Multi-organ segmentation based on spatially-divided probabilistic atlas from 3D abdominal CT images

Chengwen Chu; Masahiro Oda; Takayuki Kitasaka; Kazunari Misawa; Michitaka Fujiwara; Yuichiro Hayashi; Yukitaka Nimura; Daniel Rueckert; Kensaku Mori

doi:10.1007/978-3-642-40763-5_21

Multi-organ segmentation based on spatially-divided probabilistic atlas from 3D abdominal CT images

Chengwen Chu, Masahiro Oda, Takayuki Kitasaka, Kazunari Misawa, Michitaka Fujiwara, Yuichiro Hayashi, Yukitaka Nimura, Daniel Rueckert, Kensaku Mori

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

111 Scopus citations

Abstract

This paper presents an automated multi-organ segmentation method for 3D abdominal CT images based on a spatially-divided probabilistic atlases. Most previous abdominal organ segmentation methods are ineffective to deal with the large differences among patients in organ shape and position in local areas. In this paper, we propose an automated multi-organ segmentation method based on a spatially-divided probabilistic atlas, and solve this problem by introducing a scale hierarchical probabilistic atlas. The algorithm consists of image-space division and a multi-scale weighting scheme. The generated spatial-divided probabilistic atlas efficiently reduces the inter-subject variance in organ shape and position either in global or local regions. Our proposed method was evaluated using 100 abdominal CT volumes with manually traced ground truth data. Experimental results showed that it can segment the liver, spleen, pancreas, and kidneys with Dice similarity indices of 95.1%, 91.4%, 69.1%, and 90.1%, respectively.

Original language	English
Title of host publication	Medical Image Computing and Computer-Assisted Intervention, MICCAI 2013 - 16th International Conference, Proceedings
Pages	165-172
Number of pages	8
Edition	PART 2
DOIs	https://doi.org/10.1007/978-3-642-40763-5_21
State	Published - 2013
Externally published	Yes
Event	16th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2013 - Nagoya, Japan Duration: 22 Sep 2013 → 26 Sep 2013

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Number	PART 2
Volume	8150 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	16th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2013
Country/Territory	Japan
City	Nagoya
Period	22/09/13 → 26/09/13

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10.1007/978-3-642-40763-5_21

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Chu, C., Oda, M., Kitasaka, T., Misawa, K., Fujiwara, M., Hayashi, Y., Nimura, Y., Rueckert, D., & Mori, K. (2013). Multi-organ segmentation based on spatially-divided probabilistic atlas from 3D abdominal CT images. In Medical Image Computing and Computer-Assisted Intervention, MICCAI 2013 - 16th International Conference, Proceedings (PART 2 ed., pp. 165-172). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 8150 LNCS, No. PART 2). https://doi.org/10.1007/978-3-642-40763-5_21

Chu, Chengwen ; Oda, Masahiro ; Kitasaka, Takayuki et al. / Multi-organ segmentation based on spatially-divided probabilistic atlas from 3D abdominal CT images. Medical Image Computing and Computer-Assisted Intervention, MICCAI 2013 - 16th International Conference, Proceedings. PART 2. ed. 2013. pp. 165-172 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); PART 2).

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title = "Multi-organ segmentation based on spatially-divided probabilistic atlas from 3D abdominal CT images",

abstract = "This paper presents an automated multi-organ segmentation method for 3D abdominal CT images based on a spatially-divided probabilistic atlases. Most previous abdominal organ segmentation methods are ineffective to deal with the large differences among patients in organ shape and position in local areas. In this paper, we propose an automated multi-organ segmentation method based on a spatially-divided probabilistic atlas, and solve this problem by introducing a scale hierarchical probabilistic atlas. The algorithm consists of image-space division and a multi-scale weighting scheme. The generated spatial-divided probabilistic atlas efficiently reduces the inter-subject variance in organ shape and position either in global or local regions. Our proposed method was evaluated using 100 abdominal CT volumes with manually traced ground truth data. Experimental results showed that it can segment the liver, spleen, pancreas, and kidneys with Dice similarity indices of 95.1%, 91.4%, 69.1%, and 90.1%, respectively.",

author = "Chengwen Chu and Masahiro Oda and Takayuki Kitasaka and Kazunari Misawa and Michitaka Fujiwara and Yuichiro Hayashi and Yukitaka Nimura and Daniel Rueckert and Kensaku Mori",

year = "2013",

doi = "10.1007/978-3-642-40763-5_21",

language = "English",

isbn = "9783642407628",

series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

number = "PART 2",

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booktitle = "Medical Image Computing and Computer-Assisted Intervention, MICCAI 2013 - 16th International Conference, Proceedings",

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note = "16th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2013 ; Conference date: 22-09-2013 Through 26-09-2013",

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Chu, C, Oda, M, Kitasaka, T, Misawa, K, Fujiwara, M, Hayashi, Y, Nimura, Y, Rueckert, D & Mori, K 2013, Multi-organ segmentation based on spatially-divided probabilistic atlas from 3D abdominal CT images. in Medical Image Computing and Computer-Assisted Intervention, MICCAI 2013 - 16th International Conference, Proceedings. PART 2 edn, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), no. PART 2, vol. 8150 LNCS, pp. 165-172, 16th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2013, Nagoya, Japan, 22/09/13. https://doi.org/10.1007/978-3-642-40763-5_21

Multi-organ segmentation based on spatially-divided probabilistic atlas from 3D abdominal CT images. / Chu, Chengwen; Oda, Masahiro; Kitasaka, Takayuki et al.
Medical Image Computing and Computer-Assisted Intervention, MICCAI 2013 - 16th International Conference, Proceedings. PART 2. ed. 2013. p. 165-172 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 8150 LNCS, No. PART 2).

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

TY - GEN

T1 - Multi-organ segmentation based on spatially-divided probabilistic atlas from 3D abdominal CT images

AU - Chu, Chengwen

AU - Oda, Masahiro

AU - Kitasaka, Takayuki

AU - Misawa, Kazunari

AU - Fujiwara, Michitaka

AU - Hayashi, Yuichiro

AU - Nimura, Yukitaka

AU - Rueckert, Daniel

AU - Mori, Kensaku

PY - 2013

Y1 - 2013

N2 - This paper presents an automated multi-organ segmentation method for 3D abdominal CT images based on a spatially-divided probabilistic atlases. Most previous abdominal organ segmentation methods are ineffective to deal with the large differences among patients in organ shape and position in local areas. In this paper, we propose an automated multi-organ segmentation method based on a spatially-divided probabilistic atlas, and solve this problem by introducing a scale hierarchical probabilistic atlas. The algorithm consists of image-space division and a multi-scale weighting scheme. The generated spatial-divided probabilistic atlas efficiently reduces the inter-subject variance in organ shape and position either in global or local regions. Our proposed method was evaluated using 100 abdominal CT volumes with manually traced ground truth data. Experimental results showed that it can segment the liver, spleen, pancreas, and kidneys with Dice similarity indices of 95.1%, 91.4%, 69.1%, and 90.1%, respectively.

AB - This paper presents an automated multi-organ segmentation method for 3D abdominal CT images based on a spatially-divided probabilistic atlases. Most previous abdominal organ segmentation methods are ineffective to deal with the large differences among patients in organ shape and position in local areas. In this paper, we propose an automated multi-organ segmentation method based on a spatially-divided probabilistic atlas, and solve this problem by introducing a scale hierarchical probabilistic atlas. The algorithm consists of image-space division and a multi-scale weighting scheme. The generated spatial-divided probabilistic atlas efficiently reduces the inter-subject variance in organ shape and position either in global or local regions. Our proposed method was evaluated using 100 abdominal CT volumes with manually traced ground truth data. Experimental results showed that it can segment the liver, spleen, pancreas, and kidneys with Dice similarity indices of 95.1%, 91.4%, 69.1%, and 90.1%, respectively.

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DO - 10.1007/978-3-642-40763-5_21

M3 - Conference contribution

C2 - 24579137

AN - SCOPUS:84897577322

SN - 9783642407628

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

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T2 - 16th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2013

Y2 - 22 September 2013 through 26 September 2013

ER -

Chu C, Oda M, Kitasaka T, Misawa K, Fujiwara M, Hayashi Y et al. Multi-organ segmentation based on spatially-divided probabilistic atlas from 3D abdominal CT images. In Medical Image Computing and Computer-Assisted Intervention, MICCAI 2013 - 16th International Conference, Proceedings. PART 2 ed. 2013. p. 165-172. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); PART 2). doi: 10.1007/978-3-642-40763-5_21

Multi-organ segmentation based on spatially-divided probabilistic atlas from 3D abdominal CT images

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