Lightweight 3D U-Net for Robust Liver Segmentation in Multi-Institutional Ct Datasets Publisher



Hosseini SM ; Salahshour F ; Sebzari A ; Safaei M ; Ghadiri H
Source: 32nd National and 10th International Iranian Conference on Biomedical Engineering, ICBME 2025 Published:2025

Abstract

A computed tomography (CT) image of the liver and surrounding structures provide detailed cross-sectional images, which highlight anatomical variations and pathological conditions. Using CT and U-Net networks to segment the liver is a well-known method for accurate diagnosis, treatment planning, and surgery. Although, the high computational demands of recent 3D U-Net-based architectures prevent their deployment in resource-constrained environments. A lightweight 3D U-Net optimized for liver segmentation is proposed in this study, maintaining high performance while reducing computational complexity drastically. Several institutional datasets of 250 abdominal CT volumes were compiled from public benchmarks (LiTS, IRCAD) and local clinical sources, encompassing anatomical, pathological, and protocol variations. An isotropic resampling procedure was used to resample, normalize intensity, standardize crops, and augment data on-the-fly. With fewer than two million parameters, the proposed model retains the encoder-decoder and skip-connection designs of conventional 3D U-Nets. An evaluation of a 30 % independent set of tests achieved Dice similarity coefficients of 0.85 ± 0.02, intersect-over-unions of 0.82 ± 0.03, inference times under 0.7 s and GPU memory consumption below 2 GB. The performance was consistent across public and local datasets, highlighting the importance of heterogeneous training data. Even though the proposed model is slightly less accurate than heavy architecture, it delivers near-real-time segmentation with minimal resource consumption, so it can be integrated into clinical workflows, especially in environments where computational resources are limited. © 2025 IEEE.