Automated segmentation of upper digestive tract from abdominal contrast-enhanced CT data using hierarchical statistical modeling of organ interrelations

S. Hirayama; Y. Otake; T. Okada; M. Hori; N. Tomiyama; Y. Sato

doi:10.1117/12.2216593

21 March 2016 Automated segmentation of upper digestive tract from abdominal contrast-enhanced CT data using hierarchical statistical modeling of organ interrelations

S. Hirayama, Y. Otake, T. Okada, M. Hori, N. Tomiyama, Y. Sato

Author Affiliations +

Proceedings Volume 9784, Medical Imaging 2016: Image Processing; 97840F (2016) https://doi.org/10.1117/12.2216593
Event: SPIE Medical Imaging, 2016, San Diego, California, United States

Abstract

We have been studying the automatic segmentation of multi-organ region from abdominal CT images. In previous work, we proposed an approach using a hierarchical statistical modeling using a relationship between organs. In this paper, we have proposed automatic segmentation of the upper digestive tract from abdominal contrast-enhanced CT using previously segmented multiple organs. We compared segmentation accuracy of the esophagus, stomach and duodenum between our proposed method using hierarchical statistical modeling and a conventional statistical atlas method. Additionally, preliminary experiment was performed which added the region representing gas to the candidate region at the segmentation step. The segmentation results were evaluated quantitatively by Dice coefficient, Jaccard index and the average symmetric surface distance of the segmented region and correct region data. Percentage of the average of Dice coefficient of esophagus, stomach and duodenum were 58.7, 68.3, and 38.6 with prediction-based method and 23.7, 51.1, and 24.4 with conventional atlas method.

Citation Download Citation

S. Hirayama, Y. Otake, T. Okada, M. Hori, N. Tomiyama, and Y. Sato "Automated segmentation of upper digestive tract from abdominal contrast-enhanced CT data using hierarchical statistical modeling of organ interrelations", Proc. SPIE 9784, Medical Imaging 2016: Image Processing, 97840F (21 March 2016); https://doi.org/10.1117/12.2216593

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