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

Segmentation of key brain tissues from 3D medical images is of great significance for brain
disease diagnosis, progression assessment and monitoring of neurologic conditions. While …

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 …

Deep learning technologies have dramatically reshaped the field of medical image
registration over the past decade. The initial developments, such as regression-based and U …

Thanks to its safety and accessibility, magnetic resonance imaging (MRI) is extensively used
in clinical routine and research field, largely contributing to our understanding of the …

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 …

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 …

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 …

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 …

We introduce the Mindboggle-101 dataset, the largest and most complete set of free,
publicly accessible, manually labeled human brain images. To manually label the …