Author: Tzachi Hershkovich

Publisher:

Published: 2017

Total Pages: 0

ISBN-13:

Fully-automated segmentation algorithms offer fast, objective, and reproducible results for large data collections. However these techniques cannot handle tasks that require contextual knowledge not readily available in the images alone. Thus, the supervision of an expert is necessary. We present a generative model for image segmentation, based on a bayesian inference. Not only does our approach support an intuitive and convenient user interaction subject to the bottom-up constraints introduced by the image intensities, it also facilitates the main limitations of a human observer - 3D visualization and modality fusion. The user "dialogue" with the segmentation algorithm, via several mouse clicks in regions of disagreement, is formulated as an additional, spatial term in a global cost functional for 3D segmentation. The method is exemplified for the segmentation of regions of interest (ROIs) in three different datasets: cerebral hemorrhages (CH) in human brain CT scans; ventricles in degenerative mice brain MRI and tumors in multi-modal human brain MRI. In significant amount of study cases, user guidance might not be sufficient to solve image segmentation ambiguities. Hence, estimation of the uncertainty margins of the extracted anatomical structure or pathology boundaries should be considered. In this part of my thesis I study the concept of segmentation uncertainty of clinical images, acknowledging its great importance to patients follow up, user-interaction guidance and morphology-based population studies. We propose a novel approach for model-dependent uncertainty estimation for image segmentation. The key-contribution is an alternating, iterative algorithm for the generation of an image-specific uncertainty map. This is accomplished by defining a consistency-based measure and applying it to segmentation samples to estimate the uncertainty margins as well as the midline segmentation. We utilize the stochastic active contour framework as our segmentation generator, yet any sampling method can be applied. The method is validated on synthetic data for well-defined objects blurred with known Gaussian kernels. Qualitative and quantitative proofs of concept are further provided by an application of the proposed consistency-based algorithm to ensembles of stochastic segmentations of brain hemorrhage in CT scans. -- abstract.