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Abstract #1891

3D Mask R-CNN model comparison for brain tumor segmentation in individual modality dataset

Huijin Song1, Eunji Kim2, Hyunsil Cha2, Moon Jung Hwang3, Yongmin Chang2,4, and Chul-Ho Sohn5
1Biomedical Research Institute, Seoul National University Hospital, Seoul, Korea, Republic of, 2Medical & Biological Engineering, Kyungpook National University, Daegu, Korea, Republic of, 3GE Health Korea, Seoul, Korea, Republic of, 4Radiology, College of Medicine, Kyungpook National University, Daegu, Korea, Republic of, 5Radiology, Seoul National University Hospital, Seoul, Korea, Republic of

Deep learning-based brain tumor segmentation requires multi-modality dataset for a precision accuracy. However, multi-modality data acquisition has a limitation due to several reasons. In this study, we propose that a 3D Mask R-CNN network model could provide a reliable accuracy in individual modality dataset for brain tumor segmentation.

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