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Multi-view secondary input collaborative deep learning for lung nodule 3D segmentation.

Xianling Dong1, Shiqi Xu1, Yanli Liu1

  • 1Present Address: Department of Biomedical Engineering, Chengde Medical University, Chengde City, Hebei Province, China.

Cancer Imaging : the Official Publication of the International Cancer Imaging Society
|August 3, 2020
PubMed
Summary
This summary is machine-generated.

This study introduces a novel multi-view secondary input residual (MV-SIR) convolutional neural network for 3D lung nodule segmentation. The MV-SIR model achieves high accuracy, comparable to U-net, for segmenting pulmonary nodules in CT scans.

Keywords:
Deep learningMedical imageMulti-viewResidual blockSecondary inputThree-dimensional segmentation

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Computer Vision

Background:

  • Convolutional Neural Networks (CNNs) excel in 2D medical image segmentation.
  • Applying CNNs to 3D medical image segmentation, particularly for lung nodules, presents significant challenges.

Purpose of the Study:

  • To develop an effective deep learning model for accurate 3D lung nodule segmentation.
  • To address the limitations of existing methods in segmenting complex 3D nodule structures.

Main Methods:

  • Proposed a Multi-View Secondary Input Residual (MV-SIR) CNN model for 3D lung nodule segmentation.
  • Utilized the Lung Image Database Consortium and Image Database Resource Initiative (LIDC-IDRI) dataset of chest CT images.
  • Employed multi-view patches (axial, coronal, sagittal) from lung nodule cubes and a novel residual submodel structure.

Main Results:

  • The MV-SIR model achieved a Dice coefficient of 0.926.
  • The model demonstrated an average surface distance of 0.072.
  • Outperformed existing CNN models in 3D pulmonary nodule segmentation.

Conclusions:

  • The MV-SIR model accurately performs 3D segmentation of lung nodules.
  • The model exhibits segmentation accuracy comparable to the U-net model.
  • MV-SIR offers a promising approach for 3D medical image segmentation tasks.