  A standard treatment protocol for breast cancer entails administering
neoadjuvant therapy followed by surgical removal of the tumor and surrounding
tissue. Pathologists typically rely on cabinet X-ray radiographs, known as
Faxitron, to examine the excised breast tissue and diagnose the extent of
residual disease. However, accurately determining the location, size, and
focality of residual cancer can be challenging, and incorrect assessments can
lead to clinical consequences. The utilization of automated methods can improve
the histopathology process, allowing pathologists to choose regions for
sampling more effectively and precisely. Despite the recognized necessity,
there are currently no such methods available. Training such automated
detection models require accurate ground truth labels on ex-vivo radiology
images, which can be acquired through registering Faxitron and histopathology
images and mapping the extent of cancer from histopathology to x-ray images.
This study introduces a deep learning-based image registration approach trained
on mono-modal synthetic image pairs. The models were trained using data from 50
women who received neoadjuvant chemotherapy and underwent surgery. The results
demonstrate that our method is faster and yields significantly lower average
landmark error ($2.1\pm1.96$ mm) over the state-of-the-art iterative
($4.43\pm4.1$ mm) and deep learning ($4.02\pm3.15$ mm) approaches. Improved
performance of our approach in integrating radiology and pathology information
facilitates generating large datasets, which allows training models for more
accurate breast cancer detection.
