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Proceedings Paper

Lung lobe segmentation based on statistical atlas and graph cuts
Author(s): Yukitaka Nimura; Takayuki Kitasaka; Hirotoshi Honma; Hirotsugu Takabatake; Masaki Mori; Hiroshi Natori; Kensaku Mori
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Paper Abstract

This paper presents a novel method that can extract lung lobes by utilizing probability atlas and multilabel graph cuts. Information about pulmonary structures plays very important role for decision of the treatment strategy and surgical planning. The human lungs are divided into five anatomical regions, the lung lobes. Precise segmentation and recognition of lung lobes are indispensable tasks in computer aided diagnosis systems and computer aided surgery systems. A lot of methods for lung lobe segmentation are proposed. However, these methods only target the normal cases. Therefore, these methods cannot extract the lung lobes in abnormal cases, such as COPD cases. To extract lung lobes in abnormal cases, this paper propose a lung lobe segmentation method based on probability atlas of lobe location and multilabel graph cuts. The process consists of three components; normalization based on the patient's physique, probability atlas generation, and segmentation based on graph cuts. We apply this method to six cases of chest CT images including COPD cases. Jaccard index was 79.1%.

Paper Details

Date Published: 23 February 2012
PDF: 6 pages
Proc. SPIE 8315, Medical Imaging 2012: Computer-Aided Diagnosis, 83153B (23 February 2012); doi: 10.1117/12.911254
Show Author Affiliations
Yukitaka Nimura, Nagoya Univ. (Japan)
Takayuki Kitasaka, Aichi Institute of Technology (Japan)
Hirotoshi Honma, Sapporo Medical Univ. (Japan)
Hirotsugu Takabatake, Sapporo Minami-Sanjo Hospital (Japan)
Masaki Mori, Sapporo-Kosei Hospital (Japan)
Hiroshi Natori, Keiwa-Kai Nishioka Hospital (Japan)
Kensaku Mori, Nagoya Univ. (Japan)

Published in SPIE Proceedings Vol. 8315:
Medical Imaging 2012: Computer-Aided Diagnosis
Bram van Ginneken; Carol L. Novak, Editor(s)

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