Medical image segmentation using object atlas versus object cloud models

Abstract

Medical image segmentation is crucial for quantitative organ analysis and surgical planning. Since interactive segmentation is not practical in a production-mode clinical setting, automatic methods based on 3D object appearance models have been proposed. Among them, approaches based on object atlas are the most actively investigated. A key drawback of these approaches is that they require a time-costly image registration process to build and deploy the atlas. Object cloud models (OCM) have been introduced to avoid registration, considerably speeding up the whole process, but they have not been compared to object atlas models (OAM). The present paper fills this gap by presenting a comparative analysis of the two approaches in the task of individually segmenting nine anatomical structures of the human body. Our results indicate that OCM achieve a statistically significant better accuracy for seven anatomical structures, in terms of Dice Similarity Coefficient and Average Symmetric Surface Distance.

Medical image segmentation via atlases and fuzzy object models: Improving efficacy through optimum object search and fewer models

“…21, we showed that optimum object search can improve SOSM segmentation with a single model per object and, in Ref. 22, we presented a first comparison between FOSM and SOSM (without optimum object search) using a single model per object. This is the first time we compare FOSM and SOSMs by using multiple models per object as baseline and evaluate the optimum object search approach in depth.…”