Fast automated segmentation of multiple objects via spatially weighted shape learning

Shekhar S. Chandra, Jason A. Dowling, Peter B. Greer, Jarad Martin, Chris Wratten, Peter Pichler, Jurgen Fripp, Stuart Crozier

Research output: Contribution to journalArticlepeer-review

10 Citations (Scopus)


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.

Original languageEnglish
Pages (from-to)8070-8084
Number of pages15
JournalPhysics in Medicine and Biology
Issue number22
Publication statusPublished - 25 Oct 2016
Externally publishedYes


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