Liver margin segmentation in abdominal CT images using U-Net and Detectron2: annotated dataset for deep learning models.

ABSTRACT: The segmentation of liver margins in computed tomography (CT) images presents significant challenges due to the complex anatomical variability of the liver, with critical implications for medical diagnostics and treatment planning. In this study, we leverage a substantial dataset of over 4,200 abdominal CT images, meticulously annotated by expert radiologists from Taleghani Hospital in Kermanshah, Iran. Now made available to the research community, this dataset serves as a rich resource for enhancing and validating various neural network models. We employed two advanced deep neural network models, U-Net and Detectron2, for liver segmentation tasks. In terms of the Mask Intersection over Union (Mask IoU) metric, U-Net achieved an Mask IoU of 0.903, demonstrating high efficacy in simpler cas

SUBMITTER: Sattari MA 

PROVIDER: S-EPMC11906767 | biostudies-literature | 2025 Mar

REPOSITORIES: biostudies-literature