K Staniszewski

R Sepehr
email: rsepehr@uwm.edu

S Maleki
email: maleki@uwm.edu

C M Sorenson
email: cmsorenson@wisc.edu

N Sheibani
email: nsheibanikar@wisc.edu

M Ranji
email: ranji@uwm.edu

Automated Evaluation of Retinopathies Using Image Cytometry

Keywords: Classification, Fractals, Retinopathy, Fluorescence Microscopy, Image Cytometry

Retinopathic changes are common to many ocular diseases and if detected early, much of the change caused by various injuries can be prevented or in some cases reversed. However, detection and quantification of these changes currently requires tedious invasive and manual examination of retinal wholemount images. To remedy the quantitative limitations, we have designed a software system to automatically determine the vascular cell counts and coverage of retina in wholemount trypsin digest images. To verify the utility of the system, retinal trypsin digests from wild type (bcl-2 +/+) and bcl-2 deficient (bcl-2 -/-) mice were compared. Bcl-2 is a critical regulator of apoptosis with a significant role in retinal angiogenesis and vascularization. The bcl-2 -/-mice exhibited significant reduction in retinal vascular density and complexity. Thus, our results show the potential for automated evaluation of retinal trypsin digests delineating the differences between the wild type and bcl-2-deficient retinal vasculature.

Introduction

Retinopathy can be caused by several disorders including, but not limited to, diabetes [START_REF] Sjolie | Retinopathy and vision loss in insulin-dependent diabetes in Europe. The EURODIAB IDDM Complications Study[END_REF], hypertension [START_REF] Davis | Prevalence of renovascular hypertension in patients with grade III or IV hypertensive retinopathy[END_REF], and age related macular degeneration [START_REF] Bird | An international classification and grading system for age-related maculopathy and age-related macular degeneration. The International ARM Epidemiological Study Group[END_REF]. The manifestation of diabetic retinopathy involves loss of existing vessels, ischemia, and growth of new abnormal blood vessels in the retina. Unfortunately, the pathology of this injury is not currently fully understood, and is currently a prominent area of research. However, detection of this injury currently relies on laborious manual analysis of retinal wholemount trypsin digests and subjective measures. Therefore, it would be beneficial to create a system to automatically and reproducibly detect these changes. With this end in mind, we have created a software package to determine retinal vascular cell density and ratios. This package is capable of analyzing bright field and fluorescent images of the retinal vasculature for quantitative evaluations in a short period of time, facilitating faster and more reliable determination of vascular changes. In order to quantify the amount of injury experienced by the retina, the package measures the cell count and vessel coverage of high-resolution fields of view of the retinal wholemount trypsin digests, while requiring minimal input from the user.

Bright field and fluorescence images of stained wholemount retinal trypsin digests were acquired in digital formats, and the fluorescence images were thresholded using the histograms of intensity values in the fluorescent channel to separate vasculature from background. The histogram of these images showed a bimodal distribution, where the lower intensity mode corresponds to background and the higher intensity mode corresponds to the vasculature. With this in mind, the threshold level is determined independently for each image as the minimum value of the histogram (i.e. the least frequent intensity) between these two modes, leading to a binary image which contains the structure of the vasculature. The images acquired were saved as TIFF files and analyzed in MATLAB (The MathWorks).

Feature Extractions

Vascular Cell Detection and Count. The location of cells in each image was determined using the following segmentation algorithm. The location of foreground markers (cells) was detected by applying an FIR semi-Laplacian filter to enhance the contrast of the circular objects (using the center and surround method). The background markers were computed by applying watershed detection on the resultant binary mask of the previous stage. Then the gradient of the images is calculated and modified so that its intensity becomes zero in both foreground and background objects. The watershed transform of the modified gradient image is computed and results in a binary mask containing the borders of the objects. Automatic cell segmentation resulted in an accuracy of ~90% compared to manual cell detection and counting.

Retinal Vessel Coverage. The vessel coverage is defined as the amounts of pixels which are "on" in the thresholded image, which correspond to the area covered by the entire retinal vasculature.

Results

The Retinas from Bcl-2 -/-Mice Have Fewer Numbers of Vascular Cells. The results shown in figure 1 indicate that those retinas from a bcl-2 -/-mice contained fewer cells than the retinas from bcl-2 +/+ mice. The 6 week old group images contained a mean number of cells of 108.375 for the bcl-2 -/-mice and 152.875 for bcl-2 +/+ mice (p < 0.005). Similarly, in the 3 week old group, the mean number of cells was 137 for bcl-2 -/-mice compared with 177.75 for the bcl-2 +/+ mice (p < 0.001). Figure 1 shows the mean and standard deviations of the cell counts within each group. As expected, the mean values were higher in the bcl-2 +/+ group than those in the bcl-2 -/-group, supporting the fact that the bcl-2 gene is an apoptosis inhibitor with an important role in angiogenesis.

Wild Type Retinas have Higher Vessel Coverage. The results showed a significant difference in the 3 week old group, but a much smaller difference in the 6 week old group. In addition, the mean values of vessel caliber for the bcl-2 -/-and bcl2 +/+ mice in the 3 week old group were 7.6x10 5 and 8.7x10 5 (p < 0.05), respectively. Figure 1 shows the mean and standard deviations of the vessel coverage.

Discussion

We have developed a system capable of automatically quantifying several parameters of retinal trypsin digest images. The features used for this classification include the cell count and vasculature area. The cell count is an important indicator of retinopathy, as it is directly related to the early loss of vascular cell and later growth of new blood vessels. Therefore, we expect the concentration of cells in the retina to be a precursor to injury. Similarly, as new vessels grow, the area of the vasculature increases, so the measurement of this feature is also important.

The automated detection of the above features is important for two reasons. First, the speed of quantification capable with modern computers will allow for faster research and, therefore, sooner understanding of the causes and effects of retinopathy. Second, the automation leads to a reproducible result regardless of the person examining the retina. As previously mentioned, current measurement of these parameters requires manual examination of these images, including counting individual cells one by one. This is not only time consuming, but is also prone to error, both of which are remedied by the automation used in this software system.

Conclusion

We can automatically quantify several features of the retinal vasculature with high sensitivity. This is important in understanding retinal injuries related to a wide range of diseases including diabetes, hypertension, and age related macular disease. By automatically measuring the cell count and vessel coverage of the images, we were able to show significant differences in bcl-2 deficient compared to bcl-2 +/+ retinas. With the addition of more features, we hope to create a system capable of detecting very small changes in the retinal vasculature and various cell types allowing for the earliest detection of early vascular changes.

Fig. 1 .

 1 Fig.1. Mean values and Standard Deviations of Cell Counts (a) and Vasculature Area (b). Sample bright field (c and d) and fluorescence (e and f) images from 6 week old bcl-2 -/-(c and e) and 3 week bcl-2 +/+ (d and f) mice. The bright field images have red lines surrounding detected cells. * indicates a significant difference (p <0 .05).

Acknowledgement:

We would like to acknowledge the support of UWM research fellow award. CMS was funded, in part, by DK067120 from the National Institutes of Health and AHA research award (0950057G). NS is supported by NIH grants EY016995, and RC4 EY 021357 (NS) and an unrestricted departmental award from Research to Prevent Blindness. NS is a recipient of a Research Award from American Diabetes Association (1-10-BS-160) and Retina Research Foundation.


Cornel Zachiu

Nicolas Papadakis

Mario Ries

Chrit Moonen

Baudouin Denis De Senneville

⋆ Chrit

Moonen ⋆ Baudouin

Denis De Senneville

An improved optical flow tracking technique for real-time MR-guided beam therapies in moving organs

Keywords: Real-time MR-guidance, optical flow, HIFU, EBRT

à la diffusion de documents scientifiques de niveau recherche, publiés ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

INTRODUCTION

MRg-HIFU and EBRT, show great potential for the non-invasive treatment of tumors in abdominal organs. Therapy delivery in such areas is, however, hampered by respiratory motion. To overcome this problem, previous studies suggested combining high-frame-rate MR-imaging with a real-time optical flow algorithm such as suggested by Horn&Schunck [START_REF] Ries | Real-time 3D target tracking in MRI guided focused ultrasound ablations in moving tissues[END_REF][START_REF] Horn | Determining optical flow[END_REF]. However, this approach is intrinsically also sensitive to gray-level intensity variations from other sources than motion, such as in-flow enhancement/pulsations due to the cardiac cycle, which frequently leads to tracking errors. In this work, an improved real-time tracking algorithm with increased robustness versus such effects is proposed and experimentally compared to the original Horn&Schunck method in terms of quality of motion estimates in the presence of arterial pulsations.

MATERIALS AND METHODS

The original Horn&Schunck functional shown in equation ( 1) relies on a signal intensity conservation term and a regularization term, which imposes a smooth differentiable motion [START_REF] Horn | Determining optical flow[END_REF].

EL2L2(u, v) = Ω (Ixu + Iyv + It) 2 + α 2 ∇u 2 2 + ∇v 2 2 dxdy (1)
For motion tracking this functional has to be minimized in real-time for each image of the data stream. Problematic are hereby intensity variations due to arterial in-flow artifacts, which frequently violate the intensity conservation and thus lead to mis-registration. As consequence, we propose a modified L 2 -L 1 functional (equation ( 2)), which replaces the quadratic norm of the intensity conservation term by a linear norm:

EL2L1(u, v) = Ω |Ixu + Iyv + It| + β 2 ∇u 2 2 + ∇v 2 2 dxdy (2)
The idea is that this functional reduces the confidence in the conservation of signal intensity, relying more on the assumption of an elastic deformation. This leads to a better representation of elastic organ deformation in the vicinity of arterial signal fluctuations. The experimental validation was performed in the following way: Dynamic MR-imaging of both liver and kidney was performed under free-breathing conditions on the abdomen of two healthy volunteers, resulting in 1500 images for each volunteer. A second dataset was derived by applying retrospective cardiac gating (i.e. while respiratory motion is present, all images represent peak-systole) to serve as a gold standard. Subsequently, the registration error (i.e. registration based on the complete data vs. registration of the cardiac gated images) for both methods is compared.

RESULTS

As shown in figure 1 the proposed tracking algorithm performs significantly better in the upper liver and in particular in the vicinity of larger vessels, such as the hepatic arteries and the portal vein. With respect to performance, the L -L 1 algorithm converged on average in 25ms with a typical end-to-end processing latency (since the beginning of the MR-slice acquisition to the output of the motion fields) of under 100ms. Both the convergence time and the latency are well within the requirements for real-time guidance [START_REF] Ries | Real-time 3D target tracking in MRI guided focused ultrasound ablations in moving tissues[END_REF]. Note, however, that the lack of differentiability of the proposed functional made an implementation that respects such constraints a particularly challenging task.

CONCLUSION

The presented study proposes an improved MR-based real-time tracking method for respiratory motion. Compared to previous approaches, our method has shown increased robustness to in-flow / pulsation effects induced by the cardiac cycle. Additionally, the low end-to-end processing latency renders our method potentially suitable for real-time MR-guidance of HIFU and EBRT interventions in the abdomen, under free-breathing conditions.

  This work was supported by the Dutch Technology Foundation (STW) (project OnTrack #12813).

Fig. 1 .

 1 Fig. 1. Pixel-wise error with respect to their gold standard for the (a) Horn&Schunck and (b) L 2-L 1 method in the presence of arterial pulsations.4. CONCLUSION