A Method of Drusen Measurement Based on the Geometry of Fundus Reflectance

BACKGROUND: The hallmarks of age-related macular degeneration, the leading cause of blindness in the developed world, are the subretinal deposits known as drusen. Drusen identification and measurement play a key role in clinical studies of this disease. Current manual methods of drusen measurement are laborious and subjective. Our purpose was to expedite clinical research with an accurate, reliable digital method. METHODS: An interactive semi-automated procedure was developed to level the macular background reflectance for the purpose of morphometric analysis of drusen. 12 color fundus photographs of patients with age-related macular degeneration and drusen were analyzed. After digitizing the photographs, the underlying background pattern in the green channel was leveled by an algorithm based on the elliptically concentric geometry of the reflectance in the normal macula: the gray scale values of all structures within defined elliptical boundaries were raised sequentially until a uniform background was obtained. Segmentation of drusen and area measurements in the central and middle subfields (1000 μm and 3000 μm diameters) were performed by uniform thresholds. Two observers using this interactive semi-automated software measured each image digitally. The mean digital measurements were compared to independent stereo fundus gradings by two expert graders (stereo Grader 1 estimated the drusen percentage in each of the 24 regions as falling into one of four standard broad ranges; stereo Grader 2 estimated drusen percentages in 1% to 5% intervals). RESULTS: The mean digital area measurements had a median standard deviation of 1.9%. The mean digital area measurements agreed with stereo Grader 1 in 22/24 cases. The 95% limits of agreement between the mean digital area measurements and the more precise stereo gradings of Grader 2 were -6.4 % to +6.8 % in the central subfield and -6.0 % to +4.5 % in the middle subfield. The mean absolute differences between the digital and stereo gradings 2 were 2.8 +/- 3.4% in the central subfield and 2.2 +/- 2.7% in the middle subfield. CONCLUSIONS: Semi-automated, supervised drusen measurements may be done reproducibly and accurately with adaptations of commercial software. This technique for macular image analysis has potential for use in clinical research

DepthCut: Improved Depth Edge Estimation Using Multiple Unreliable Channels

In the context of scene understanding, a variety of methods exists to estimate different information channels from mono or stereo images, including disparity, depth, and normals. Although several advances have been reported in the recent years for these tasks, the estimated information is often imprecise particularly near depth discontinuities or creases. Studies have however shown that precisely such depth edges carry critical cues for the perception of shape, and play important roles in tasks like depth-based segmentation or foreground selection. Unfortunately, the currently extracted channels often carry conflicting signals, making it difficult for subsequent applications to effectively use them. In this paper, we focus on the problem of obtaining high-precision depth edges (i.e., depth contours and creases) by jointly analyzing such unreliable information channels. We propose DepthCut, a data-driven fusion of the channels using a convolutional neural network trained on a large dataset with known depth. The resulting depth edges can be used for segmentation, decomposing a scene into depth layers with relatively flat depth, or improving the accuracy of the depth estimate near depth edges by constraining its gradients to agree with these edges. Quantitatively, we compare against 15 variants of baselines and demonstrate that our depth edges result in an improved segmentation performance and an improved depth estimate near depth edges compared to data-agnostic channel fusion. Qualitatively, we demonstrate that the depth edges result in superior segmentation and depth orderings.Comment: 12 page

Fast Multi-frame Stereo Scene Flow with Motion Segmentation

We propose a new multi-frame method for efficiently computing scene flow (dense depth and optical flow) and camera ego-motion for a dynamic scene observed from a moving stereo camera rig. Our technique also segments out moving objects from the rigid scene. In our method, we first estimate the disparity map and the 6-DOF camera motion using stereo matching and visual odometry. We then identify regions inconsistent with the estimated camera motion and compute per-pixel optical flow only at these regions. This flow proposal is fused with the camera motion-based flow proposal using fusion moves to obtain the final optical flow and motion segmentation. This unified framework benefits all four tasks - stereo, optical flow, visual odometry and motion segmentation leading to overall higher accuracy and efficiency. Our method is currently ranked third on the KITTI 2015 scene flow benchmark. Furthermore, our CPU implementation runs in 2-3 seconds per frame which is 1-3 orders of magnitude faster than the top six methods. We also report a thorough evaluation on challenging Sintel sequences with fast camera and object motion, where our method consistently outperforms OSF [Menze and Geiger, 2015], which is currently ranked second on the KITTI benchmark.Comment: 15 pages. To appear at IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2017). Our results were submitted to KITTI 2015 Stereo Scene Flow Benchmark in November 201

Visualization and Correction of Automated Segmentation, Tracking and Lineaging from 5-D Stem Cell Image Sequences

Results: We present an application that enables the quantitative analysis of multichannel 5-D (x, y, z, t, channel) and large montage confocal fluorescence microscopy images. The image sequences show stem cells together with blood vessels, enabling quantification of the dynamic behaviors of stem cells in relation to their vascular niche, with applications in developmental and cancer biology. Our application automatically segments, tracks, and lineages the image sequence data and then allows the user to view and edit the results of automated algorithms in a stereoscopic 3-D window while simultaneously viewing the stem cell lineage tree in a 2-D window. Using the GPU to store and render the image sequence data enables a hybrid computational approach. An inference-based approach utilizing user-provided edits to automatically correct related mistakes executes interactively on the system CPU while the GPU handles 3-D visualization tasks. Conclusions: By exploiting commodity computer gaming hardware, we have developed an application that can be run in the laboratory to facilitate rapid iteration through biological experiments. There is a pressing need for visualization and analysis tools for 5-D live cell image data. We combine accurate unsupervised processes with an intuitive visualization of the results. Our validation interface allows for each data set to be corrected to 100% accuracy, ensuring that downstream data analysis is accurate and verifiable. Our tool is the first to combine all of these aspects, leveraging the synergies obtained by utilizing validation information from stereo visualization to improve the low level image processing tasks.Comment: BioVis 2014 conferenc

Enabling Depth-driven Visual Attention on the iCub Humanoid Robot: Instructions for Use and New Perspectives

The importance of depth perception in the interactions that humans have within their nearby space is a well established fact. Consequently, it is also well known that the possibility of exploiting good stereo information would ease and, in many cases, enable, a large variety of attentional and interactive behaviors on humanoid robotic platforms. However, the difficulty of computing real-time and robust binocular disparity maps from moving stereo cameras often prevents from relying on this kind of cue to visually guide robots' attention and actions in real-world scenarios. The contribution of this paper is two-fold: first, we show that the Efficient Large-scale Stereo Matching algorithm (ELAS) by A. Geiger et al. 2010 for computation of the disparity map is well suited to be used on a humanoid robotic platform as the iCub robot; second, we show how, provided with a fast and reliable stereo system, implementing relatively challenging visual behaviors in natural settings can require much less effort. As a case of study we consider the common situation where the robot is asked to focus the attention on one object close in the scene, showing how a simple but effective disparity-based segmentation solves the problem in this case. Indeed this example paves the way to a variety of other similar applications