Deep learning for histopathological segmentation of smooth muscle in the urinary bladder.

ABSTRACT:

Background

Histological assessment of smooth muscle is a critical step particularly in staging malignant tumors in various internal organs including  the urinary bladder. Nonetheless, manual segmentation and classification of muscular tissues by pathologists is often challenging. Therefore, a fully automated and reliable smooth muscle image segmentation system is in high demand.

Methods

To characterize muscle fibers in the urinary bladder, including muscularis mucosa (MM) and muscularis propria (MP), we assessed 277 histological images from surgical specimens, using two well-known deep learning (DL) model groups, one including VGG16, ResNet18, SqueezeNet, and MobileNetV2, considered as a patch-based approach, and the other including U-Net, MA-Net, DeepLabv3 + , and FPN, cons

SUBMITTER: Subramanya SK 

PROVIDER: S-EPMC10349433 | biostudies-literature | 2023 Jul

REPOSITORIES: biostudies-literature