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
Annu Int Conf IEEE Eng Med Biol Soc. 2020 Jul;2020:2003-2006. 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.
保乳手术，也称为肿块切除术，是一种早期乳腺癌治疗方法，旨在尽可能保留更多健康的乳腺组织。与肿块切除术相关的一个风险是术后出现癌阳性切缘。手术导航已被证明可以减少癌阳性切缘，但需要在术中对肿瘤进行手动分割。在本文中，我们提出了一种端到端的解决方案，使用两种卷积神经网络架构，即U-Net和残差U-Net，从术中超声图像中自动勾勒乳腺肿瘤轮廓。这些网络在标注的术中乳腺超声图像上进行训练，并根据预测分割的质量进行评估。这项工作使我们离为外科医生提供一种自动化手术导航系统又近了一步，该系统有助于在肿块切除术中减少癌阳性切缘。
