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Abstract #0531

Automatic segmentation of uterine endometrial cancer on MRI with convolutional neural network

Yasuhisa Kurata1, Mizuho Nishio1, Yusaku Moribata2, Aki Kido1, Yuki Himoto1, Koji Fujimoto3, Masahiro Yakami2, Sachiko Minamiguchi4, Masaki Mandai5, and Yuji Nakamoto1
1Diagnostic Imaging and Nuclear Medicine, Kyoto university hospital, Kyoto, Japan, 2Preemptive Medicine and Lifestyle-Related Disease Research Center, Kyoto university hospital, Kyoto, Japan, 3Real World Data Research and Development, Graduate School of Medicine Kyoto University, Kyoto, Japan, 4Diagnostic Pathology, Kyoto university hospital, Kyoto, Japan, 5Gynecology and Obstetrics, Kyoto university hospital, Kyoto, Japan

Endometrial cancer is the most common gynecological malignant tumor in developed countries, and accurate preoperative risk stratification is essential for personalized medicine. For realizing tumor feature extraction by radiomics approach, the segmentation of the tumor is usually required. The model developed in this study has achieved high-accuracy automatic segmentation of endometrial cancer on MRI using a convolutional neural network for the first time. Using multi-sequence MR images were important for high accuracy segmentation. Our model will lead to efficient medical image analysis of a large number of cases using the radiomics approach and/or deep learning methods.

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