Strategies to improve deep learning-based salivary gland segmentation
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Strategies to improve deep learning‑based salivary gland segmentation Ward van Rooij* , Max Dahele, Hanne Nijhuis, Berend J. Slotman and Wilko F. Verbakel
Abstract Background: Deep learning-based delineation of organs-at-risk for radiotherapy purposes has been investigated to reduce the time-intensiveness and inter-/intra-observer variability associated with manual delineation. We systematically evaluated ways to improve the performance and reliability of deep learning for organ-at-risk segmentation, with the salivary glands as the paradigm. Improving deep learning performance is clinically relevant with applications ranging from the initial contouring process, to on-line adaptive radiotherapy. Methods: Various experiments were designed: increasing the amount of training data (1) with original images, (2) with traditional data augmentation and (3) with domain-specific data augmentation; (4) the influence of data quality was tested by comparing training/testing on clinical versus curated contours, (5) the effect of using several custom cost functions was explored, and (6) patient-specific Hounsfield unit windowing was applied during inference; lastly, (7) the effect of model ensembles was analyzed. Model performance was measured with geometric parameters and model reliability with those parameters’ variance. Results: A positive effect was observed from increasing the (1) training set size, (2/3) data augmentation, (6) patientspecific Hounsfield unit windowing and (7) model ensembles. The effects of the strategies on performance diminished when the base model performance was already ‘high’. The effect of combining all beneficial strategies was an increase in average Sørensen–Dice coefficient of about 4% and 3% and a decrease in standard deviation of about 1% and 1% for the submandibular and parotid gland, respectively. Conclusions: A subset of the strategies that were investigated provided a positive effect on model performance and reliability. The clinical impact of such strategies would be an expected reduction in post-segmentation editing, which facilitates the adoption of deep learning for autonomous automated salivary gland segmentation. Keywords: Artificial intelligence, Deep learning, Salivary glands, Segmentation Background Target volume and organ-at-risk (OAR) delineation are fundamental steps in the radiotherapy treatment planning process. However, they are time and labor intensive and prone to inter and intra-observer variation. Automated deep-learning (DL) based delineation, in particular of OARs, has been investigated to address *Correspondence: [email protected] Department of Radiation Oncology, Cancer Center Amsterdam, Vrije Universiteit Amsterdam, Amsterdam UMC, de Boelelaan 1117, Amsterdam, The Netherlands
the challenges associated with manual delineation [1, 2]. Although results have generally been promising, even for anatomically complex regions like the head-and-neck [3], further improvements are desirable for various reasons, including enabling more reliable
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