Abstract
Breast-conserving surgery requires supportive radiotherapy to prevent cancer recurrence. However, the task of localizing the tumor bed to be irradiated is not trivial. The automatic image registration could significantly aid the tumor bed localization and lower the radiation dose delivered to the surrounding healthy tissues. This study proposes a novel image registration method dedicated to breast tumor bed localization addressing the problem of missing data due to tumor resection that may be applied to real-time radiotherapy planning. We propose a deep learning-based nonrigid image registration method based on a modified U-Net architecture. The algorithm works simultaneously on several image resolutions to handle large deformations. Moreover, we propose a dedicated volume penalty that introduces the medical knowledge about tumor resection into the registration process. The proposed method may be useful for improving real-time radiation therapy planning after the tumor resection and, thus, lower the surrounding healthy tissues’ irradiation. The data used in this study consist of 30 computed tomography scans acquired in patients with diagnosed breast cancer, before and after tumor surgery. The method is evaluated using the target registration error between manually annotated landmarks, the ratio of tumor volume, and the subjective visual assessment. We compare the proposed method to several other approaches and show that both the multilevel approach and the volume regularization improve the registration results. The mean target registration error is below 6.5 mm, and the relative volume ratio is close to zero. The registration time below 1 s enables the real-time processing. These results show improvements compared to the classical, iterative methods or other learning-based approaches that do not introduce the knowledge about tumor resection into the registration process. In future research, we plan to propose a method dedicated to automatic localization of missing regions that may be used to automatically segment tumors in the source image and scars in the target image.
Highlights
The proposed contributions are compared to the state-of-the-art using both the quantitative (target registration error (TRE), tumor volume ratio (TVR)) and the qualitative visual assessment
The TRE should be interpreted with care, keeping in mind that the correspondence cannot be directly estimated between the tumor in the moving image and its bed in the fixed image
We propose a novel, semi-supervised deep learning (DL)-based image registration method dedicated to tumor bed localization
Summary
Breast cancer is the most frequently diagnosed cancer among women and the second most common cancer worldwide. More than 2 million new cases were diagnosed in 2018, and more than half a million deaths. Breast cancer accounts for more than a quarter of all malignant tumor cases. It is crucial to increase the quality of breast cancer therapy [1,2]
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