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Proceedings Paper

Automated torso organ segmentation from 3D CT images using structured perceptron and dual decomposition
Author(s): Yukitaka Nimura; Yuichiro Hayashi; Takayuki Kitasaka; Kensaku Mori
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Paper Abstract

This paper presents a method for torso organ segmentation from abdominal CT images using structured perceptron and dual decomposition. A lot of methods have been proposed to enable automated extraction of organ regions from volumetric medical images. However, it is necessary to adjust empirical parameters of them to obtain precise organ regions. This paper proposes an organ segmentation method using structured output learning. Our method utilizes a graphical model and binary features which represent the relationship between voxel intensities and organ labels. Also we optimize the weights of the graphical model by structured perceptron and estimate the best organ label for a given image by dynamic programming and dual decomposition. The experimental result revealed that the proposed method can extract organ regions automatically using structured output learning. The error of organ label estimation was 4.4%. The DICE coefficients of left lung, right lung, heart, liver, spleen, pancreas, left kidney, right kidney, and gallbladder were 0.91, 0.95, 0.77, 0.81, 0.74, 0.08, 0.83, 0.84, and 0.03, respectively.

Paper Details

Date Published: 20 March 2015
PDF: 6 pages
Proc. SPIE 9414, Medical Imaging 2015: Computer-Aided Diagnosis, 94143L (20 March 2015); doi: 10.1117/12.2081774
Show Author Affiliations
Yukitaka Nimura, Graduate School of Medicine, Nagoya Univ. (Japan)
Yuichiro Hayashi, Graduate School of Medicine, Nagoya Univ. (Japan)
Takayuki Kitasaka, Aichi Institute of Technology (Japan)
Kensaku Mori, Graduate School of Medicine, Nagoya Univ. (Japan)


Published in SPIE Proceedings Vol. 9414:
Medical Imaging 2015: Computer-Aided Diagnosis
Lubomir M. Hadjiiski; Georgia D. Tourassi, Editor(s)

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