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Shape-aware multi-atlas segmentation

Alvén, Jennifer; Kahl, Fredrik LU ; Landgren, Matilda LU ; Larsson, Viktor LU and Ulén, Johannes LU (2017) 2016 23rd International Conference on Pattern Recognition (ICPR 2016) In 2016 23rd International Conference on Pattern Recognition, ICPR 2016 p.1101-1106
Abstract

Despite of having no explicit shape model, multi-atlas approaches to image segmentation have proved to be a top-performer for several diverse datasets and imaging modalities. In this paper, we show how one can directly incorporate shape regularization into the multi-atlas framework. Unlike traditional methods, our proposed approach does not rely on label fusion on the voxel level. Instead, each registered atlas is viewed as an estimate of the position of a shape model. We evaluate and compare our method on two public benchmarks: (i) the VISCERAL Grand Challenge on multi-organ segmentation of whole-body CT images and (ii) the Hammers brain atlas of MR images for segmenting the hippocampus and the amygdala. For this wide spectrum of both... (More)

Despite of having no explicit shape model, multi-atlas approaches to image segmentation have proved to be a top-performer for several diverse datasets and imaging modalities. In this paper, we show how one can directly incorporate shape regularization into the multi-atlas framework. Unlike traditional methods, our proposed approach does not rely on label fusion on the voxel level. Instead, each registered atlas is viewed as an estimate of the position of a shape model. We evaluate and compare our method on two public benchmarks: (i) the VISCERAL Grand Challenge on multi-organ segmentation of whole-body CT images and (ii) the Hammers brain atlas of MR images for segmenting the hippocampus and the amygdala. For this wide spectrum of both easy and hard segmentation tasks, our experimental quantitative results are on par or better than state-of-the-art. More importantly, we obtain qualitatively better segmentation boundaries, for instance, preserving fine structures.

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author
organization
publishing date
type
Chapter in Book/Report/Conference proceeding
publication status
published
subject
in
2016 23rd International Conference on Pattern Recognition, ICPR 2016
pages
6 pages
publisher
Institute of Electrical and Electronics Engineers Inc.
conference name
2016 23rd International Conference on Pattern Recognition (ICPR 2016)
external identifiers
  • scopus:85019157404
DOI
10.1109/ICPR.2016.7899783
language
English
LU publication?
yes
id
c7553c58-7777-49d9-a51e-504c1302cf12
date added to LUP
2017-06-02 13:53:40
date last changed
2017-06-02 13:53:40
@inproceedings{c7553c58-7777-49d9-a51e-504c1302cf12,
  abstract     = {<p>Despite of having no explicit shape model, multi-atlas approaches to image segmentation have proved to be a top-performer for several diverse datasets and imaging modalities. In this paper, we show how one can directly incorporate shape regularization into the multi-atlas framework. Unlike traditional methods, our proposed approach does not rely on label fusion on the voxel level. Instead, each registered atlas is viewed as an estimate of the position of a shape model. We evaluate and compare our method on two public benchmarks: (i) the VISCERAL Grand Challenge on multi-organ segmentation of whole-body CT images and (ii) the Hammers brain atlas of MR images for segmenting the hippocampus and the amygdala. For this wide spectrum of both easy and hard segmentation tasks, our experimental quantitative results are on par or better than state-of-the-art. More importantly, we obtain qualitatively better segmentation boundaries, for instance, preserving fine structures.</p>},
  author       = {Alvén, Jennifer and Kahl, Fredrik and Landgren, Matilda and Larsson, Viktor and Ulén, Johannes},
  booktitle    = {2016 23rd International Conference on Pattern Recognition, ICPR 2016},
  language     = {eng},
  month        = {04},
  pages        = {1101--1106},
  publisher    = {Institute of Electrical and Electronics Engineers Inc.},
  title        = {Shape-aware multi-atlas segmentation},
  url          = {http://dx.doi.org/10.1109/ICPR.2016.7899783},
  year         = {2017},
}