用图像修复技术模拟乳腺癌术前术后形态，辅助患者决策
An inpainting approach to manipulate asymmetry in pre-operative breast images
- 基于图像修复技术，无标注条件下调整乳腺形状与乳头位置
- 在两个数据集上实现真实感术后形态重建，还原术后不对称性
- 适合医学影像可视化与手术方案美学评估场景
乳腺癌治疗中常见手术方式，可能引发乳房外观变化，包括疤痕和不对称。为帮助患者做出知情治疗选择，需预估不同方案的美学结果。本文提出一种图像修复方法，用于操控术前乳腺图像中的乳腺形态与乳头位置，以预测手术美学效果。实验对比了多种模型架构，包括无需真实轮廓与乳头标注的可逆网络。在两个乳腺图像数据集上的测试表明，所提模型能真实地重构患者乳房形态，准确再现术后患者的乳房不对称特征。
原文摘要 · Abstract (English)
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.
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