Fast automated segmentation of multiple objects via spatially weighted shape learning

Chandra, Shekhar S., Dowling, Jason A., Greer, Peter B., Martin, Jarad, Wratten, Chris, Pichler, Peter, Fripp, Jurgen and Crozier, Stuart (2016) Fast automated segmentation of multiple objects via spatially weighted shape learning. Physics in Medicine and Biology, 61 22: 8070-8084. doi:10.1088/0031-9155/61/22/8070

Author Chandra, Shekhar S.
Dowling, Jason A.
Greer, Peter B.
Martin, Jarad
Wratten, Chris
Pichler, Peter
Fripp, Jurgen
Crozier, Stuart
Title Fast automated segmentation of multiple objects via spatially weighted shape learning
Journal name Physics in Medicine and Biology   Check publisher's open access policy
ISSN 0031-9155
Publication date 2016-10-25
Sub-type Article (original research)
DOI 10.1088/0031-9155/61/22/8070
Open Access Status Not yet assessed
Volume 61
Issue 22
Start page 8070
End page 8084
Total pages 15
Place of publication Bristol, United Kingdom
Publisher Institute of Physics Publishing
Language eng
Subject 3614 Radiological and Ultrasound Technology
2741 Radiology Nuclear Medicine and imaging
Abstract Active shape models (ASMs) have proved successful in automatic segmentation by using shape and appearance priors in a number of areas such as prostate segmentation, where accurate contouring is important in treatment planning for prostate cancer. The ASM approach however, is heavily reliant on a good initialisation for achieving high segmentation quality. This initialisation often requires algorithms with high computational complexity, such as three dimensional (3D) image registration. In this work, we present a fast, self-initialised ASM approach that simultaneously fits multiple objects hierarchically controlled by spatially weighted shape learning. Prominent objects are targeted initially and spatial weights are progressively adjusted so that the next (more difficult, less visible) object is simultaneously initialised using a series of weighted shape models. The scheme was validated and compared to a multi-atlas approach on 3D magnetic resonance (MR) images of 38 cancer patients and had the same (mean, median, inter-rater) Dice's similarity coefficients of (0.79, 0.81, 0.85), while having no registration error and a computational time of 12–15 min, nearly an order of magnitude faster than the multi-atlas approach.
Keyword Segmentation
Active shape models
Q-Index Code C1
Q-Index Status Provisional Code
Institutional Status UQ

Document type: Journal Article
Sub-type: Article (original research)
Collections: HERDC Pre-Audit
School of Information Technology and Electrical Engineering Publications
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Created: Wed, 26 Oct 2016, 19:08:10 EST by Shekhar Chandra on behalf of School of Information Technol and Elec Engineering