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Abstract #2313

Automatic Segmentation of Tumor-related Vessels of Breast Cancer on Ultrafast DCE MRI using U-Net  

Masako Kataoka1, Takuto Fukutome2, Tomohiro Takemura2, Kango Kawase2, Kojiro Yano3, Maya Honda4, Mami Iima4, Akane Ohashi4, Masakazu Toi5, and Kaori Togashi4
1Radiology (Diagnostic Imaging and Nuclear Medicine), Kyoto Univ. Hospital, Kyoto, Japan, 2Faculty of Medicine, Kyoto University, Kyoto, Japan, 3Osaka Institute of Technology, Osaka, Japan, 4Kyoto University Graduate School of Medicine, Kyoto, Japan, 5Kyoto University, Kyoto, Japan

We aimed to develop a system for automatic segmentation of tumor-related vessels on ultrafast dynamic contrast (UF-DCE) MRI using U-net. Training set consisted of image dataset of 20 MIP images obtained from -15 to +60 sec after contrast injection from 59 patients. Exclusion criteria were those with poor image quality. The dice similarity coefficient was above 0.8 for training set, 0.6 for validation set. Careful analysis of failed cases revealed that inaccurate segmentation of the vessel were caused by low-contrast images, noisy-images, artifact, bright skin line due to incomplete fat suppression, and non-mass enhancement that may mimic vasculature.

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