Efficient Segmentation for Left Atrium With Convolution Neural Network Based on Active Learning in Late Gadolinium Enhancement Magnetic Resonance Imaging

Yongwon Cho, Hyungjoon Cho, Jaemin Shim, Jong Il Choi, Young Hoon Kim, Namkug Kim, Yu Whan Oh, Sung Ho Hwang

Research output: Contribution to journalArticlepeer-review

Abstract

Background: To propose fully automatic segmentation of left atrium using active learning with limited dataset in late gadolinium enhancement in cardiac magnetic resonance imaging (LGE-CMRI). Methods: An active learning framework was developed to segment the left atrium in cardiac LGE-CMRI. Patients (n = 98) with atrial fibrillation from the Korea University Anam Hospital were enrolled. First, 20 cases were delineated for ground truths by two experts and used for training a draft model. Second, the 20 cases from the first step and 50 new cases, corrected in a human-in-the-loop manner after predicting using the draft model, were used to train the next model; all 98 cases (70 cases from the second step and 28 new cases) were trained. An additional 20 LGE-CMRI were evaluated in each step. Results: The Dice coefficients for the three steps were 0.85 ± 0.06, 0.89 ± 0.02, and 0.90 ± 0.02, respectively. The biases (95% confidence interval) in the Bland-Altman plots of each step were 6.36% (−14.90–27.61), 6.21% (−9.62–22.03), and 2.68% (−8.57–13.93). Deep active learning-based annotation times were 218 ± 31 seconds, 36.70 ± 18 seconds, and 36.56 ± 15 seconds, respectively. Conclusion: Deep active learning reduced annotation time and enabled efficient training on limited LGE-CMRI.

Original languageEnglish
Article numbere271
JournalJournal of Korean medical science
Volume37
Issue number36
DOIs
Publication statusPublished - 2022
Externally publishedYes

Keywords

  • Active learning
  • Cardiac image analysis
  • Convolutional neural network
  • Deep learning
  • Human-in-the-loop
  • Magnetic resonance images

ASJC Scopus subject areas

  • Medicine(all)

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