To deform or not: treatment-aware longitudinal registration for breast
  DCE-MRI during neoadjuvant chemotherapy via unsupervised keypoints detection
Luyi Han Tao Tan Tianyu Zhang Yuan Gao Xin Wang Valentina Longo Sofía Ventura-Díaz Anna D'Angelo Jonas Teuwen Ritse Mann
Clinicians compare breast DCE-MRI after neoadjuvant chemotherapy (NAC) with
pre-treatment scans to evaluate the response to NAC. Clinical evidence supports
that accurate longitudinal deformable registration without deforming treated
tumor regions is key to quantifying tumor changes. We propose a conditional
pyramid registration network based on unsupervised keypoint detection and
selective volume-preserving to quantify changes over time. In this approach, we
extract the structural and the abnormal keypoints from DCE-MRI, apply the
structural keypoints for the registration algorithm to restrict large
deformation, and employ volume-preserving loss based on abnormal keypoints to
keep the volume of the tumor unchanged after registration. We use a clinical
dataset with 1630 MRI scans from 314 patients treated with NAC. The results
demonstrate that our method registers with better performance and better volume
preservation of the tumors. Furthermore, a local-global-combining biomarker
based on the proposed method achieves high accuracy in pathological complete
response (pCR) prediction, indicating that predictive information exists
outside tumor regions. The biomarkers could potentially be used to avoid
unnecessary surgeries for certain patients. It may be valuable for clinicians
and/or computer systems to conduct follow-up tumor segmentation and response
prediction on images registered by our method. Our code is available on
\url{https://github.com/fiy2W/Treatment-aware-Longitudinal-Registration}.