Semi-automatic level set segmentation of liver tumors combining a spiral-scanning technique with supervised fuzzy pixel classification |
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| Authors: | Dirk Smeets Dirk Loeckx Bert Stijnen Bart De Dobbelaer Dirk Vandermeulen Paul Suetens |
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| Institution: | 1. School of Biomedical Engineering, Shenzhen University, National-Regional Key Technology Engineering Laboratory for Medical Ultrasound, Guangdong Key Laboratory for Biomedical Measurements and Ultrasound Imaging, Shenzhen 518060, China;2. Shenzhen Institute of Hepatology, Shenzhen Third People''s Hospital, Shenzhen 518020, China;1. College of Electronics and Information Engineering, Tongji University, Shanghai 200092, China;2. School of Computer Engineering and Science, Shanghai University, Shanghai 200444, China;3. Iwate Prefectural University, Faculty of Software and Information Science, Iwate, Japan;4. Eastern Hepatobiliary Surgery Hospital, Shanghai, China;1. Faculty of Engineering, Department of Biomedical Engineering, Medical Image Processing Laboratory, Tel Aviv University, Tel Aviv 69978, Israel;2. Sheba Medical Center, Diagnostic Imaging Department, Abdominal Imaging Unit, affiliated to Sackler School of Medicine, Tel Aviv University, Tel Hashomer 52621, Israel |
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| Abstract: | In this paper, a specific method is presented to facilitate the semi-automatic segmentation of liver tumors and liver metastases in CT images. Accurate and reliable segmentation of tumors is essential for the follow-up of cancer treatment. The core of the algorithm is a level set method. The initialization is generated by a spiral-scanning technique based on dynamic programming. The level set evolves according to a speed image that is the result of a statistical pixel classification algorithm with supervised learning. This method is tested on CT images of the abdomen and compared with manual delineations of liver tumors. The described method outperformed the semi-automatic methods of the other participants of the “3D Liver Tumor Segmentation Challenge 2008”. Evaluating the algorithm on the provided test data leads to an average overlap error of 32.6% and an average volume difference of 17.9%. The average, the RMS and the maximum surface distance are 2.0, 2.6 and 10.1 mm, respectively. |
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