U-Net Architecture for Prostate Segmentation: The Impact of Loss Function on System Performance

• Published in: Bioengineering (Basel) Vol. 10; no. 4; p. 412
• Main Authors: Montazerolghaem, Maryam, Sun, Yu, Sasso, Giuseppe, Haworth, Annette
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 26.03.2023; MDPI
• Subjects: Accuracy; Bioengineering; Datasets; Deep learning; Entropy; Glands; Histology; Image processing; Image segmentation; loss function; Magnetic resonance imaging; medical imaging; Methods; mp-MRI; Prostate; Prostate cancer; prostate segmentation; Radiation therapy; U-Net; United States; loss function; prostate cancer; prostate segmentation; mp-MRI; U-Net; medical imaging
• ISSN: 2306-5354; 2306-5354
• DOI: 10.3390/bioengineering10040412

Segmentation of the prostate gland from magnetic resonance images is rapidly becoming a standard of care in prostate cancer radiotherapy treatment planning. Automating this process has the potential to improve accuracy and efficiency. However, the performance and accuracy of deep learning models varies depending on the design and optimal tuning of the hyper-parameters. In this study, we examine the effect of loss functions on the performance of deep-learning-based prostate segmentation models. A U-Net model for prostate segmentation using T2-weighted images from a local dataset was trained and performance compared when using nine different loss functions, including: Binary Cross-Entropy (BCE), Intersection over Union (IoU), Dice, BCE and Dice (BCE + Dice), weighted BCE and Dice (W (BCE + Dice)), Focal, Tversky, Focal Tversky, and Surface loss functions. Model outputs were compared using several metrics on a five-fold cross-validation set. Ranking of model performance was found to be dependent on the metric used to measure performance, but in general, W (BCE + Dice) and Focal Tversky performed well for all metrics (whole gland Dice similarity coefficient (DSC): 0.71 and 0.74; 95HD: 6.66 and 7.42; Ravid 0.05 and 0.18, respectively) and Surface loss generally ranked lowest (DSC: 0.40; 95HD: 13.64; Ravid -0.09). When comparing the performance of the models for the mid-gland, apex, and base parts of the prostate gland, the models' performance was lower for the apex and base compared to the mid-gland. In conclusion, we have demonstrated that the performance of a deep learning model for prostate segmentation can be affected by choice of loss function. For prostate segmentation, it would appear that compound loss functions generally outperform singles loss functions such as Surface loss.