Dr. Wei Li Profile

Dr. Wei Li

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Proceedings Article | 15 February 2021 Poster + Paper

Automatic tumor segmentation in 3D automated breast ultrasound using convolutional neural network

Yang Lei, Xiuxiu He, Tonghe Wang, Jincao Yao, Lijing Wang, Wei Li, Walter Curran, Tian Liu, Dong Xu, Xiaofeng Yang

Proceedings Volume 11602, 1160219 (2021) https://doi.org/10.1117/12.2581144

KEYWORDS: Tumors, Breast, Ultrasonography, Image segmentation, Convolutional neural networks, 3D acquisition, Mammography, Visualization

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Automatic breast ultrasound (ABUS) imaging provides complementary information when other imaging modalities (i.e., mammography) are not conclusive in the task of tumor detection. It enables sectional plane visualization, simplified temporal comparison and coronal depiction, and features higher reproducibility than conventional ultrasound imaging. Although the 3D ABUS acquisition significantly reduces the acquisition time and cost, the manual segmentation of tumor on 3D ABUS could be time-consuming and labor-intensive due to its high slice number. This work aims to develop a deep-learning-based method to automatically segment the breast tumor on 3D ABUS. We integrated mask scoring-based self-correlation strategy into the R-CNN-based method to force the final segmented tumor contour to be more reasonable. We tested the performance of the proposed method using 3D ABUS of 40 patients who are confirmed with breast tumor through four-fold cross validation test. The comparison between our results and the ground truth contours was quantified by metrics including the Dice similarity coefficient (DSC), Jaccard index (JAC), 95% Hausdorff distance (HD95). The mean DSC, JAC, and HD95 were 0.85 ± 0.10, 0.75 ± 0.14, and 1.65 ± 1.20 (mm), respectively.

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