant ones could also be beneficial while opening explainability opportunities. • First Order Statistics (18 features) (standard deviation is not included)

• Gray Level Cooccurence Matrix (GLCM) (24 features)

• Gray Level Run Length Matrix (GLRLM) (16 features)

• Gray Level Size Zone Matrix (GLSZM) (16 features)

• Neighbouring Gray Tone Difference Matrix (NGTDM) (5 features)

• Gray Level Dependence Matrix (GLDM) (14 features)

B.2. Visual examples

Figure 1 :

 1 Figure 1: Overall framework of radiomic feature-based clustered federated finetuning.

  optimize each cluster model w c by finetuning the global model w init with FedAvg on the decentralized dataset Dc for T c rounds. The aggregation step for the federated finetuning of cluster model w c at communication round t becomes w t+1 c

  Figure 2: t-SNE plots of radiomic features computed on FeTS2022 samples.

Figure 5 :

 5 Figure 5: FeTS2022 01172 MRI scans, owned by institution 4, assigned to cluster 7.

Figure 6 :

 6 Figure 6: FeTS2022 01195 MRI scans, owned by institution 4, assigned to cluster 4.

  . Centralized training was performed for 300 epochs, FedAvg for 300 communication rounds with one local epoch per round, Local Finetuning for 20 local epochs, CFFT ideal for 50 epochs and CFFT for 50 communication rounds with one local epoch. The best models at each epoch/communication round were selected based on the validation set performance for all methods. Best results were found by removing data augmentation for clustered finetuning methods, while keeping it for Local Finetuning.

  aggregated test DICE scores and 95% Hausdorff distances respectively of Centralized, FedAvg, Local Finetuning, CFFT ideal and CFFT, with samplewise standard deviation. Per institution results are given in Appendix A.2. On average Centralized training remains the gold standard with the best performance on both metrics. FedAvg produces a less robust model, with a gap of more than one DICE point compared to

Table 1 :

 1 Test DICE scores (mean ± standard deviation)

	Training algorithm	Average	TC	WT	ET
	Centralized	0.8912 ± 0.1201 0.8896 ± 0.1878 0.9188 ± 0.0859 0.8651 ± 0.1956
	FedAvg	0.8803 ± 0.1414 0.8722 ± 0.2251 0.9099 ± 0.0906 0.8588 ± 0.2005
	Local finetuning	0.8879 ± 0.1202 0.8876 ± 0.1863 0.9132 ± 0.0891 0.8629 ± 0.1960
	CFFT ideal	0.8887 ± 0.1239 0.8867 ± 0.1891 0.9139 ± 0.0892 0.8654 ± 0.1960
	CFFT	0.8874 ± 0.1254 0.8799 ± 0.2056 0.9120 ± 0.0907 0.8704 ± 0.1852
	Training algorithm	Average	TC	WT	ET
	Centralized	5.

1348 ± 6.2212 4.4407 ± 7.2052 7.0318 ± 11.0671 3.7770 ± 8.5314 FedAvg 5.8854 ± 7.7368 4.7745 ± 8.0444 8.8814 ± 15.9499 3.7091 ± 7.6837 Local finetuning 5.9334 ± 7.9817 4.8587 ± 9.3901 8.9284 ± 16.3270 3.5861 ± 7.5650 CFFT ideal 5.5915 ± 7.2588 4.6436 ± 7.3390 8.3688 ± 15.3738 3.5383 ± 7.5576 CFFT 5.8749 ± 7.6108 4.7359 ± 7.4458 8.8709 ± 16.4791 3.6520 ± 7.6291

Table 2 :

 2

	A.2. Per institution results
	Institution Test size Centralized FedAvg Local Finetuning CFFT ideal CFFT
	1	77	0.9300	0.9256	0.9280	0.9289	0.9285
	10	2	0.8044	0.8051	0.8076	0.8085	0.8072
	11	3	0.9069	0.9000	0.9006	0.9055	0.9053
	12	2	0.7386	0.7031	0.7131	0.7032	0.7106
	13	6	0.6248	0.4910	0.6090	0.5596	0.5781
	14	1	0.6261	0.6115	0.6237	0.6195	0.6100
	15	2	0.5924	0.5282	0.6296	0.6135	0.5539
	16	5	0.9185	0.9211	0.9265	0.9028	0.9095
	17	2	0.9162	0.9133	0.9133	0.9140	0.9101
	18	58	0.9187	0.9117	0.9082	0.9138	0.9119
	19	1	0.9659	0.9625	0.9634	0.9649	0.9650
	2	1	0.9231	0.9221	0.9221	0.9206	0.9160
	20	5	0.9386	0.9314	0.9354	0.9346	0.9358
	21	6	0.9594	0.9545	0.9559	0.9574	0.9599
	22	2	0.9600	0.9553	0.9553	0.9563	0.9570
	23	1	0.8881	0.8919	0.8937	0.8879	0.8867
	3	3	0.6956	0.5952	0.6946	0.6928	0.6681
	4	8	0.6687	0.6669	0.7005	0.6960	0.6943
	5	4	0.8057	0.7946	0.7925	0.8349	0.8204
	6	6	0.8762	0.8762	0.8762	0.8806	0.8844
	7	2	0.8895	0.8873	0.8924	0.8828	0.8804
	8	2	0.9631	0.9621	0.9621	0.9645	0.9634
	9	1	0.7895	0.8232	0.7818	0.8545	0.8416

Test 95% Hausdorff distances (mean ± standard deviation) Figure 3: Label distribution per computed cluster in FeTS2022. Green values correspond to the amount of samples associated to each cluster.

Table 3 :

 3 Average test dice scores per institution Institution Test size Centralized FedAvg Local Finetuning CFFT ideal CFFT

	1	77	3.6752	4.3375	4.5268	4.3651	4.4183
	10	2	7.5957	7.1775	7.1275	6.8905	7.1512
	11	3	3.1412	3.5267	3.2199	3.2042	3.0589
	12	2	10.6487	24.9708	26.7511	23.4790	28.9451
	13	6	8.1182	12.2476	13.0226	8.0737	10.0930
	14	1	3.2863	4.2002	3.3708	4.1926	5.0000
	15	2	15.2838	17.3938	18.8894	19.4479	24.4377
	16	5	4.3992	8.8826	8.7169	9.8859	9.9735
	17	2	1.7756	2.0977	2.0977	1.9249	2.2928
	18	58	4.7928	5.1874	5.0459	5.1053	4.8157
	19	1	1.6667	1.3333	1.3333	1.1381	1.1381
	2	1	5.0726	5.9127	5.9127	6.1498	8.7888
	20	5	5.8938	2.6021	2.4663	2.4494	2.3830
	21	6	2.1143	2.2983	2.3060	1.6677	1.5816
	22	2	2.4694	3.1271	3.1271	3.2594	15.0383
	23	1	4.2356	3.9560	3.7742	4.2201	4.2560
	3	3	6.5017	9.2293	7.6449	7.4780	8.9391
	4	8	14.1589	12.5674	12.6770	10.3157	10.3217
	5	4	5.2451	7.8477	7.7993	6.7148	7.6399
	6	6	10.0190	10.2264	10.2264	9.6674	9.5711
	7	2	12.2938	15.7393	14.9248	16.0055	16.4357
	8	2	1.2761	1.4267	1.4267	1.3291	1.3738
	9	1	12.8816	6.7070	7.9211	6.1545	6.5459

Table 4 :

 4 Average test hausdorff distances per institution

	Appendix B. Radiomic features extraction

B.1. Radiomic features list

Using Pyradiomics, 93 features are extracted per modality:

Acknowledgments

This work was partially supported by the Agence Nationale de la Recherche under grant ANR-20-THIA-0007 (IADoc@UdL). It was granted access to the HPC resources of IDRIS under the allocation 2022-AD011013327 made by GENCI.

Appendix A. Per institution analysis

A.1. Per institution label distribution and local datasets sizes

Appendix C. Radiomic features validation on CC359

Dataset The Calgary-Campinas-359 (CC359) dataset [START_REF] Souza | An open, multi-vendor, multi-field-strength brain MR dataset and analysis of publicly available skull stripping methods agreement[END_REF]. This dataset consists of 359 T1-weighted brain MRI scans of healthy subjects along with their "silverstandards" brain masks generated both using the STAPLE algorithm. These images were acquired on scanners from three vendors (Siemens, Philips and General Electric) at both 1.5 T and 3 T. Sixty exams were acquired per vendor and magnetic field strength, except for Philips 1.5T which totalizes 59 exams. This dataset, concatenating 6 series of exams of equal size and low intra-feature shift serves as a use case to validate the radiomic feature extraction process.

Parameters We fixed the histogram bin size to 0.15 for features extraction on the CC359 dataset. We normalized the feature vectors following Equation (1) and setting P r min as the 2nd-centile and P r max as the 98th-centile. Dimension of the normalized feature vectors is then reduced to 30 through PCA, preserving 99.96% of the variance.

Results

We show on Figure 7 a t-SNE plot of the radiomic features computed on CC359 after normalization and PCA where each label encodes for one of the 6 scanners on which the image were acquired (e.g. philips 3 corresponds to a 3T Philips scanner). Despite only dealing with healthy patients with a normalization process that significantly reduces feature shift, distinct clusters can be identified for each scanner manufacturer and magnetic field value. This demonstrates the capacity of the selected radiomic feature vector to capture textural patterns induced by the scanner characteristics.

Appendix D. Model architecture

We use a small-sized 3D U-Net as the backbone architecture. It includes 3 down-sampling and up-sampling paths. Stride 2 convolutions are used in down-sampling paths, and transpose convolutions for up-sampling paths. Kernel size is (3,3,3) for every convolution block. A first stride 1 convolution block outputs 16 channels, multiplied by 2 at each downsampling path to reach 128 channels in the bottleneck part of the network. Inference are done with a sliding window with an overlap of 0.5 and Gaussian aggregation of results on overlaps.

Appendix E. Outliers detection

For some extracted features, the normalization process clearly highlighted outliers. As example, the GLCM Cluster Prominence feature computed on the FLAIR n°1441 was two orders of magnitude higher than for any other volume. We verified that this volume has a particular appearance, with extreme intensities toward the eye balls (Figure 8). Such manually computed features can thus be leveraged for federated outlier detection without sacrificing too much privacy.

Appendix F. Privacy preservation basic validation

We explore in this section the efficiency of an attack on the radiomic features communicated during the proposed clustered federated finetuning process. Its objective is, given every normalized feature vector computed on the train and validation T1 volumes, to train a decoder to reconstruct the associated volumes. Each volume was standardized and resized to 128 3 voxels to simplify the task.

Model

The model is composed of a first linear layer outputting 4096 values reshaped to [512, 2, 2, 2], followed by 6 3D residual convolutional blocks, upsampling each dimension by a factor of 2 while dividing the number of channels by 2. We use 3D batch normalization and LeakyRelu activation function with a slope of 0.2 between each layer. The model is composed of approximately 20 millions parameters.

Training parameters We train this model using the same training -validation -test split as for the original segmentation task. The loss and metric used is a standard MSE. We use Adam with a learning rate of 1e-4 and a weight decay of 1e-5 for 300 epochs with a batch size of 2. The final model is selected based on best validation performance.

Result We show in Figure 9 the training and validation loss curves. After only 20 epochs, the model starts to overfit on the training set with a stagnating validation performance. We show in Figure 10 two slices of reconstruction outputs of the test set. The model is only able to reconstruct an average volume, validating the fact that the transmitted feature vectors do not contain enough information to reconstruct a volume, even the tumorous parts. The proposed scheme of attack is also unrealistically powerful, as it presumes a large amount of already leaked data.


Hang P Vu

Luong N Nguyen
email: luongngoc.nguyen@uts.edu.au

Geoffroy Lesage

Long D Nghiem

Synergistic effect of dual flocculation between inorganic salts and chitosan on harvesting microalgae Chlorella vulgaris

Keywords: Ferric Chloride, Aluminium sulphate, Charge neutralisation, Bridging, Dual flocculation, Polyacrylamide

The flocculation efficiency of microalgae Chlorella vulgaris for subsequent harvesting was investigated using single flocculants of inorganic salts, synthetic polymer, chitosan and dual flocculants of inorganic salts and chitosan. Synthetic polymer (Flopam T M ) could achieve over 90% optical density removal (OD680 removal) at a low flocculant dose (20 to 40 mg polymer per litre of algal suspension) through the bridging mechanism and charge neutralisatio n.

Inorganic salts (i.e. ferric chloride and aluminium sulphate) and chitosan individually resulted in low flocculation efficiency (<90%) despite high dose (i.e. 160 to 200 mg per litre of algal suspension). The dual flocculation combining ferric chloride or aluminium sulphate with chitosan induced synergistic effects, resulting in >80% flocculation efficiency, significa nt ly higher than the sum of each individual flocculation. The improvement in flocculation efficie nc y was 57 and 24% respectively for ferric chloride/chitosan and aluminium sulphate/chitosa n.

Charge neutralisation of microalgal cells by ferric chloride or aluminium sulphate combined with bridging by chitosan produced the synergy.

Introduction

Microalgae are among the most important organisms in ecological evolution and history of the Earth. They have the potential to shape our future with a wide range of promising applications that tackle worldwide issues. The global fossil fuel supply is depleted and has caused destructive environmental effects over its life cycle. There is growing interest in microalgal biomass as renewable and environmental-friendly feedstock for third-genera t io n biofuel [START_REF] Vo Hoang Nhat | Can algae-based technologies be an affordable green process for biofuel production and wastewater remediation?[END_REF][START_REF] Ma | Biodiesels from microbial oils: Opportunity and challenges[END_REF]. The nutritive value of microalgal biomass for human as well as their versatile biochemical features have allowed for the production of health supplements, bioactive compounds, food additives and biotechnology applications, although there are still several hurdles in terms of socio-economic aspects [START_REF] Koyande | Microalgae : A potential alternative to health supplementation for humans[END_REF][START_REF] Rizwan | Exploring the potential of microalgae for new biotechnology applications and beyond: A review[END_REF][START_REF] De La Noue | The potential of microalgal biotechnology: A review of production and uses of microalgae[END_REF]. In particular, harvesting has been a major technical and economic bottleneck in microalgal biomass production due to low cell concentrations in cultures (0.5 to 5 g/L), small cell size (< 30 µm), the stability of cell suspension and variation in culture medium [START_REF] Singh | Microalgae harvesting techniques: A review[END_REF][START_REF] Klein-Marcuschamer | A matter of detail: Assessing the true potential of microalgal biofuels[END_REF][START_REF] Edzwald | Bubbles, Coagulants, and Dissolved Air Flotation[END_REF][START_REF] Vandamme | Flocculation of microalgae using cationic starch[END_REF]. Currently, microalgal harvesting is the most expensive step (i.e. 20-30% of total cost) in the process of microalgal biomass production [START_REF] Singh | Microalgae harvesting techniques: A review[END_REF][START_REF] Molina Grima | Recovery of microalgal biomass and metabolites: process options and economics[END_REF].

The microalgal harvesting techniques include coagulation, flocculation, flotation, membrane filtration and centrifuge [START_REF] Singh | Microalgae harvesting techniques: A review[END_REF][START_REF] Barros | Harvesting techniques applied to microalgae: A review[END_REF][START_REF] Leite | Coagulation and dissolved air flotation as a harvesting method for microalgae cultivated in wastewater[END_REF]. Amongst them, flocculation has received signific a nt attention for its simple operation and relatively low-cost approach, but efficiency is dependent on flocculant type [START_REF] Vandamme | Flocculation of microalgae using cationic starch[END_REF][START_REF] Barros | Harvesting techniques applied to microalgae: A review[END_REF][START_REF] Okoro | Microalgae cultiva tio n and harvesting: Growth performance and use of flocculants -A review[END_REF]. Available chemical flocculants for microalgal harvesting can be grouped into three categories: (i) inorganic flocculants such iron and aluminium salts, (ii) synthetic polymer such as polyacrylamide and polyelectrolyte and (iii) natural organic polymers such as chitosan and cationic starch [START_REF] Vandamme | Flocculation of microalgae using cationic starch[END_REF][START_REF] Okoro | Microalgae cultiva tio n and harvesting: Growth performance and use of flocculants -A review[END_REF]. Synthetic polymers often provide high harvesting efficiency at low dose [START_REF] Nguyen | Validation of a cationic polyacrylamide floccula nt for the harvesting fresh and seawater microalgal biomass[END_REF]. However, these polymers are expensive. Inorganic flocculants such as ferric chloride and aluminium sulphate are less expensive but require a higher dose.

Contamination and/or discolouration of microalgal biomass are possible concerns when using inorganic salts. The presence of these salts in the harvested biomass hinders its applications for biofuel and pigment extraction [START_REF] Barros | Harvesting techniques applied to microalgae: A review[END_REF]. These issues with the quality of the harvested biomass can be avoided by using natural polymers like chitosan. Chitosan is a promising flocculant due to its advantages (e.g. natural product, biodegradation and non-toxic) [START_REF] Barros | Harvesting techniques applied to microalgae: A review[END_REF][START_REF] Şirin | Harvesting the microalgae Phaeodactylum tricornutum with polyaluminum chloride, aluminium sulphate, chitosan and alkalinity-induced flocculation[END_REF]. It has been demonstrated that chitosan residual in the culture media (i.e. after biomass harvesting) is nontoxic to microalgae. This feature enhances the reusability of the culture media, which is a potential option to reduce cost [START_REF] Şirin | Harvesting the microalgae Phaeodactylum tricornutum with polyaluminum chloride, aluminium sulphate, chitosan and alkalinity-induced flocculation[END_REF]. However, the expensive cost around 20 to 50 USD/kg of chitosan (depending on the purity) sets back its large-scale application [START_REF] Barros | Harvesting techniques applied to microalgae: A review[END_REF][START_REF] Alibaba | Chitosan products[END_REF].

Inorganic salts provide flocculation through neutralising microalgal cell charge while chitosan flocculates microalgal biomass through bridging [START_REF] Barros | Harvesting techniques applied to microalgae: A review[END_REF]. Therefore, it is hypothes ized that the combination of these two mechanisms can enhance flocculation efficiency or harvesting efficiency. [START_REF] Loganathan | Removal of microalgae from seawater using chitosan-alum/ferric chloride dual coagulations[END_REF] reported that a combination of alum and chitosan as flocculant aid induced a synergistic impact on harvesting seawater microalgae [START_REF] Loganathan | Removal of microalgae from seawater using chitosan-alum/ferric chloride dual coagulations[END_REF]. The author indicated that a reduction of 20 mg flocculants per litre of algal suspension was achieved while maintaining the harvesting efficiency over 95% [START_REF] Loganathan | Removal of microalgae from seawater using chitosan-alum/ferric chloride dual coagulations[END_REF]. However, there has yet been any studies on freshwater Chlorella vulgaris harvesting using this type of flocculant combination. The most similar approach combining ferric chloride and polyethylene was conducted by Gorin et al. [START_REF] Gorin | Methods coagulation/flocculation and flocculation with ballast agent for effective harvesting of microalgae[END_REF]. They reported an increase from 60% to 90% flocculation efficiency of Chlorella vulgaris using dual flocculation. However, the dose of ferric chloride was very high at 500 mg/L, which may cause unfavourable effects on algal cells. Given the benefits (e.g. biological and pharmaceutical properties, nutrient contents for human health) of microalgae Chlorella vulgaris [START_REF] Sharifah | Benefits of live phytoplankton, Chlorella vulgaris, as a biocontrol agent against fish pathogen Vibrio anguillarum[END_REF], effective harvesting of its biomass without compromising the cell quality will be a stepping stone to mass production of microalgal based products.

This study aims to compare the performance of four types of flocculants including two metal salts ferric chloride and aluminium sulphate, polyacrylamide polymer Flopam T M and organic polymer chitosan on Chlorella vulgaris harvesting. From the results of these single floccula t io n tests, dual flocculation tests using inorganic salt followed by chitosan addition were conducted to determine to what extent this strategy can improve the efficiency and reduce flocculant dose of the process. Optical density removal, turbidity and zeta potential were measured to evaluate flocculation efficiency and mechanisms. The results from this study is expected to contribute to the greater research on optimising microalgae harvesting, particularly using floccula t io n process.

Materials and methods

Microalgal suspension and materials

Microalgal suspension sample was prepared using the freshwater species Chlorella vulgaris (CS-41) (Australian National Algae Culture Collection, CSIRO Microalgae Research, Hobart, TAS). This species was grown in the MLA medium (Algaboost; Wallaroo, SA, Australia) to its mid-stationary phase following the previous protocol [START_REF] Nguyen | Validation of a cationic polyacrylamide floccula nt for the harvesting fresh and seawater microalgal biomass[END_REF]. Its growth phase was monitored daily by measuring the optical density of the solution at wavelengths of 680 nm.

Microalgal suspensions at a mid-stationary growth phase were used for harvesting experiments (Section 2.2). The mid-stationary growth phase was selected because of its peak in biomass production. In the microalgal growth cycle, the mid-stationary phase occurs right after their population increased exponentially. At the mid-stationary phase, cell divisions had slowed down significantly due to high cell density thus the decrease in feeding factors (e.g. nutrients, light, pH and carbon dioxide). Thus, harvesting microalgae at mid-stationary phase is a common protocol.

Anhydrous ferric chloride powder (> 98% purity) was supplied by Chem-Supply (Australia).

Aluminium sulphate hydrate (54 -59% assay) was purchased from Sigma-Aldrich (Australia). Cationic polyacrylamide polymer Flopam T M (model no. FO4808) with very high molecular weight was obtained from SNF Australia. Stock solutions of 2 g/L were prepared for each of these flocculants in 200 mL of Milli-Q water and mixed at 100 rpm for one hour. Cationic polyacrylamide polymer (2 g/L) was used within one hour of preparation to avoid polymer hydrolysis. Chitosan (originated from chitin shells of crustaceans) was purchased from Sigma-Aldrich (Australia). Since chitosan is insoluble in water, 0.4 g of chitosan was dissolved in 10 mL of 0.1% HCl solution, followed by the dilution with 190 mL of Milli-Q water to obtain the desired 2 g/L stock concentration. The stock solutions were stored in room temperature and used within two days of preparation.

Flocculation experiment

A 4G Platypus Jar Tester (Australia Scientific, Kotara NSW) was used in floccula t io n experiments. Samples of 200 mL microalgal suspension were added to 500 mL beakers.

Flocculant was introduced to each beaker to obtain a predetermined dose. The microalga l suspension was rapidly mixed at 200 rpm for one minute followed by 15 minutes of slow mixing at 50 rpm. The flocculated microalgal suspension was allowed to settle for one hour. A supernatant sample of 15 mL was pipetted from the suspension at between one-and two-third from the bottom for measurement of the flocculation efficiency.

In the individual flocculation experiments, a dose-response relationship protocol was used to define the optimal flocculant dose. Ferric chloride and aluminium sulphate were dosed at a concentration of 40 to 180 g per litre of algal suspension. This corresponds to 112 to 504 mg flocculant/g dry biomass. Flopam T M was dosed at 10 to 100 mg per litre of algal suspension (i.e.

28 to 280 mg polymer/g dry biomass). While chitosan dose was 40 to 200 mg per litre of algal suspension equivalent to 112 to 560 mg chitosan/g dry biomass.

In the dual flocculation experiments, ferric chloride or aluminium sulphate was added at a fixed 40 mg per litre algal suspension during the rapid mixing stage (200 rpm). This concentration was selected as it was the lowest dose tested in the single floccula t io n experiments, thus emphasise the purposes of dual flocculation i.e. limiting the number of metal salts in harvested biomass and minimising potential contamination of algal cells. Chitosan was then added at doses of 0 to 80 mg per litre of algal suspension (i.e. 0 to 224 mg/g dry biomass)

during the slow mixing period (50 rpm).

Analytical methods

The optical density of C. vulgaris solution before and after flocculation was measured at a wavelength of 680 nm using the UV spectrophotometer (UV 6000 Shimadzu; Ermingto n, NSW, Australia). The flocculation efficiency was then calculated using these values as below:

Flocculation efficiency (%) = � 𝑂𝑂𝑂𝑂 𝑖𝑖-𝑂𝑂𝑂𝑂 𝑓𝑓 𝑂𝑂𝑂𝑂 𝑖𝑖 � × 100
Eq. ( 1)

Where ODi and ODf are the optical density of the culture before and after flocculant addition.

Each flocculant was repeated three times for individual and dual flocculation experiments.

A volume of 150 mL of microalgae cell suspension was filtered through a 1.1 µm preweighed glass fibre filter paper. The biomass concentration of the microalgae culture was then obtained gravimetrically by drying the sample on the filter paper overnight at 60 o C to a constant weight. The weight of the final filter paper was used to determine the dry microalgal biomass.

The Zetasizer nano instrument (Nano ZS Zen 3600; Malvern, UK) was used to measure the zeta potential of the microalgae solutions using the 15 mL aliquots taken before and after flocculation.

The solution pH was measured using a pH/conductivity meter (Orion 4-Star Plus Thermo Scientific; Waltham, MA, USA). Turbidity of the microalgae solution before and after flocculation was measured using a portable turbidity meter kit (Apera TN400; Colombus, OH, USA) with accuracy ±1% or 0.02 NTU. Statistical analysis was performed using Student's unpaired t-Test, with a two-tailed distribution.

Results and discussion

Optimal doses for ferric chloride and aluminium sulphate flocculants

A dose-response relationship can be observed when ferric chloride and aluminium sulphate were used individually as the flocculant (Fig. 1). The flocculation efficiency was less than 40% OD680 removal at 120 mg flocculant per litre of algal suspension (i.e. 336 mg flocculant/g dry biomass), after which the flocculation efficiency steadily increased (Fig. 1). A higher flocculation efficiency was achieved as 86% and 77% at 160 mg ferric chloride per litre of algal suspension (i.e. 448 mg/g dry biomass) and 180 mg aluminium sulphate per litre of algal suspension (i.e. 504 mg/g dry biomass) respectively. Charge neutralisation is the main flocculation mechanism by inorganic flocculants [START_REF] Singh | Microalgae harvesting techniques: A review[END_REF][START_REF] Barros | Harvesting techniques applied to microalgae: A review[END_REF].

Small microalgae cells are very stable in suspension due to the repulsive force caused by their negatively charged surface (-20.2 mV for C. vulgaris in this study). Thus, positively charged ferric or alum ions are required for charge neutralisation to overcome this electrostatic stabilisation through neutralising the charge of microalgae cells [START_REF] Wyatt | Critical conditions for ferric chloride-induced flocculation of freshwater algae[END_REF]. This was demonstrated by the plateau region below 350 mg flocculant/g dry biomass (Fig. 1) where the OD680 removal value remained quite low, < 35% for ferric chloride and < 20% for aluminium sulphate.

Although the optimal flocculation efficiency was acceptable, it was achieved at very high doses of ferric chloride and aluminium sulphate. This aligns with the literature results in which improved flocculation performance (> 90%) of inorganic flocculants like ferric chloride and aluminium sulphate requires high dose (Table 1). The variation in the microalgal culture and growth conditions might be accountable for the difference in optimal doses among these studies. 3.2 Flocculation performance by organic polymers

Synthetic polyacrylamide polymers

Synthetic cationic polymer Flopam T M showed the highest OD680 removal of 96% at 20 mg polymer per litre of algal suspension (i.e. 56 mg polymer/g dry biomass) (Fig. 2). A further increase in its dose up to 100 mg per litre of algal suspension (i.e. 280 mg/g dry biomass) caused the flocculation performance to decrease gradually. Results in Fig. 2 suggest that polymer overdosing can be counterproductive. This observation is in good agreement with the literature [START_REF] Nguyen | Validation of a cationic polyacrylamide floccula nt for the harvesting fresh and seawater microalgal biomass[END_REF].

Flopam T M is a high molecule weight and highly charged cationic polymer. Thus, charge neutralisation is the first step of flocculation, followed by entanglement and bridging of algal cells and the polymer [START_REF] Pugazhendhi | A review on chemical mechanism of microalgae flocculation via polymers[END_REF][START_REF] Biggs | Aggregate structures formed via a bridging flocculation mechanism[END_REF]. As this process continues, more and more microalgae cells are bridged or connected to each another, forming bigger flocs. A combination of mechanis ms performed by synthetic cation polymer enhances its flocculation efficiency.

Natural polymer Chitosan

In the flocculation of C. vulgaris using natural polymer chitosan, the value of OD680 removal improved with the increasing doses (Fig. 3), suggesting a proportional relationship between flocculation efficiency and chitosan dose. At the lowest dose of 40 mg chitosan per litre of algal suspension (i.e. 112 mg chitosan/g dry biomass), the OD680 removal was 20%. This was increased to 62% when using 200 mg chitosan per litre of algal suspension (i.e. 560 mg chitosan/g dry biomass). Flocculation efficiency of chitosan in this study is not only much lower, but it also required a dose twenty times that of the synthetic cationic polymer Flopam T M to achieve the same OD680 removal around 60%.

Flocculation using chitosan works presumably based on a small degree of charge neutralisation and mostly bridging mechanism, similar to the synthetic cationic polymers made from polyacrylamide in section 3.2.1 [START_REF] Divakaran | Flocculation of algae using chitosan[END_REF][START_REF] Chen | A New Approach for the Flocculatio n Mechanism of Chitosan[END_REF]. pH plays a key role in the efficiency of chitosan flocculation since at both acidic and very alkaline condition, the performance is decreased [START_REF] Divakaran | Flocculation of algae using chitosan[END_REF][START_REF] Harith | Effect of differe nt flocculants on the flocculation performance of microalgae, Chaetoceros calcitrans, cells[END_REF]. Gualteri et al., 1988 explained that in an acidic environment, chitosan exists as a linear chain and remains dispersed due to the repulsive forces between closely placed -NH2 groups and -NH 3+ group carrying positive charge [START_REF] Gualtieri | Chitosan as flocculant for concentrating Euglena gracilis cultures[END_REF]. This prevents chitosan from effective ly flocculate the microalgae cells. With an alkaline pH, the positive charge of chitosan is gradually neutralised, thus charge neutralisation of microalgae cells becomes less efficient [START_REF] Harith | Effect of differe nt flocculants on the flocculation performance of microalgae, Chaetoceros calcitrans, cells[END_REF]. Optimal flocculation using chitosan is obtained within a narrow pH range of approximately 6 to 8 [START_REF] Divakaran | Flocculation of algae using chitosan[END_REF].

In this experiment, the pH of the microalgal solution after the addition of chitosan was 8.05.

However, the removal efficiency reported was relatively low with high dosage, leading to the subsequent study of dual flocculation using inorganic flocculants and chitosan.

Synergistic effect of dual flocculation

Improved flocculation using a combination of inorganic flocculants and chitosan

Signifcantly better OD680 removal efficiency was observed for dual flocculation combining inorganic salts with chitosan, compared to that achieved by individual flocculation (Fig. 4).

Dual flocculation using ferric chloride and chitosan achieved an OD680 removal of 81% at 80 mg chitosan per litre of algal suspension (i.e. 224 mg chitosan/g dry biomass).

Synergistic mechanisms of enhanced performance mechanisms

The combination of charge neutralization and bridging is the main reason for the observed synergy. By adding ferric chloride or aluminium sulphate as a primary flocculant in the rapid mixing step, negatively charged C. vulgaris cells were neutralised to higher zeta potential and no longer remained stable in suspension (Fig. 5). Collision among cells was initiated leading to the formation of small flocs. When chitosan was slowly mixed in at this stage, particle entrapment and bridging took place [START_REF] Loganathan | Removal of microalgae from seawater using chitosan-alum/ferric chloride dual coagulations[END_REF]. Chitosan chains attached to existing microalga lalum/ferric flocs and further agglomerated them into bigger masses (i.e. macroflocs of size >1 cm, data not shown). These combined mechanisms increased the flocculation efficiency of the dual experiment to above 80%, much greater than that achieved by solely ferric or aluminium flocculation (Section 3.3.1).

At high dose of chitosan (>70 mg/g dry biomass for ferric chloride/chitosan and >140 mg/g dry biomass for aluminium sulphate/chitosan), a synergistic effect is observed for charge neutralisation of the microalgae cells (Fig. 5). Flocculation using positively charged ferric chloride, aluminium sulphate and chitosan primarily work on the basis of neutralis ing negatively-charged algal cells to destabilise cells in suspension [START_REF] Singh | Microalgae harvesting techniques: A review[END_REF][START_REF] Barros | Harvesting techniques applied to microalgae: A review[END_REF]. Although the main mechanism of chitosan flocculation is bridging, the addition of chitosan at a higher dose in the dual flocculation still significantly increased the charge neutralisation compared to single ferric chloride or aluminium sulphate flocculation. At optimal chitosan dose, charge neutralisa tio n was 13.8 mV for ferric chloride/chitosan flocculation and 17.2 for aluminium sulphate/ chitosan flocculation (Fig. 5). A lower dose of chitosan (< 70 mg/g dry biomass) did not induce any synergistic effect because chitosan was working mostly on the bridging mechanism and charge neutralisation had a negligible effect on the dual flocculation performance.

Comparison of flocculants

An indicative cost analysis was conducted for each individual and dual flocculation to obtain an overview of the large-scale feasibility (Table 2). Flopam T M performed excellent floccula t io n of C. vulgaris cells, however, the cost per ton dry C. vulgaris biomass for it is estimated at 120 USD (Table 2). This value is more than the cost per ton of dry biomass for aluminium sulphate (105 USD) but less than that of ferric chloride (364 USD). Chitosan is the most expensive (i.e. 20-50 USD/kg) among all the flocculants investigated in this study. The cost to achieve >90% flocculation efficiency per ton of dry C. vulgaris biomass using chitosan is approximately 7280 USD (Table 2).

For dual flocculation, the combination of aluminium sulphate and chitosan would cost 4920 USD per ton dry C. vulgaris biomass, while it is 7925 USD for ferric chloride and chitosan combination. This suggests that by combining aluminium sulphate and chitosan, the cost could be reduced significantly by approximately 30%. Further research into the optimisation of dual flocculation for microalgae using inorganic flocculant and chitosan (e.g. biomass quality and quantity, processing times, species specific and toxicity), there is potential for prospective applications of this method in a large-scale environment.

Conclusions

A preliminary assessment of microalgal flocculation efficiency was reported in this study.

Individual flocculant including ferric chloride, aluminium sulphate and polymer chitosan required a high dose to achieve a benchmark of 90% harvesting efficiency. Polymer Flopam T M can effectively harvest microalgae at a lower dose and thus lower cost. A dual floccula tio n method combining ferric chloride or aluminium sulphate with chitosan resulted in a synergist ic effect. The synergistic effect was resulted from the interaction between charge neutralisa tio n and bridging mechanisms. The dual flocculation method has a great potential for large-scale microalgal harvesting application.

Figure 1 :

 1 Figure 1: The C. vulgaris flocculation efficiency indicated by optical density removal at λ =

Figure 2 :

 2 Figure 2: The flocculation performance of Flopam T M indicated by its optical density removal

Figure 3 :

 3 Figure 3: The effect on C. vulgaris flocculation using Chitosan, based on its optical density

Figure 4 :

 4 Figure 4: The synergistic effect of combining inorganic flocculant (a) ferric chloride and (b)

Figure 5 :

 5 Figure 5: The synergistic effect of dual flocculation using (a) ferric chloride with chitosan and

Table 2 :Flocculanta

 2 Cost comparison for types of flocculants or polymers used in this study based on their current market value. Prices are collected from Alibaba.com b Average value from indicative cost is used for calculation c Price is reported by SNF Australia

Table 1 :

 1 Summary of literature on the flocculation of Chlorella genus using aluminium sulphate and ferric chloride compared to the results from this study.

	Microalgae culture	Flocculant	Optimal dose (mg/g	Efficiency	References
	(dry biomass g/L)		dry biomass)	(%)
	Chlorella vulgaris (0.36)	Aluminium sulphate Ferric chloride	504 448	77 86	This study
	Chlorella vulgaris (1.2) Aluminium sulphate	2083	> 90	[21]
		Chitosan	208
	Chlorella sp. (0.12)	Aluminium sulphate	1266	> 90	[22]
		Ferric chloride	1191
	Chlorella vulgaris	Aluminium sulphate	350	> 95	[23]
	(freshwater) (1.0)	Ferric chloride	300
	Chlorella vulgaris	Aluminium sulphate	600	> 95	[24]
	(0.25)

Acknowledgements

The authors acknowledged the funding supports from the Faculty of Engineering and Information Technology, University of Technology Sydney under Tech lab BlueSky Project funding scheme 2019.