Exploiting Clinically Available Delineations for CNN-based Segmentation in Radiotherapy Treatment Planning
This work addresses the data limitation issue for CNN-based segmentation in radiotherapy treatment planning, offering a practical solution for clinicians by leveraging existing clinical data, though it is incremental as it adapts existing methods to a specific domain challenge.
The paper tackled the problem of training convolutional neural networks for medical image segmentation without requiring large numbers of expert-segmented volumes by using clinically available delineations from picture archiving and communication systems, finding that increasing training set size beyond a limited number leads to sharply diminishing returns and that multi-label segmentation prevents negative effects from missing structures.
Convolutional neural networks (CNNs) have been widely and successfully used for medical image segmentation. However, CNNs are typically considered to require large numbers of dedicated expert-segmented training volumes, which may be limiting in practice. This work investigates whether clinically obtained segmentations which are readily available in picture archiving and communication systems (PACS) could provide a possible source of data to train a CNN for segmentation of organs-at-risk (OARs) in radiotherapy treatment planning. In such data, delineations of structures deemed irrelevant to the target clinical use may be lacking. To overcome this issue, we use multi-label instead of multi-class segmentation. We empirically assess how many clinical delineations would be sufficient to train a CNN for the segmentation of OARs and find that increasing the training set size beyond a limited number of images leads to sharply diminishing returns. Moreover, we find that by using multi-label segmentation, missing structures in the reference standard do not have a negative effect on overall segmentation accuracy. These results indicate that segmentations obtained in a clinical workflow can be used to train an accurate OAR segmentation model.