Deep Learning Based Dominant Index Lesion Segmentation for MR-guided Radiation Therapy of Prostate Cancer

Understand

Dose escalation radiotherapy allows increased control of prostate cancer (PCa) but requires segmentation of dominant index lesions (DIL), motivating the development of automated methods for fast, accurate, and consistent segmentation of PCa DIL.

• We evaluated five deep-learning networks on apparent diffusion coefficient (ADC) MRI from 500 lesions in 365 patients arising from internal training Dataset 1 (1.5Tesla GE MR with endorectal coil), external ProstateX Dataset 2 (3Tesla Siemens MR), and internal inter-rater Dataset 3 (3Tesla Philips MR).
• The networks include: multiple resolution residually connected network (MRRN) and MRRN regularized in training with deep supervision (MRRN-DS), Unet, Unet++, ResUnet, and fast panoptic segmentation (FPSnet) as well as fast panoptic segmentation with smoothed labels (FPSnet-SL).
• Models were evaluated by volumetric DIL segmentation accuracy using Dice similarity coefficient (DSC) and detection accuracy, as a function of lesion aggressiveness, size, and location (Dataset 1 and 2), and accuracy with respect to two-raters (on Dataset 3).