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Deep learning-based liver segmentation for fusion-guided intervention.

Xi Fang1, Sheng Xu2, Bradford J Wood2

  • 1Department of Biomedical Engineering and Center for Biotechnology and Interdisciplinary Studies, Rensselaer Polytechnic Institute, Troy, NY, 12180, USA.

International Journal of Computer Assisted Radiology and Surgery
|April 22, 2020
PubMed
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This study introduces a deep learning liver segmentation method to improve image fusion for CT-guided procedures. The technique enhances visualization for diagnosing and treating liver tumors, aiding interventional guidance.

Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Oncology

Background:

  • Tumors exhibit diverse imaging properties, necessitating multi-modal imaging for comprehensive visualization.
  • Image fusion, often combining CT with MRI or PET, is crucial for CT-guided interventions when tumors are not directly visible.
  • Multi-modality image registration presents significant challenges in medical imaging.

Purpose of the Study:

  • Develop a deep learning-based liver segmentation algorithm to aid image fusion.
  • Utilize segmented liver surfaces to assist in registering interventional CT images with diagnostic modalities.
  • Enhance image guidance for needle placement procedures in liver tumor diagnosis and treatment.

Main Methods:

  • Implemented a deep learning network integrating multi-scale input and output features for context abstraction.
Keywords:
Deep learningImage fusionImage segmentationImage-guided interventions

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  • Employed automatic segmentation results for registering interventional CT with diagnostic images.
  • Applied the segmentation algorithm for surface-based image fusion in liver tumor interventions.

    • Main Results:

    • Achieved high segmentation accuracy (Dice of 96.1%) on 70 CT scans from the LiTS challenge.
    • Demonstrated the effectiveness of the segmentation algorithm for surface-based image fusion in clinical cases.
    • Validated the method's utility in guiding liver tumor interventions.

    Conclusions:

    • Deep learning-based image segmentation provides valuable assistance for image fusion in interventional guidance.
    • The developed technique shows promise for improving accuracy and outcomes in liver tumor procedures.
    • Potential for broader applications in medical image analysis and interventional radiology.