@inproceedings{c11b2ee7a7414c25aad433049a38291c,
title = "Automatic 3D segmentation of renal cysts in CT",
abstract = "A fully automatic methodology for renal cysts detection and segmentation in abdominal computed tomography is presented in this paper. The segmentation workflow begins with the lungs segmentation followed by the kidneys extraction using marker controlled watershed algorithm. Detection of candidate cysts employs the artificial neural network classifier supplied by shape-related 3D object features. Anisotropic diffusion filtering and hybrid level set method are used at the fine segmentation stage. During the evaluation 23 out of 25 cysts delineated by an expert within 16 studies were detected correctly. The fine segmentation stage resulted in a 92.3\% sensitivity and 93.2\% Dice index combined over all detected cases.",
keywords = "Abdominal computed tomography, Artificial neural network, Image segmentation, Level sets, Renal cyst",
author = "Pawel Badura and Wojciech Wieclawek and Bartlomiej Pycinski",
note = "Publisher Copyright: {\textcopyright} Springer International Publishing Switzerland 2016.; 5th International Conference on Information Technologies in Biomedicine, ITIB 2016 ; Conference date: 20-06-2016 Through 22-06-2016",
year = "2016",
doi = "10.1007/978-3-319-39796-2\_13",
language = "English",
isbn = "9783319397955",
series = "Advances in Intelligent Systems and Computing",
publisher = "Springer Verlag",
pages = "149--163",
editor = "Ewa Pi{\c e}tka and Pawel Badura and Jacek Kawa and Wojciech Wieclawek",
booktitle = "Information Technologies in Medicine - 5th International Conference, ITIB 2016, Proceedings",
address = "Germany",
}