Holistic Segmentation of Intermuscular Adipose Tissues on Thigh MRI View Full Text


Ontology type: schema:Chapter     


Chapter Info

DATE

2017

AUTHORS

Jianhua Yao , William Kovacs , Nathan Hsieh , Chia-Ying Liu , Ronald M. Summers

ABSTRACT

Muscular dystrophies (MD) cause muscles to gradually degenerate into fat. In order to effectively study and track disease progression, it is important to quantify both muscle and fat volumes, especially the intermuscular adipose tissue (IMAT). Existing methods were mostly based on unsupervised pixel clustering and morphological models. We propose a method integrating two holistic neural networks (one for edges and one for regions) and a dual active contour model to accurately locate the fascia lata and segment multiple tissue types on thigh MRIs. The proposed method is robust to image artifacts and weak boundaries, and thus it performs well for severe MD cases. Our method was tested on 104 data sets and achieved Dice coefficients 0.940 and 0.943 for muscle and IMAT in challenging severe cases, respectively. More... »

PAGES

737-745

Book

TITLE

Medical Image Computing and Computer Assisted Intervention − MICCAI 2017

ISBN

978-3-319-66181-0
978-3-319-66182-7

Author Affiliations

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/978-3-319-66182-7_84

DOI

http://dx.doi.org/10.1007/978-3-319-66182-7_84

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1091427459


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