Deep iterative vessel segmentation in OCT angiography.

This paper addresses retinal vessel segmentation on (OCT-A) images of the human retina. Our approach is motivated by the need for high precision image-guided delivery of regenerative therapies in vitreo-retinal surgery. OCT-A visualizes macular vasculature, the main landmark of the surgically targeted area, at a level of detail and spatial extent unattainable by other imaging modalities.

Learned optical flow for intra-operative tracking of the retinal fundus.

Purpose: Sustained delivery of regenerative retinal therapies by robotic systems requires intra-operative tracking of the retinal fundus. We propose a supervised deep convolutional neural network to densely predict semantic segmentation and optical flow of the retina as mutually supportive tasks, implicitly inpainting retinal flow information missing due to occlusion by surgical tools.

Methods: As manual annotation of optical flow is infeasible, we propose a flexible algorithm for generation of large synthetic training datasets on the basis of given intra-operative retinal images.

Information Accrual From the Period Preceding Racket-Ball Contact for Tennis Ground Strokes: Inferences From Stochastic Masking.

Previous research suggests the existence of an expert anticipatory advantage, whereby skilled sportspeople are able to predict an upcoming action by utilizing cues contained in their opponent's body kinematics. This ability is often inferred from "occlusion" experiments: information is systematically removed from first-person videos of an opponent, for example, by stopping a tennis video at the point of racket-ball contact, yet performance, such as discrimination of shot direction, remains above chance. In this study, we assessed the expert anticipatory advantage for tennis ground strokes via a modified approach, known as "bubbles," in which information is randomly removed from videos in each trial.

Classification Videos Reveal the Visual Information Driving Complex Real-World Speeded Decisions.

Humans can rapidly discriminate complex scenarios as they unfold in real time, for example during law enforcement or, more prosaically, driving and sport. Such decision-making improves with experience, as new sources of information are exploited. For example, sports experts are able to predict the outcome of their opponent's next action (e.

Automated mitosis detection using texture, SIFT features and HMAX biologically inspired approach.

Context: According to Nottingham grading system, mitosis count in breast cancer histopathology is one of three components required for cancer grading and prognosis. Manual counting of mitosis is tedious and subject to considerable inter- and intra-reader variations.

Aims: The aim is to investigate the various texture features and Hierarchical Model and X (HMAX) biologically inspired approach for mitosis detection using machine-learning techniques.