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Weighted atlas auto-context with application to multiple organ segmentation

Lookup NU author(s): Dr Telmo Amaral, Professor Ilias Kyriazakis

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This is the authors' accepted manuscript of a conference proceedings (inc. abstract) that has been published in its final definitive form by Institute of Electrical and Electronics Engineers, 2016.

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Abstract

Difficulties can arise from the segmentation of three-dimensional objects formed by multiple non-rigid parts represented 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.


Publication metadata

Author(s): Amaral T, Kyriazakis I, McKenna SJ, Plötz T

Publication type: Conference Proceedings (inc. Abstract)

Publication status: Published

Conference Name: IEEE Winter Conference on Applications of Computer Vision (WACV)

Year of Conference: 2016

Pages: 1-9

Online publication date: 07/03/2016

Acceptance date: 15/01/2016

ISSN: 1550-5790

Publisher: Institute of Electrical and Electronics Engineers

URL: http://dx.doi.org/10.1109/WACV.2016.7477605

DOI: 10.1109/WACV.2016.7477605

Series Title: IEEE Workshop on Applications of Computer Vision. Proceedings


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