Deep learning has revolutionized image processing and achieved the-state-of-art
performance in many medical image segmentation tasks. Many deep learning-based …

Manual image segmentation is a time-consuming task routinely performed in radiotherapy to
identify each patient's targets and anatomical structures. The efficacy and safety of the …

In radiotherapy for cancer patients, an indispensable process is to delineate organs-at-risk
(OARs) and tumors. However, it is the most time-consuming step as manual delineation is …

Purpose Accurate and timely organs‐at‐risk (OARs) segmentation is key to efficient and
high‐quality radiation therapy planning. The purpose of this work is to develop a deep …

Detailed whole brain segmentation is an essential quantitative technique in medical image
analysis, which provides a non-invasive way of measuring brain regions from a clinical …

Robust and fast detection of anatomical structures is a prerequisite for both diagnostic and
interventional medical image analysis. Current solutions for anatomy detection are typically …

A whole heart segmentation (WHS) method is presented for cardiac MRI. This segmentation
method employs multi-modality atlases from MRI and CT and adopts a new label fusion …

Abstract Multi-atlas segmentation (MAS), first introduced and popularized by the pioneering
work of Rohlfing, et al.(2004), Klein, et al.(2005), and Heckemann, et al.(2006), is becoming …

This paper presents a review of automated image registration methodologies that have been
used in the medical field. The aim of this paper is to be an introduction to the field, provide …

Multi-atlas segmentation is an effective approach for automatically labeling objects of
interest in biomedical images. In this approach, multiple expert-segmented example images …