@inproceedings{9fff1575a0b147bf88ce14312cdf4d5c,
title = "A novel minimal surface overlay model for the whole colon wall segmentation",
abstract = "To segment the boundary of both inner and outer colon wall is of much significance for colonic polyps detection in computed tomographic colonography (CTC). However, the low contrast of CT attenuation values between colon wall and the surrounding tissues limits many traditional algorithms to achieve this task. Moreover, when sticking presents between two colon walls, the task turns to be much more complicated and the threshold level set segmentation method may fail in this situation. In view of this, we present a minimum surface overlay model to extract the inner wall in this paper. Combined with the superposition model, we are able to depict the outer wall of colon in a natural way. We validated the proposed algorithm based on 60 CTC datasets. Compared with the golden standard (the manual drawing by experts), the new presented method achieved with more than 95\% overlapping coverage rate (OCR).",
keywords = "Colonic wall, Computed tomography colonography (CTC), Levelset, Minimum surface overlay model",
author = "Huafeng Wang and Wenfeng Song and Katherine Wei and Yuan Cao and Haixia Pan and Ming Ma and Jiang Huang and Guangming Mao and Zhengrong Liang",
note = "Publisher Copyright: {\textcopyright} Springer International Publishing Switzerland 2014.; 6th International Workshop on Abdominal Imaging: Computational and Clinical Applications, ABDI 2014 held in conjunction with 17th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2014 ; Conference date: 14-09-2014 Through 14-09-2014",
year = "2014",
doi = "10.1007/978-3-319-13692-9\_17",
language = "English",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "179--187",
editor = "Hiroyuki Yoshida and N{\"a}ppi, \{Janne J.\} and Sanjay Saini",
booktitle = "Abdominal Imaging",
}