Image data is of immense practical importance in medical informatics. Automated image
segmentation, which aims at automated extraction of region boundary features, plays a …

Bottom-up segmentation based only on low-level cues is a notoriously difficult problem. This
difficulty has lead to recent top-down segmentation algorithms that are based on class …

We present an image segmentation method that transfers label maps of entire organs from
the training images to the novel image to be segmented. The transfer is based on sparse …

This paper presents an effective and general data augmentation framework for medical
image segmentation. We adopt a computationally efficient and data-efficient gradient-based …

Label fusion is a critical step in many image segmentation frameworks (eg, multi-atlas
segmentation) as it provides a mechanism for generalizing a collection of labeled examples …

We propose a novel framework for rapid and accurate segmentation of a cohort of organs.
First, it integrates local and global image context through a product rule to simultaneously …

In this paper, we introduce a new fusion model whose objective is to fuse multiple region-
based segmentation maps to get a final better segmentation result. The suggested new …

Medical image segmentation is a critical component in clinical practice, facilitating accurate
diagnosis, treatment planning, and disease monitoring. However, existing methods, often …

Medical image segmentation is often a prerequisite for clinical applications. As an ill-posed
problem, it leads to uncertain estimations of the region of interest which may have a …

Anatomical segmentation of structures of interest is critical to quantitative analysis in medical
imaging. Several automated multi-atlas based segmentation propagation methods that …