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 Chen^1,5, 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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