Lumbar disc localization and labeling with a probabilistic model on both pixel and object features

Jason J. Corso; Raja S. Alomari; Vipin Chaudhary

doi:10.1007/978-3-540-85988-8_25

Lumbar disc localization and labeling with a probabilistic model on both pixel and object features

Jason J. Corso, Raja S. Alomari, Vipin Chaudhary

Department of Computer Science

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

84 Scopus citations

Abstract

Repeatable, quantitative assessment of intervertebral disc pathology requires accurate localization and labeling of the lumbar region discs. To that end, we propose a two-level probabilistic model for such disc localization and labeling. Our model integrates both pixel-level information, such as appearance, and object-level information, such as relative location. Utilizing both levels of information adds robustness to the ambiguous disc intensity signature and high structure variation. Yet, we are able to do efficient (and convergent) localization and labeling with generalized expectation-maximization. We present accurate results on 20 normal cases (96%) and a promising extension to a pathology case.

Original language	English
Title of host publication	Medical Image Computing and Computer-Assisted Intervention - MICCAI 2008 - 11th International Conference, Proceedings
Pages	202-210
Number of pages	9
Edition	PART 1
DOIs	https://doi.org/10.1007/978-3-540-85988-8_25
State	Published - 2008
Event	11th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2008 - New York, NY, United States Duration: 6 Sep 2008 → 10 Sep 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

Conference

Conference	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

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10.1007/978-3-540-85988-8_25

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Corso, J. J., Alomari, R. S., & Chaudhary, V. (2008). Lumbar disc localization and labeling with a probabilistic model on both pixel and object features. In Medical Image Computing and Computer-Assisted Intervention - MICCAI 2008 - 11th International Conference, Proceedings (PART 1 ed., pp. 202-210). (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_25

Corso, Jason J. ; Alomari, Raja S. ; Chaudhary, Vipin. / Lumbar disc localization and labeling with a probabilistic model on both pixel and object features. Medical Image Computing and Computer-Assisted Intervention - MICCAI 2008 - 11th International Conference, Proceedings. PART 1. ed. 2008. pp. 202-210 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); PART 1).

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abstract = "Repeatable, quantitative assessment of intervertebral disc pathology requires accurate localization and labeling of the lumbar region discs. To that end, we propose a two-level probabilistic model for such disc localization and labeling. Our model integrates both pixel-level information, such as appearance, and object-level information, such as relative location. Utilizing both levels of information adds robustness to the ambiguous disc intensity signature and high structure variation. Yet, we are able to do efficient (and convergent) localization and labeling with generalized expectation-maximization. We present accurate results on 20 normal cases (96%) and a promising extension to a pathology case.",

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Corso, JJ, Alomari, RS & Chaudhary, V 2008, Lumbar disc localization and labeling with a probabilistic model on both pixel and object features. 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. 202-210, 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_25

Lumbar disc localization and labeling with a probabilistic model on both pixel and object features. / Corso, Jason J.; Alomari, Raja S.; Chaudhary, Vipin.
Medical Image Computing and Computer-Assisted Intervention - MICCAI 2008 - 11th International Conference, Proceedings. PART 1. ed. 2008. p. 202-210 (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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AB - Repeatable, quantitative assessment of intervertebral disc pathology requires accurate localization and labeling of the lumbar region discs. To that end, we propose a two-level probabilistic model for such disc localization and labeling. Our model integrates both pixel-level information, such as appearance, and object-level information, such as relative location. Utilizing both levels of information adds robustness to the ambiguous disc intensity signature and high structure variation. Yet, we are able to do efficient (and convergent) localization and labeling with generalized expectation-maximization. We present accurate results on 20 normal cases (96%) and a promising extension to a pathology case.

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Corso JJ, Alomari RS, Chaudhary V. Lumbar disc localization and labeling with a probabilistic model on both pixel and object features. In Medical Image Computing and Computer-Assisted Intervention - MICCAI 2008 - 11th International Conference, Proceedings. PART 1 ed. 2008. p. 202-210. (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_25

Lumbar disc localization and labeling with a probabilistic model on both pixel and object features

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