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A 3D image segmentation for lung cancer using V.Net architecture based deep convolutional networks.

Kamel K Mohammed1,2, Aboul Ella Hassanien2,3, Heba M Afify2,4

  • 1Center for Virus Research and Studies, Al-Azhar University, Cairo, Egypt.

Journal of Medical Engineering & Technology
|April 12, 2021
PubMed
Summary

This study presents a V-Net-based deep learning model for accurate 3D lung segmentation in CT scans, crucial for lung cancer detection and diagnosis. The system achieved high performance in segmenting both tumors and lung tissues.

Keywords:
3D lung segmentationTask06_Lung databaseV-Net modeldice score coefficient (DSC)fully convolutional networks (FCNs)

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence in Healthcare
  • Radiology

Background:

  • Accurate lung segmentation in CT scans is vital for lung cancer identification and other diagnostic procedures.
  • Distinguishing tumors from lung parenchyma requires precise segmentation algorithms.
  • Fully convolutional networks (FCNs) offer a promising approach for semantic lung segmentation.

Purpose of the Study:

  • To develop and evaluate a deep learning-based system for accurate 3D lung segmentation in CT images.
  • To improve the segmentation of tumors and lung parenchyma for enhanced cancer diagnosis.
  • To assess the efficacy of a V-Net inspired FCN for this task.

Main Methods:

  • Utilized CT cancer scans from the Task06_Lung database (64 training, 32 testing images).
  • Applied a fully convolutional network (FCN) inspired by V-Net architecture for 3D segmentation.
  • Implemented a system involving data preprocessing, data augmentation, and the V-Net model.

Main Results:

  • Achieved an average Dice Score Coefficient (DSC) of 80% for region of interest (tumor) segmentation.
  • Obtained an average DSC of 98% for surrounding lung tissue segmentation.
  • Demonstrated superior performance compared to previous 3D lung segmentation methods.

Conclusions:

  • The proposed 3D segmentation system provides robust and accurate lung segmentation, essential for tumor estimation.
  • The V-Net based approach effectively segments both lung parenchyma and tumor regions.
  • This method enhances the precision of lung cancer diagnosis through improved CT image analysis.