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Title: Enhancing atlas based segmentation with multiclass linear classifiers

Abstract

Purpose: To present a method to enrich atlases for atlas based segmentation. Such enriched atlases can then be used as a single atlas or within a multiatlas framework. Methods: In this paper, machine learning techniques have been used to enhance the atlas based segmentation approach. The enhanced atlas defined in this work is a pair composed of a gray level image alongside an image of multiclass classifiers with one classifier per voxel. Each classifier embeds local information from the whole training dataset that allows for the correction of some systematic errors in the segmentation and accounts for the possible local registration errors. The authors also propose to use these images of classifiers within a multiatlas framework: results produced by a set of such local classifier atlases can be combined using a label fusion method. Results: Experiments have been made on the in vivo images of the IBSR dataset and a comparison has been made with several state-of-the-art methods such as FreeSurfer and the multiatlas nonlocal patch based method of Coupé or Rousseau. These experiments show that their method is competitive with state-of-the-art methods while having a low computational cost. Further enhancement has also been obtained with a multiatlas version ofmore » their method. It is also shown that, in this case, nonlocal fusion is unnecessary. The multiatlas fusion can therefore be done efficiently. Conclusions: The single atlas version has similar quality as state-of-the-arts multiatlas methods but with the computational cost of a naive single atlas segmentation. The multiatlas version offers a improvement in quality and can be done efficiently without a nonlocal strategy.« less

Authors:
 [1]
  1. Université de Lyon, CREATIS, CNRS UMR 5220, Inserm U1044, INSA-Lyon, Université Lyon 1, Villeurbanne 69300 (France)
Publication Date:
OSTI Identifier:
22482444
Resource Type:
Journal Article
Journal Name:
Medical Physics
Additional Journal Information:
Journal Volume: 42; Journal Issue: 12; Other Information: (c) 2015 American Association of Physicists in Medicine; Country of input: International Atomic Energy Agency (IAEA); Journal ID: ISSN 0094-2405
Country of Publication:
United States
Language:
English
Subject:
60 APPLIED LIFE SCIENCES; CORRECTIONS; DATASETS; ERRORS; IMAGES; IN VIVO; RADIATION DOSE UNITS; TRAINING

Citation Formats

Sdika, Michaël. Enhancing atlas based segmentation with multiclass linear classifiers. United States: N. p., 2015. Web. doi:10.1118/1.4935946.
Sdika, Michaël. Enhancing atlas based segmentation with multiclass linear classifiers. United States. https://doi.org/10.1118/1.4935946
Sdika, Michaël. 2015. "Enhancing atlas based segmentation with multiclass linear classifiers". United States. https://doi.org/10.1118/1.4935946.
@article{osti_22482444,
title = {Enhancing atlas based segmentation with multiclass linear classifiers},
author = {Sdika, Michaël},
abstractNote = {Purpose: To present a method to enrich atlases for atlas based segmentation. Such enriched atlases can then be used as a single atlas or within a multiatlas framework. Methods: In this paper, machine learning techniques have been used to enhance the atlas based segmentation approach. The enhanced atlas defined in this work is a pair composed of a gray level image alongside an image of multiclass classifiers with one classifier per voxel. Each classifier embeds local information from the whole training dataset that allows for the correction of some systematic errors in the segmentation and accounts for the possible local registration errors. The authors also propose to use these images of classifiers within a multiatlas framework: results produced by a set of such local classifier atlases can be combined using a label fusion method. Results: Experiments have been made on the in vivo images of the IBSR dataset and a comparison has been made with several state-of-the-art methods such as FreeSurfer and the multiatlas nonlocal patch based method of Coupé or Rousseau. These experiments show that their method is competitive with state-of-the-art methods while having a low computational cost. Further enhancement has also been obtained with a multiatlas version of their method. It is also shown that, in this case, nonlocal fusion is unnecessary. The multiatlas fusion can therefore be done efficiently. Conclusions: The single atlas version has similar quality as state-of-the-arts multiatlas methods but with the computational cost of a naive single atlas segmentation. The multiatlas version offers a improvement in quality and can be done efficiently without a nonlocal strategy.},
doi = {10.1118/1.4935946},
url = {https://www.osti.gov/biblio/22482444}, journal = {Medical Physics},
issn = {0094-2405},
number = 12,
volume = 42,
place = {United States},
year = {Tue Dec 15 00:00:00 EST 2015},
month = {Tue Dec 15 00:00:00 EST 2015}
}