Deep learning for automated contouring of neurovascular structures on magnetic resonance imaging for prostate cancer patients.

ABSTRACT:

Background and purpose

Manual contouring of neurovascular structures on prostate magnetic resonance imaging (MRI) is labor-intensive and prone to considerable interrater disagreement. Our aim is to contour neurovascular structures automatically on prostate MRI by deep learning (DL) to improve workflow and interrater agreement.

Materials and methods

Segmentation of neurovascular structures was performed on pre-treatment 3.0 T MRI data of 131 prostate cancer patients (training [n = 105] and testing [n = 26]). The neurovascular structures include the penile bulb (PB), corpora cavernosa (CCs), internal pudendal arteries (IPAs), and neurovascular bundles (NVBs). Two DL networks, nnU-Net and DeepMedic, were trained for auto-contouring on prostate MRI and evaluated using volumetric

SUBMITTER: van den Berg I 

PROVIDER: S-EPMC10258498 | biostudies-literature | 2023 Apr

REPOSITORIES: biostudies-literature