Training Convolutional Networks for Prostate Segmentation With Limited Data

• Published in: IEEE access Vol. 9; pp. 109214 - 109223
• Main Authors: Saunders, Sara L., Leng, Ethan, Spilseth, Benjamin, Wasserman, Neil, Metzger, Gregory J., Bolan, Patrick J.
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 2021; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: 3D U-Net; Artificial neural networks; Automatic prostate segmentation; convolutional neural networks; Critical components; Data models; Datasets; Diagnostic systems; Human performance; Image segmentation; Machine learning; Magnetic resonance imaging; Medical imaging; Performance enhancement; Performance evaluation; Prostate; Three-dimensional displays; Training; Training data; Transfer learning
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2021.3100585

Multi-zonal segmentation is a critical component of computer-aided diagnostic systems for detecting and staging prostate cancer. Previously, convolutional neural networks such as the U-Net have been used to produce fully automatic multi-zonal prostate segmentation on magnetic resonance images (MRIs) with performance comparable to human experts, but these often require large amounts of manually segmented training data to produce acceptable results. For institutions that have limited amounts of labeled MRI exams, it is not clear how much data is needed to train a segmentation model, and which training strategy should be used to maximize the value of the available data. This work compares how the strategies of transfer learning and aggregated training using publicly available external data can improve segmentation performance on internal, site-specific prostate MR images, and evaluates how the performance varies with the amount of internal data used for training. Cross training experiments were performed to show that differences between internal and external data were impactful. Using a standard U-Net architecture, optimizations were performed to select between 2D and 3D variants, and to determine the depth of fine-tuning required for optimal transfer learning. With the optimized architecture, the performance of transfer learning and aggregated training were compared for a range of 5-40 internal datasets. The results show that both strategies consistently improve performance and produced segmentation results that are comparable to that of human experts with approximately 20 site-specific MRI datasets. These findings can help guide the development of site-specific prostate segmentation models for both clinical and research applications.