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.

翻译：[translated abstract in Chinese] 乳腺癌的标准治疗方案通常包括新辅助治疗，随后通过手术切除肿瘤及周围组织。病理学家通常依赖柜式X射线影像（即Faxitron）检查切除的乳腺组织，并评估残留病灶的程度。然而，准确定位残留癌的位置、大小和局灶性具有挑战性，错误评估可能导致临床后果。利用自动化方法可改进组织病理学流程，使病理学家能够更有效、更精确地选择采样区域。尽管已认识到其必要性，但目前尚无此类可用方法。训练此类自动化检测模型需要获取离体放射学图像上的精确真实标签，这可通过配准Faxitron与组织病理学图像，并将癌症范围从组织病理学图像映射到X射线图像来实现。本研究提出了一种基于深度学习的图像配准方法，该方法基于单模态合成图像对进行训练。模型使用了50名接受新辅助化疗并手术的妇女的数据进行训练。结果表明，我们的方法速度更快，且平均标志点误差（$2.1\pm1.96$ mm）显著低于现有最优的迭代方法（$4.43\pm4.1$ mm）和深度学习方法（$4.02\pm3.15$ mm）。我们的方法在整合放射学与病理学信息方面的性能提升，有助于生成大规模数据集，从而训练更准确的乳腺癌检测模型。