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Pancreas segmentation using a dual-input v-mesh network.

Yuan Wang1, Guanzhong Gong2, Deting Kong1

  • 1Business School, Academy of Management Science, Shandong Normal University, Jinan, Shandong 250014, China.

Medical Image Analysis
|February 7, 2021
PubMed
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This summary is machine-generated.

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This study introduces a novel dual-input v-mesh fully convolutional network (FCN) for accurate pancreas segmentation in CT scans. The method enhances contrast and geometric feature extraction, significantly improving segmentation performance.

Area of Science:

  • Medical Imaging
  • Computer Vision
  • Radiology

Background:

  • Pancreas segmentation in CT scans is vital for diagnosing pancreatic diseases.
  • Challenges include low contrast, anatomical variability, and organ elasticity.
  • Existing methods struggle with precise pancreas delineation.

Purpose of the Study:

  • To develop an advanced deep learning model for accurate pancreas segmentation.
  • To improve segmentation by enhancing tissue contrast and capturing geometric information.
  • To address the limitations of current pancreas segmentation techniques.

Main Methods:

  • Proposed a dual-input v-mesh fully convolutional network (FCN).
  • Utilized original CT scans and contrast-enhanced images (via graph-based visual saliency).
Keywords:
Abdominal CT scansDual-inputPancreas segmentationV-mesh FCN

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  • Incorporated an attention mechanism and a spatial transformation and fusion (SF) module.

    • Main Results:

    • The dual-input v-mesh FCN model demonstrated superior performance over baseline methods.
    • Achieved higher Dice similarity coefficient (DSC), positive predictive value (PPV), and sensitivity (SEN).
    • Showed reduced average surface distance (ASD) and Hausdorff distance (HD).

    Conclusions:

    • The proposed dual-input v-mesh FCN is effective for pancreas segmentation in abdominal CT images.
    • The integrated modules are crucial for enhancing segmentation accuracy.
    • This approach offers a promising solution for clinical applications in pancreatic disease diagnosis.