@inproceedings{eccc7e4337084a8ab70969a0fc9be2d6,
title = "Psoas major muscle segmentation using higher-order shape prior",
abstract = "We propose a novel segmentation method based on higher-order graph cuts which enables the utilization of prior knowledge regarding anatomical shapes. We applied the method for segmentation of psoas major muscles by using combinations of logistic curves which representing their shapes. The higher-order terms consisting of variables (voxels) just inside or outside of the estimated shapes are added to the energy function to encourage the segmentation results to fit to the shapes. We verified the effectiveness of the method with 20 abdominal CT images. By comparing the segmentation results to the ground truth data prepared by a clinical expert, we validated the method where it achieved the Jaccard similarity coefficient (JSC) of 75.4 % (right major) and 77.5 % (left major). We also confirmed that the proposed method worked well for thick CT images.",
keywords = "Abdominal CT images, Graph cuts, Higher-order potential, Psoas major muscle",
author = "Tsutomu Inoue and Yoshiro Kitamura and Yuanzhong Li and Wataru Ito and Hiroshi Ishikawa",
year = "2016",
doi = "10.1007/978-3-319-42016-5_11",
language = "English",
isbn = "9783319420158",
volume = "9601",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "116--124",
booktitle = "Medical Computer Vision: Algorithms for Big Data - International Workshop, MCV 2015 and Held in Conjunction with MICCAI 2015, Revised Selected Papers",
address = "Germany",
note = "International Workshop on Medical Image Computing for Computer Assisted Intervention, 2015 MICCAI ; Conference date: 09-10-2015 Through 09-10-2015",
}