Comparison and evaluation of segmentation techniques for subcortical structures in brain MRI

Kolawole O. Babalola; Brian Patenaude; Paul Aljabar; Julia Schnabel; David Kennedy; William Crum; Stephen Smith; Tim F. Cootes; Mark Jenkinson; Daniel Rueckert

doi:10.1007/978-3-540-85988-8_49

Comparison and evaluation of segmentation techniques for subcortical structures in brain MRI

Kolawole O. Babalola, Brian Patenaude, Paul Aljabar, Julia Schnabel, David Kennedy, William Crum, Stephen Smith, Tim F. Cootes, Mark Jenkinson, Daniel Rueckert

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

67 Citations (Scopus)

Abstract

The automation of segmentation of medical images is an active research area. However, there has been criticism of the standard of evaluation of methods. We have comprehensively evaluated four novel methods of automatically segmenting subcortical structures using volumetric, spatial overlap and distance-based measures. Two of the methods are atlas-based - classifier fusion and labelling (CFL) and expectation-maximisation segmentation using a dynamic brain atlas (EMS), and two model-based - profile active appearance models (PAM) and Bayesian appearance models (BAM). Each method was applied to the segmentation of 18 subcortical structures in 270 subjects from a diverse pool varying in age, disease, sex and image acquisition parameters. Our results showed that all four methods perform on par with recently published methods. CFL performed significantly better than the other three methods according to all three classes of metrics.

Original language	English
Title of host publication	Medical Image Computing and Computer-Assisted Intervention - MICCAI 2008 - 11th International Conference, Proceedings
Pages	409-416
Number of pages	8
Edition	PART 1
DOIs	https://doi.org/10.1007/978-3-540-85988-8_49
Publication status	Published or Issued - 2008
Externally published	Yes
Event	11th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2008 - New York, NY, United States Duration: 6 Sept 2008 → 10 Sept 2008

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Number	PART 1
Volume	5241 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Other

Other	11th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2008
Country/Territory	United States
City	New York, NY
Period	6/09/08 → 10/09/08

ASJC Scopus subject areas

Theoretical Computer Science
General Computer Science

Access to Document

10.1007/978-3-540-85988-8_49

Cite this

Babalola, K. O., Patenaude, B., Aljabar, P., Schnabel, J., Kennedy, D., Crum, W., Smith, S., Cootes, T. F., Jenkinson, M., & Rueckert, D. (2008). Comparison and evaluation of segmentation techniques for subcortical structures in brain MRI. In Medical Image Computing and Computer-Assisted Intervention - MICCAI 2008 - 11th International Conference, Proceedings (PART 1 ed., pp. 409-416). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 5241 LNCS, No. PART 1). https://doi.org/10.1007/978-3-540-85988-8_49

Babalola, Kolawole O. ; Patenaude, Brian ; Aljabar, Paul et al. / Comparison and evaluation of segmentation techniques for subcortical structures in brain MRI. Medical Image Computing and Computer-Assisted Intervention - MICCAI 2008 - 11th International Conference, Proceedings. PART 1. ed. 2008. pp. 409-416 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); PART 1).

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title = "Comparison and evaluation of segmentation techniques for subcortical structures in brain MRI",

abstract = "The automation of segmentation of medical images is an active research area. However, there has been criticism of the standard of evaluation of methods. We have comprehensively evaluated four novel methods of automatically segmenting subcortical structures using volumetric, spatial overlap and distance-based measures. Two of the methods are atlas-based - classifier fusion and labelling (CFL) and expectation-maximisation segmentation using a dynamic brain atlas (EMS), and two model-based - profile active appearance models (PAM) and Bayesian appearance models (BAM). Each method was applied to the segmentation of 18 subcortical structures in 270 subjects from a diverse pool varying in age, disease, sex and image acquisition parameters. Our results showed that all four methods perform on par with recently published methods. CFL performed significantly better than the other three methods according to all three classes of metrics.",

author = "Babalola, {Kolawole O.} and Brian Patenaude and Paul Aljabar and Julia Schnabel and David Kennedy and William Crum and Stephen Smith and Cootes, {Tim F.} and Mark Jenkinson and Daniel Rueckert",

note = "Copyright: Copyright 2009 Elsevier B.V., All rights reserved.; 11th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2008 ; Conference date: 06-09-2008 Through 10-09-2008",

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doi = "10.1007/978-3-540-85988-8_49",

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Babalola, KO, Patenaude, B, Aljabar, P, Schnabel, J, Kennedy, D, Crum, W, Smith, S, Cootes, TF, Jenkinson, M & Rueckert, D 2008, Comparison and evaluation of segmentation techniques for subcortical structures in brain MRI. in Medical Image Computing and Computer-Assisted Intervention - MICCAI 2008 - 11th International Conference, Proceedings. PART 1 edn, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), no. PART 1, vol. 5241 LNCS, pp. 409-416, 11th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2008, New York, NY, United States, 6/09/08. https://doi.org/10.1007/978-3-540-85988-8_49

Comparison and evaluation of segmentation techniques for subcortical structures in brain MRI. / Babalola, Kolawole O.; Patenaude, Brian; Aljabar, Paul et al.
Medical Image Computing and Computer-Assisted Intervention - MICCAI 2008 - 11th International Conference, Proceedings. PART 1. ed. 2008. p. 409-416 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 5241 LNCS, No. PART 1).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AU - Kennedy, David

AU - Crum, William

AU - Smith, Stephen

AU - Cootes, Tim F.

AU - Jenkinson, Mark

AU - Rueckert, Daniel

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AB - The automation of segmentation of medical images is an active research area. However, there has been criticism of the standard of evaluation of methods. We have comprehensively evaluated four novel methods of automatically segmenting subcortical structures using volumetric, spatial overlap and distance-based measures. Two of the methods are atlas-based - classifier fusion and labelling (CFL) and expectation-maximisation segmentation using a dynamic brain atlas (EMS), and two model-based - profile active appearance models (PAM) and Bayesian appearance models (BAM). Each method was applied to the segmentation of 18 subcortical structures in 270 subjects from a diverse pool varying in age, disease, sex and image acquisition parameters. Our results showed that all four methods perform on par with recently published methods. CFL performed significantly better than the other three methods according to all three classes of metrics.

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Babalola KO, Patenaude B, Aljabar P, Schnabel J, Kennedy D, Crum W et al. Comparison and evaluation of segmentation techniques for subcortical structures in brain MRI. In Medical Image Computing and Computer-Assisted Intervention - MICCAI 2008 - 11th International Conference, Proceedings. PART 1 ed. 2008. p. 409-416. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); PART 1). doi: 10.1007/978-3-540-85988-8_49

Comparison and evaluation of segmentation techniques for subcortical structures in brain MRI

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