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Related Experiment Video

Updated: Sep 23, 2025

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Automatic Liver Tumor Segmentation on Dynamic Contrast Enhanced MRI Using 4D Information: Deep Learning Model Based

Rencheng Zheng, Qidong Wang, Shuangzhi Lv

    IEEE Transactions on Medical Imaging
    |May 16, 2022
    PubMed
    Summary

    This study introduces a 4D deep learning model using 3D convolution and convolutional long short-term memory (C-LSTM) for accurate hepatocellular carcinoma (HCC) segmentation in MRI scans. The model shows improved performance and efficiency in liver tumor segmentation.

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    Area of Science:

    • Medical Imaging Analysis
    • Artificial Intelligence in Medicine
    • Oncology

    Background:

    • Accurate liver tumor segmentation is critical for diagnosing liver cancer and guiding treatment decisions.
    • Hepatocellular carcinoma (HCC) poses a significant clinical challenge requiring precise imaging analysis.
    • Existing segmentation methods may not fully leverage the temporal and spatial information in dynamic contrast-enhanced MRI.

    Purpose of the Study:

    • To develop and evaluate a novel 4-dimensional (4D) deep learning model for precise segmentation of hepatocellular carcinoma (HCC) lesions.
    • To improve the accuracy and efficiency of liver tumor segmentation using dynamic contrast-enhanced (DCE) MRI.
    • To leverage both spatial and temporal features from multi-phase DCE-MRI for enhanced segmentation.

    Main Methods:

    • A 4D deep learning model integrating 3D convolution and convolutional long short-term memory (C-LSTM) was proposed.
    • A 3D Convolutional Neural Network (CNN) module extracted spatial features from individual DCE-MRI phases.
    • A C-LSTM network module processed temporal dynamics across multiple DCE-MRI phases for feature exploitation.

    Main Results:

    • The proposed 4D model achieved a Dice score of 0.825±0.077 and a Hausdorff distance of 12.84±8.14 mm for HCC segmentation.
    • The model demonstrated superior performance compared to 3D U-Net and RA-UNet models in both internal and external test sets.
    • Performance was comparable to the state-of-the-art nnU-Net model, with significantly reduced prediction time.

    Conclusions:

    • The developed 3D convolution and C-LSTM based model accurately segments hepatocellular carcinoma (HCC) lesions.
    • The 4D approach effectively utilizes multi-phase DCE-MRI data for improved liver tumor segmentation.
    • This model offers a promising tool for clinical diagnosis and treatment planning in liver cancer management.