MICCAI 2013 - LNCS 8149-8151

Multi-organ Segmentation Based on Spatially-Divided Probabilistic Atlas from 3D Abdominal CT Images

Chengwen Chu¹, Masahiro Oda¹, Takayuki Kitasaka², Kazunari Misawa³, Michitaka Fujiwara⁴, Yuichiro Hayashi⁵, Yukitaka Nimura⁵, Daniel Rueckert⁶, and Kensaku Mori⁵

¹Graduate School of Information Science, Nagoya University, Nagoya, Japan

²Aichi Institute of Technology, Toyota, Japan

³Aichi Cancer Center Hospital, Nagoya, Japan

⁴Graduate School of Medicine, Nagoya University, Nagoya, Japan

⁵Information and Communications Headquarters, Nagoya University, Nagoya, Japan

⁶Imperial College London, London, UK

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.

LNCS 8150, p. 165 ff.

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