In breast surgical planning, accurate registration of MR images across
patient positions has the potential to improve the localisation of tumours
during breast cancer treatment. While learning-based registration methods have
recently become the state-of-the-art approach for most medical image
registration tasks, these methods have yet to make inroads into breast image
registration due to certain difficulties-the lack of rich texture information
in breast MR images and the need for the deformations to be diffeomophic. In
this work, we propose learning strategies for breast MR image registration that
are amenable to diffeomorphic constraints, together with early experimental
results from in-silico and in-vivo experiments. One key contribution of this
work is a registration network which produces superior registration outcomes
for breast images in addition to providing diffeomorphic guarantees.

在乳腺手术规划中，通过准确地对患者不同位置的磁共振图像进行注册，可以改善乳腺癌治疗中肿瘤的定位。本文提出了适用于乳腺磁共振图像注册的学习策略，克服了乳腺图像中纹理信息不足和变形需要满足微分同胚约束的困难，同时给出了基于模拟和体内实验的早期实验结果。本文的一个关键贡献是提出的注册网络在乳腺图像的注册中产生了优越的结果，并提供了微分同胚保证。