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Updated: Feb 28, 2026

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
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Efficient Liver and Tumor Segmentation Using a Compact Residual Network and Contrast-Enhanced Pre-Processing

Chun-Ling Lin, Bang-Yu Liu

    IEEE Journal of Biomedical and Health Informatics
    |February 26, 2026
    PubMed
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    This study introduces SegResNet_2335, a lightweight AI model for accurate liver and tumor segmentation in CT scans. It achieves high performance with efficient processing, aiding clinical diagnosis and treatment planning.

    Area of Science:

    • Medical Imaging
    • Artificial Intelligence
    • Computer Vision

    Background:

    • Accurate segmentation of liver and tumors in CT images is crucial for clinical decision-making.
    • Existing methods may lack efficiency or generalizability across different datasets.

    Purpose of the Study:

    • To develop and evaluate SegResNet_2335, a lightweight 3D residual network for volumetric segmentation of liver and tumors in CT images.
    • To assess the framework's performance and cross-dataset generalization capabilities.

    Main Methods:

    • A tailored pre-processing pipeline including voxel spacing resampling, CT window adjustment, CLAHE, and z-score normalization was applied.
    • A lightweight 3D residual network (SegResNet_2335) with 1.5 million parameters was utilized for segmentation.
    • The framework was evaluated on the LiTS and 3D-IRCADb-01 datasets.

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    Main Results:

    • The model achieved high Dice Similarity Coefficients (DSCs): 0.956 for liver and 0.754 for tumor on the LiTS test set.
    • Cross-dataset evaluation on 3D-IRCADb-01 yielded a liver DSC of 0.847 and a tumor DSC of 0.706.
    • Rapid inference time of approximately 1.8 seconds per scan was demonstrated.

    Conclusions:

    • SegResNet_2335 offers strong and consistent liver and tumor segmentation performance.
    • The framework exhibits robust cross-dataset generalization and is suitable for resource-constrained clinical deployment.
    • The publicly available implementation promotes reproducibility in medical image analysis research.