Advancements in Deep Learning for Automated Segmentation in Radiotherapy: Current Trends and Future Prospects

Abstract

Objective: Accurate segmentation of tumors is crucial in the field of radiotherapy to ensure that the correct radiation dose is administered to cancerous tissues while minimizing exposure to nearby healthy organs. Conventional segmentation techniques often take a considerable amount of time and are prone to human errors, while the recent advancements in deep learning, particularly through convolutional neural networks (CNN), have shown great promise in automating these procedures. In this review of the literature, we evaluate the impact of deep learning methods on automated segmentation in radiotherapy and their potential to facilitate future research in cancer treatment planning.

Methods: Databases like PubMed, IEEE Xplore, and Scopus, along with articles published from January 2018 to August 2024, were utilized for an in-depth literature review. The review focused on research exploring the use of deep learning algorithms for automating tumor segmentation in radiotherapy, as well as the challenges related to model accuracy, data quality, and integration into clinical workflows.

Results: With the rise of deep learning technologies, CNNs have enhanced both the precision and speed of tumour segmentation in radiotherapy, providing highly accurate results. These models have demonstrated exceptional effectiveness in identifying tumours across various cancer types, including brain, lung, and prostate cancers. Deep learning has facilitated the implementation of automated segmentation in treatment planning, thereby reducing the time required and, more importantly, minimizing inter-observer variability caused by manual segmentation. Furthermore, the incorporation of multi-modality imaging data (such as CT, MRI, and PET scans) into the automated segmentation models has been proven to enhance segmentation accuracy.

Conclusion: In recent years, deep learning has demonstrated remarkable capabilities in transforming tumor segmentation in the field of radiotherapy. However, there are several obstacles in the way, particularly regarding data quality, algorithm interpretability, and integration into clinical practice. Addressing these challenging issues is essential for artificial intelligence to reach its full potential in cancer treatment planning. Technical, ethical, and regulatory challenges must be addressed through continued research, and there needs to be an enhancement in the generalization of deep learning models across diverse patient populations and clinical environments.