Surgical Planning Laboratory - Brigham & Women's Hospital - Boston, Massachusetts USA - a teaching affiliate of Harvard Medical School

Surgical Planning Laboratory

The Publication Database hosted by SPL

All Publications | Upload | Advanced Search | Gallery View | Download Statistics | Help | Import | Log in

Atlas-Based Under-Segmentation

Institution:
Computer Science and Artificial Intelligence Lab, Massachusetts Institute of Technology, Cambridge, MA, USA.
Publisher:
Int Conf Med Image Comput Comput Assist Interv. MICCAI 2014
Publication Date:
Sep-2014
Journal:
Int Conf Med Image Comput Comput Assist Interv.
Volume Number:
17
Issue Number:
Pt 1
Pages:
315-22
Citation:
Int Conf Med Image Comput Comput Assist Interv. 2014 Sep;17(Pt 1):315-22.
PubMed ID:
25333133
PMCID:
PMC4219918
Appears in Collections:
NAC, NA-MIC
Sponsors:
P41 EB015902/EB/NIBIB NIH HHS/United States
U54 EB005149/EB/NIBIB NIH HHS/United States
Generated Citation:
Wachinger C., Golland P. Atlas-Based Under-Segmentation. Int Conf Med Image Comput Comput Assist Interv. 2014 Sep;17(Pt 1):315-22. PMID: 25333133. PMCID: PMC4219918.
Downloaded: 709 times. [view map]
Paper: Download, View online
Export citation:
Google Scholar: link

We study the widespread, but rarely discussed, tendency of atlas-based segmentation to under-segment the organs of interest. Commonly used error measures do not distinguish between under- and over-segmentation, contributing to the problem. We explicitly quantify over- and under-segmentation in several typical examples and present a new hypothesis for the cause. We provide evidence that segmenting only one organ of interest and merging all surrounding structures into one label creates bias towards background in the label estimates suggested by the atlas. We propose a generative model that corrects for this effect by learning the background structures from the data. Inference in the model separates the background into distinct structures and consequently improves the segmentation accuracy. Our experiments demonstrate a clear improvement in several applications.

Additional Material
1 File (54.713kB)
Wachinger-MICCAI2014-2-fig2.jpg (54.713kB)