Efficient Segmentation for Left Atrium With Convolution Neural Network Based on Active Learning in Late Gadolinium Enhancement Magnetic Resonance Imagingopen access
- Authors
- Cho, Yongwon; Cho, Hyungjoon; Shim, Jaemin; Choi, Jong-Il; Kim, Young-Hoon; Kim, Namkug; Oh, Yu-Whan; Hwang, Sung Ho
- Issue Date
- Sep-2022
- Publisher
- 대한의학회
- Keywords
- Active Learning; Cardiac Image Analysis; Convolutional Neural Network; Deep Learning; Human-in-the-Loop; Magnetic Resonance Images
- Citation
- Journal of Korean Medical Science, v.37, no.36, pp 1 - 12
- Pages
- 12
- Indexed
- SCIE
SCOPUS
KCI
- Journal Title
- Journal of Korean Medical Science
- Volume
- 37
- Number
- 36
- Start Page
- 1
- End Page
- 12
- URI
- https://scholarworks.korea.ac.kr/kumedicine/handle/2021.sw.kumedicine/61589
- DOI
- 10.3346/jkms.2022.37.e271
- ISSN
- 1011-8934
1598-6357
- 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.
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- Appears in
Collections - 2. Clinical Science > Department of Radiology > 1. Journal Articles
- 2. Clinical Science > Department of Cardiology > 1. Journal Articles
- 4. Research institute > Institute of Human Behavior and Genetics > 1. Journal Articles
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