\u3cem\u3eGRASP News\u3c/em\u3e, Volume 8, Number 1

A report of the General Robotics and Active Sensory Perception (GRASP) Laboratory. Edited by Thomas Lindsay

Prediction of treatment response from retinal OCT in patients with exudative age-related macular degeneration

Age related macular degeneration is a major cause of blindness and visual impairment in older adults. Its exudative form, where fluids leak into the macula, is especially damaging. The standard treatment involves injections of anti-VEGF (vascular endothelial growth factor) agents into the eye, which prevent further vascular growth and leakage, and can restore vision. These intravitreal injections have a risk of devastating complications including blindness from infection and are expensive. Optimizing the interval between injections in a patient specific manner is of great interest, as the retinal response is partially patient specific. In this paper we propose a machine learning approach to predict the retinal response at the end of a standardized 12-week induction phase of the treatment. From a longitudinal series of optical coherence tomography (OCT) images, a number of quantitative measurements are extracted, describing the underlying retinal structure and pathology and its response to initial treatment. After initial feature selection, the selected set of features is used to predict the treatment response status at the end of the induction phase using the support vector machine classifier. On a population of 30 patients, leave-one-out cross-validation showed the classification success rate of 87% of predicting whether the subject will show a response to the treatment at the next visit. The proposed methodology is a promising step towards the much needed image-guided prediction of patient-specific treatment response

Advances in Graph-Cut Optimization: Multi-Surface Models, Label Costs, and Hierarchical Costs

Computer vision is full of problems that are elegantly expressed in terms of mathematical optimization, or energy minimization. This is particularly true of low-level inference problems such as cleaning up noisy signals, clustering and classifying data, or estimating 3D points from images. Energies let us state each problem as a clear, precise objective function. Minimizing the correct energy would, hypothetically, yield a good solution to the corresponding problem. Unfortunately, even for low-level problems we are confronted by energies that are computationally hard—often NP-hard—to minimize. As a consequence, a rather large portion of computer vision research is dedicated to proposing better energies and better algorithms for energies. This dissertation presents work along the same line, specifically new energies and algorithms based on graph cuts. We present three distinct contributions. First we consider biomedical segmentation where the object of interest comprises multiple distinct regions of uncertain shape (e.g. blood vessels, airways, bone tissue). We show that this common yet difficult scenario can be modeled as an energy over multiple interacting surfaces, and can be globally optimized by a single graph cut. Second, we introduce multi-label energies with label costs and provide algorithms to minimize them. We show how label costs are useful for clustering and robust estimation problems in vision. Third, we characterize a class of energies with hierarchical costs and propose a novel hierarchical fusion algorithm with improved approximation guarantees. Hierarchical costs are natural for modeling an array of difficult problems, e.g. segmentation with hierarchical context, simultaneous estimation of motions and homographies, or detecting hierarchies of patterns