Deep pancreas segmentation through quantification of pancreatic uncertainty on abdominal CT images

Hyeon Dham Yoon; Hyeonjin Kim; Helen Hong

doi:10.1117/12.2611454

4 April 2022 Deep pancreas segmentation through quantification of pancreatic uncertainty on abdominal CT images

Hyeon Dham Yoon, Hyeonjin Kim, Helen Hong

Proceedings Volume 12033, Medical Imaging 2022: Computer-Aided Diagnosis; 120333P (2022) https://doi.org/10.1117/12.2611454
Event: SPIE Medical Imaging, 2022, San Diego, California, United States

Abstract

Accurate segmentation of the pancreas on abdominal CT images is a prerequisite step for understanding the shape of the pancreas in pancreatic cancer diagnosis, surgery, and treatment planning. However, pancreas segmentation is very challenging due to the characteristics of the pancreas about the high within and between patient variability and the poor contrast with surrounding organs. In addition, the uncertain area arising from variability in the location and morphology of the pancreas will lead to over-segmentation or under-segmentation. Therefore, the purpose of this study is to improve the performance of pancreas segmentation by increasing the level of confidence through multi-scale prediction network (MP-Net) for areas with high uncertainty due to the characteristics of the pancreas. First, the pancreas is localized using U-Net based 2D segmentation networks on the three-orthogonal planes and combined through a majority voting. Second, the localized pancreas is segmented using a 2D MP-Net considering pancreatic uncertainty from multi-scale prediction results. The average F1-score, recall, and precision of the proposed pancreas segmentation method were 78.60%, 78.44%, and 79.72%, respectively. Our deep pancreas segmentation can be used to reduce intra- and inter-patient variations for understanding the shape of the pancreas, which is helpful in diagnosing cancer, surgery, and planning treatment.

Conference Presentation

Citation Download Citation

Hyeon Dham Yoon, Hyeonjin Kim, and Helen Hong "Deep pancreas segmentation through quantification of pancreatic uncertainty on abdominal CT images", Proc. SPIE 12033, Medical Imaging 2022: Computer-Aided Diagnosis, 120333P (4 April 2022); https://doi.org/10.1117/12.2611454

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