Efficient Segmentation for Left Atrium With Convolution Neural Network Based on Active Learning in Late Gadolinium Enhancement Magnetic Resonance Imaging
10.3346/jkms.2022.37.e271
- Author:
Yongwon CHO
1
;
Hyungjoon CHO
;
Jaemin SHIM
;
Jong-Il CHOI
;
Young-Hoon KIM
;
Namkug KIM
;
Yu-Whan OH
;
Sung Ho HWANG
Author Information
1. Department of Radiology, Korea University Anam Hospital, Seoul, Korea
- Publication Type:Original Article Medical Imaging
- From:Journal of Korean Medical Science
2022;37(36):e271-
- CountryRepublic of Korea
- Language:English
-
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.
