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Intra-operative Tumor Margin Evaluation in Breast-Conserving Surgery with Deep Learning

Yi-Chun Chen1, Dar-Ren Chen2, Hwa-Koon Wu2, and Yu-Len Huang1
1. Tunghai University, Department of Computer Science, Taichung, Taiwan
2. Changhua Christian Hospital, Comprehensive Breast Cancer Center, Changhua, Taiwan

Abstract—Breast-Conserving Therapy (BCT) followed by irradiation is the treatment of choice for early-stage breast cancer. A positive margin may result in an increased risk of local recurrences after BCT for any malignant tumor. In order to reduce the number of positive margins would offer surgeon real-time intra-operative information on the presence of positive resection margins. This study proposed an intra-operative tumor margin evaluation in breastconserving surgery. The proposed method utilized image segmentation and deep learning techniques to segment the cancerous tissue and then to evaluate the margin width of normal tissues surrounding it. With this work, surgeons might have more information to get clean margins when performing breast conserving surgeries.

Index Terms—intra-operative margin evaluation, breastconserving therapy, specimen mammography, deep learning, image segmentation

Cite: Yi-Chun Chen, Dar-Ren Chen, Hwa-Koon Wu, and Yu-Len Huang, "Intra-operative Tumor Margin Evaluation in Breast-Conserving Surgery with Deep Learning," Journal of Image and Graphics, Vol. 7, No. 3, pp. 90-93, September 2019. doi: 10.18178/joig.7.3.90-93

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