Robust Resolution-Enhanced Prostate Segmentation in Magnetic Resonance and Ultrasound Images through Convolutional Neural Networks

• Main Authors: Oscar J. Pellicer-Valero, Victor Gonzalez-Perez, Juan Luis Casanova Ramón-Borja, Isabel Martín García, María Barrios Benito, Paula Pelechano Gómez, José Rubio-Briones, María José Rupérez, José D. Martín-Guerrero
• Format: Article
• Language: English
• Published: MDPI AG 2021-01-01
• Series: Applied Sciences
• Subjects: MR prostate imaging; US prostate imaging; convolutional neural network; prostate segmentation; neural resolution enhancement
• Online Access: https://www.mdpi.com/2076-3417/11/2/844

Prostate segmentations are required for an ever-increasing number of medical applications, such as image-based lesion detection, fusion-guided biopsy and focal therapies. However, obtaining accurate segmentations is laborious, requires expertise and, even then, the inter-observer variability remains high. In this paper, a robust, accurate and generalizable model for Magnetic Resonance (MR) and three-dimensional (3D) Ultrasound (US) prostate image segmentation is proposed. It uses a densenet-resnet-based Convolutional Neural Network (CNN) combined with techniques such as deep supervision, checkpoint ensembling and Neural Resolution Enhancement. The MR prostate segmentation model was trained with five challenging and heterogeneous MR prostate datasets (and two US datasets), with segmentations from many different experts with varying segmentation criteria. The model achieves a consistently strong performance in all datasets independently (mean Dice Similarity Coefficient -<i>DSC</i>- above 0.91 for all datasets except for one), outperforming the inter-expert variability significantly in MR (mean <i>DSC</i> of 0.9099 vs. 0.8794). When evaluated on the publicly available Promise12 challenge dataset, it attains a similar performance to the best entries. In summary, the model has the potential of having a significant impact on current prostate procedures, undercutting, and even eliminating, the need of manual segmentations through improvements in terms of robustness, generalizability and output resolution.