Advances in Auto-Segmentation

• Published in: Seminars in radiation oncology Vol. 29; no. 3; pp. 185 - 197
• Main Authors: Cardenas, Carlos E., Yang, Jinzhong, Anderson, Brian M., Court, Laurence E., Brock, Kristy B.
• Format: Journal Article
• Language: English
• Published: United States Elsevier Inc 01.07.2019
• Subjects: Algorithms; Deep Learning; Humans; Image Processing, Computer-Assisted - trends; Neoplasms - diagnostic imaging; Neoplasms - radiotherapy; Neural Networks (Computer); Organs at Risk - diagnostic imaging; Organs at Risk - radiation effects; Radiotherapy, Computer-Assisted - trends; Radiotherapy, Image-Guided - trends; Software
• ISSN: 1053-4296; 1532-9461; 1532-9461
• DOI: 10.1016/j.semradonc.2019.02.001

Manual image segmentation is a time-consuming task routinely performed in radiotherapy to identify each patient's targets and anatomical structures. The efficacy and safety of the radiotherapy plan requires accurate segmentations as these regions of interest are generally used to optimize and assess the quality of the plan. However, reports have shown that this process can be subject to significant inter- and intraobserver variability. Furthermore, the quality of the radiotherapy treatment, and subsequent analyses (ie, radiomics, dosimetric), can be subject to the accuracy of these manual segmentations. Automatic segmentation (or auto-segmentation) of targets and normal tissues is, therefore, preferable as it would address these challenges. Previously, auto-segmentation techniques have been clustered into 3 generations of algorithms, with multiatlas based and hybrid techniques (third generation) being considered the state-of-the-art. More recently, however, the field of medical image segmentation has seen accelerated growth driven by advances in computer vision, particularly through the application of deep learning algorithms, suggesting we have entered the fourth generation of auto-segmentation algorithm development. In this paper, the authors review traditional (nondeep learning) algorithms particularly relevant for applications in radiotherapy. Concepts from deep learning are introduced focusing on convolutional neural networks and fully-convolutional networks which are generally used for segmentation tasks. Furthermore, the authors provide a summary of deep learning auto-segmentation radiotherapy applications reported in the literature. Lastly, considerations for clinical deployment (commissioning and QA) of auto-segmentation software are provided.