• Yongwon Cho, Hyungjoon Cho, Jaemin Shim, Jong-Il Choi, Young-Hoon Kim, Namkug Kim, Yu-Whan Oh, and Sung Ho Hwang.
• Department of Radiology, Korea University Anam Hospital, Seoul,Korea.
• J. Korean Med. Sci. 2022 Sep 19; 37 (36): e271.

BackgroundTo propose fully automatic segmentation of left atrium using active learning with limited dataset in late gadolinium enhancement in cardiac magnetic resonance imaging (LGE-CMRI).MethodsAn 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.ResultsThe 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.ConclusionDeep active learning reduced annotation time and enabled efficient training on limited LGE-CMRI.© 2022 The Korean Academy of Medical Sciences.

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