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Weighted atlas auto-context with application to multiple organ segmentation

Weighted atlas auto-context with application to multiple organ segmentation

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Original languageEnglish
Title of host publication2016 IEEE Winter Conference on Applications of Computer Vision (WACV)
PublisherIEEE
Number of pages9
ISBN (Electronic)9781509006410
ISBN (Print)9781509006403
DOIs
StatePublished - 26 May 2016
EventIEEE Winter Conference on Applications of Computer Vision - Lake Placid, NY, United States

Conference

ConferenceIEEE Winter Conference on Applications of Computer Vision
Abbreviated titleWACV 2016
CountryUnited States
CityLake Placid, NY
Period7/03/169/03/16
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Abstract

Difficulties can arise from the segmentation of three-dimensional objects formed by multiple non-rigid parts rep-resented in two-dimensional images. Problems involving parts whose spatial arrangement is subject to weak restrictions, and whose appearance and form change across images, can be particularly challenging. Segmentation methods that take into account spatial context information have addressed these types of problem, which often involve image data of a multi-modal nature. An attractive feature of the auto-context (AC) technique is that a prior “atlas”, typically obtained by averaging multiple label maps created by experts, can be used as an initial source of contextual data. However, a prior obtained in this way is likely to hide the inherent multi-modality of the data. We propose a modification of AC in which a probabilistic atlas of part locations is iteratively improved and made available as an additional source of information. We illustrate this technique with the problem of segmenting individual organs in images of pig offal, reporting statistically significant improvements in relation to both conventional AC and a state-of-the-art technique based on conditional random fields.

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