Abstract
One of the most frequent modalities of breast cancer treatment is surgery. Breast surgery can cause visual alterations to the breasts, due to scars and asymmetries. To enable an informed choice of treatment, the patient must be adequately informed of the aesthetic outcomes of each treatment plan. In this work, we propose an inpainting approach to manipulate breast shape and nipple position in breast images, for the purpose of predicting the aesthetic outcomes of breast cancer treatment. We perform experiments with various model architectures for the inpainting task, including invertible networks capable of manipulating breasts in the absence of ground-truth breast contour and nipple annotations. Experiments on two breast datasets show the proposed models' ability to realistically alter a patient's breasts, enabling a faithful reproduction of breast asymmetries of post-operative patients in pre-operative images.
Abstract (translated)
乳腺癌治疗中最常见的方法之一是手术。乳房手术可能会由于疤痕和不对称而导致视觉变化。为了使患者能够做出知情的治疗选择，必须充分告知他们每种治疗方案的美学效果。在这项工作中，我们提出了一种图像修复方法，用于操纵乳房形状和乳头位置，以预测乳腺癌治疗的美学结果。我们在各种模型架构上进行了实验，这些模型用于执行没有真实乳房轮廓和乳头注释情况下的乳房操作任务，包括能够在这种情况下操作乳房的可逆网络。在两个乳房数据集上的实验证明了所提出的模型有能力现实地改变患者的乳房，使术前图像中术后患者乳房不对称的真实再现成为可能。
URL
https://arxiv.org/abs/2502.05652
