Functional localization is a concept which involves the application of a sequence of
geometrical and statistical image processing operations in order to define the location of …

Automated segmentation in medical image analysis is a challenging task that requires a
large amount of manually labeled data. However, most existing learning-based approaches …

Abstract Accurate segmentation of 2-D, 3-D, and 4-D medical images to isolate anatomical
objects of interest for analysis is essential in almost any computer-aided diagnosis system or …

The notion of using context information for solving high-level vision and medical image
segmentation problems has been increasingly realized in the field. However, how to learn …

We propose a nonparametric, probabilistic model for the automatic segmentation of medical
images, given a training set of images and corresponding label maps. The resulting …

This paper addresses the automated segmentation of multiple organs in upper abdominal
computed tomography (CT) data. The aim of our study is to develop methods to effectively …

We are developing a computer‐aided diagnosis (CAD) system to classify malignant and
benign lung nodules found on CT scans. A fully automated system was designed to segment …

In this paper, a hybrid discriminative/generative model for brain anatomical structure
segmentation is proposed. The learning aspect of the approach is emphasized. In the …

The medical image analysis field has traditionally been focused on the development of
organ-, and disease-specific methods. Recently, the interest in the development of more …

Automatic segmentation of the hippocampus from 3D magnetic resonance imaging mostly
relied on multi-atlas registration methods. In this work, we exploit recent advances in deep …