Domain generalization for prostate segmentation in transrectal ultrasound images: A multi-center study.

ABSTRACT: Prostate biopsy and image-guided treatment procedures are often performed under the guidance of ultrasound fused with magnetic resonance images (MRI). Accurate image fusion relies on accurate segmentation of the prostate on ultrasound images. Yet, the reduced signal-to-noise ratio and artifacts (e.g., speckle and shadowing) in ultrasound images limit the performance of automated prostate segmentation techniques and generalizing these methods to new image domains is inherently difficult. In this study, we address these challenges by introducing a novel 2.5D deep neural network for prostate segmentation on ultrasound images. Our approach addresses the limitations of transfer learning and finetuning methods (i.e., drop in performance on the original training data when the model weights are up

SUBMITTER: Vesal S 

PROVIDER: S-EPMC10161676 | biostudies-literature | 2022 Nov

REPOSITORIES: biostudies-literature