An End-to-End Solution for Automatic Contouring of Tumor Region in Intraoperative Images of Breast Lumpectomy

• Main Authors: Gerolami, J., Wu, V., Fauerbach, P. Nasute, Jabs, D., Engel, C. J., Rudan, J., Merchant, S., Walker, R., Anas, E. M. A., Abolmaesumi, P., Fichtinger, G., Ungi, T., Mousavi, P.
• Format: Conference Proceeding
• Language: English
• ISSN: 1558-4615; 2694-0604
• DOI: 10.1109/EMBC44109.2020.9176505

Breast-conserving surgery, also known as lumpectomy, is an early stage breast cancer treatment that aims to spare as much healthy breast tissue as possible. A risk associated with lumpectomy is the presence of cancer positive margins post operation. Surgical navigation has been shown to reduce cancer positive margins but requires manual segmentation of the tumor intraoperatively. In this paper, we propose an end-to-end solution for automatic contouring of breast tumor from intraoperative ultrasound images using two convolutional neural network architectures, the U-Net and residual U-Net. The networks are trained on annotated intraoperative breast ultrasound images and evaluated on the quality of predicted segmentations. This work brings us one step closer to providing surgeons with an automated surgical navigation system that helps reduce cancer-positive margins during lumpectomy.