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Cardiac MR segmentation based on sequence propagation by deep learning.

Chao Luo1, Canghong Shi2, Xiaoji Li1

  • 1Chengdu University of Information Technology, Chengdu, Sichuan, China.

Plos One
|April 10, 2020
PubMed
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This study introduces a novel U-Net based method for cardiac magnetic resonance (CMR) segmentation, improving accuracy in noisy images by utilizing 3D sequence information. The enhanced approach achieves superior myocardial segmentation performance compared to existing methods.

Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Cardiology

Background:

  • Accurate myocardial segmentation in cardiac magnetic resonance (CMR) is crucial for diagnosis and pathology analysis.
  • Image noise in CMR significantly hinders manual segmentation accuracy and speed.

Purpose of the Study:

  • To develop an automated method for accurate myocardial segmentation in noisy CMR images.
  • To leverage 3D sequence information within CMR image stacks for improved segmentation.

Main Methods:

  • A U-Net based deep learning architecture was employed for CMR segmentation.
  • The method incorporates sequential information from adjacent slices during training, where each slice's segmentation depends on the previous one.
  • The approach was validated using the ACDC dataset, comprising 1700 2D MRI slices from 100 patients.

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Main Results:

  • The proposed method demonstrated efficient and accurate myocardial segmentation across CMR slices.
  • The model achieved a high average Dice score of 85.02 ± 0.15 on 340 CMR images.
  • Performance significantly surpassed a classical U-Net segmentation method (Dice score = 0.78 ± 0.3).

Conclusions:

  • The U-Net based method effectively utilizes 3D sequence information for robust myocardial segmentation in CMR.
  • This approach offers a significant improvement over existing methods, particularly for noisy cardiac magnetic resonance images.
  • The method enables rapid and efficient segmentation, aiding in clinical diagnosis and quantitative analysis.