Affiliation:
1. International School of Information Science & Engineering (DUT‐RUISE) Dalian University of Technology Dalian China
2. DUT‐RU Co‐Research Center of Advanced ICT for Active Life Dalian University of Technology Dalian China
3. Shengjing Hospital of China Medical University China Medical University Shenyang China
Abstract
AbstractThe pelvic structure is complex and the tumor is poorly defined from the surrounding tissues. Finding the exact tumor resection margin based on the surgeon's clinical experience alone is a time‐consuming and difficult task, which is a major factor leading to surgical failure. An accurate method for segmenting pelvic bone tumors is needed. In this paper, a semiautomatic segmentation method for pelvic bone tumors based on CT‐MR multimodal images is presented. The method combines multiple medical prior knowledge and image segmentation algorithms. Finally, the segmentation results are visualized in three dimensions. We tested the proposed method on a collection of 10 cases (97 tumor MR images in total). The segmentation results were compared with the manual annotation of the physicians. On average, our method has an accuracy of 0.9358, a recall of 0.9278, an IOU value of 0.8697, a Dice value of 0.9280, and an AUC value of 0.9632. The average error of the 3D model was within the allowable range of the surgery. The proposed algorithm can accurately segment bone tumors in pelvic MR images regardless of tumor location, size, and other factors. It provides the possibility to assist pelvic bone tumor preservation surgery.
Funder
Fundamental Research Funds for the Central Universities
National Natural Science Foundation of China
Subject
Applied Mathematics,Computational Theory and Mathematics,Molecular Biology,Modeling and Simulation,Biomedical Engineering,Software
Cited by
1 articles.
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