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Abstract #2318

Automatic Detection and Segmentation of Breast Cancer on MRI Using Mask R-CNN Trained on Non-Fat-Sat Images and Tested on Fat-Sat Images

Yang Zhang1, Jiejie Zhou2, Youngjean Park3, Siwa Chan4, Meihao Wang2, Min Jung Kim3, Kai-Ting Chang1, Peter Chang1, Daniel Chow1, Jeon-Hor Chen1,5, and Min-Ying Su1
1Department of Radiological Science, University of California, Irvine, CA, United States, 2Department of Radiology, First Affiliate Hospital of Wenzhou Medical University, Wenzhou, China, 3Department of Radiology and Research Institute of Radiological Science, Severance Hospital, Yonsei University College of Medicine, Seoul, Korea, Republic of, 4Department of Medical Imaging, Taichung Tzu-Chi Hospital, Taichung, Taiwan, 5Department of Radiology, E-Da Hospital and I-Shou University, Kaohsiung, Taiwan

The mask R-CNN algorithm was implemented to search entire images to identify suspicious lesions for further evaluation of malignancy probability. One training (N=98, Siemens 1.5T, non-fat-sat) and two independent testing (N=241, Siemens 3T, non-fat-sat; and N=91, GE 3T, fat-sat) datasets were used. The pre-contrast and subtraction image, and the subtraction image of the contralateral breast, were used as three inputs. The training set had a total of 1353 positive slices (containing lesion), 8055 negative slices without lesion. The 10-fold cross-validation showed accuracy=0.80 and mean DSC= 0.82. The accuracy was 0.73 and 0.62 for two testing datasets, lower for fat-sat images.

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