A positive margin may result in an increased risk of local recurrences after breast retention surgery for any malignant tumour. 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 aims to design an intra-operative tumour margin evaluation scheme by using specimen mammography in breast-conserving surgery. Total of 30 cases were evaluated and compared with the manually determined contours by experienced physicians and pathology report. The proposed method utilizes image thresholding to extract regions of interest and then performs a deep learning model, i.e. SegNet, to segment tumour tissue. The margin width of normal tissues surrounding it is evaluated as the result. The desired size of margin around the tumor was set for 10 mm. The smallest average difference to manual sketched margin (6.53 mm +- 5.84). In the all case, the SegNet architecture was utilized to obtain tissue specimen boundary and tumor contour, respectively. The simulation results indicated that this technology is helpful in discriminating positive from negative margins in the intra-operative setting. The aim of proposed scheme was a potential procedure in the intra-operative measurement system. The experimental results reveal that deep learning techniques can draw results that are consistent with pathology reports.

翻译：恶性肿瘤保乳手术后，阳性切缘可能增加局部复发风险。为减少阳性切缘数量，需为外科医生提供术中实时信息以判断是否存在阳性切除切缘。本研究旨在利用标本乳腺摄影技术设计保乳手术的术中肿瘤切缘评估方案。共评估30例病例，并与经验丰富的医师手动勾画的肿瘤轮廓及病理报告进行对比。所提方法采用图像阈值提取感兴趣区域，进而运用深度学习模型（即SegNet）分割肿瘤组织，并评估其周围正常组织的切缘宽度。肿瘤周围理想切缘宽度设定为10毫米，与手动勾画切缘的最小平均差值为（6.53毫米±5.84）。所有病例均使用SegNet架构分别获取组织标本边界与肿瘤轮廓。仿真结果表明，该技术有助于在术中区分阳性与阴性切缘。所提方案旨在作为术中测量系统的潜在操作流程。实验结果揭示，深度学习技术能够得出与病理报告一致的结果。