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KMID : 0191120220370360271
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Journal of Korean Medical Science
2022 Volume.37 No. 36 p.271 ~ p.271
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Efficient Segmentation for Left Atrium With Convolution Neural Network Based on Active Learning in Late Gadolinium Enhancement Magnetic Resonance Imaging
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Cho Yong-Won
Cho Hyung-Joon
Shim Jae-Min
Choi Jong-Il
Kim Young-Hoon
Kim Nam-Kug
Oh Yu-Whan
Hwang Sung-Ho
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Abstract
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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.
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KEYWORD
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Active Learning, Cardiac Image Analysis, Convolutional Neural Network, Deep Learning, Human-in-the-Loop, Magnetic Resonance Images
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