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 Chang^2,4, 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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