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Article

Semi-Supervised Deep Learning-Based Image Registration Method with Volume Penalty for Real-Time Breast Tumor Bed Localization

1
Department of Measurement and Electronics, AGH University of Science and Technology, PL30059 Kraków, Poland
2
Department of Radiotherapy, The Holycross Cancer Center, PL25734 Kielce, Poland
3
Department of Medical Physics, The Holycross Cancer Center, PL25734 Kielce, Poland
4
Collegium Medicum, Institute of Health Sciences, Jan Kochanowski University, PL25369 Kielce, Poland
*
Author to whom correspondence should be addressed.
Academic Editor: Enrico G. Caiani
Sensors 2021, 21(12), 4085; https://doi.org/10.3390/s21124085
Received: 19 May 2021 / Revised: 7 June 2021 / Accepted: 11 June 2021 / Published: 14 June 2021
(This article belongs to the Special Issue Biomedical Image and Signals for Treatment Monitoring)
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. View Full-Text
Keywords: deep learning; image registration; missing data; radiotherapy; breast-conserving surgery deep learning; image registration; missing data; radiotherapy; breast-conserving surgery
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MDPI and ACS Style

Wodzinski, M.; Ciepiela, I.; Kuszewski, T.; Kedzierawski, P.; Skalski, A. Semi-Supervised Deep Learning-Based Image Registration Method with Volume Penalty for Real-Time Breast Tumor Bed Localization. Sensors 2021, 21, 4085. https://doi.org/10.3390/s21124085

AMA Style

Wodzinski M, Ciepiela I, Kuszewski T, Kedzierawski P, Skalski A. Semi-Supervised Deep Learning-Based Image Registration Method with Volume Penalty for Real-Time Breast Tumor Bed Localization. Sensors. 2021; 21(12):4085. https://doi.org/10.3390/s21124085

Chicago/Turabian Style

Wodzinski, Marek, Izabela Ciepiela, Tomasz Kuszewski, Piotr Kedzierawski, and Andrzej Skalski. 2021. "Semi-Supervised Deep Learning-Based Image Registration Method with Volume Penalty for Real-Time Breast Tumor Bed Localization" Sensors 21, no. 12: 4085. https://doi.org/10.3390/s21124085

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