MLC: Multi-level consistency learning for semi-supervised left atrium segmentation

Zhebin Shi, Mingfeng Jiang, Yang Li, Bo Wei, Zefeng Wang, Yongquan Wu, Tao Tan, Guang Yang

Research output: Contribution to journalArticlepeer-review


Atrial fibrillation is the most common type of arrhythmia associated with a high mortality rate. Left atrium segmentation is crucial for the diagnosis and treatment of atrial fibrillation. Accurate left atrium segmentation with limited labeled data is a tricky problem. In this paper, a novel multi-level consistency semi-supervised learning method is proposed for left atrium segmentation from 3D magnetic resonance images. The proposed framework can efficiently utilize limited labeled data and large amounts of unlabeled data by performing consistency predictions under task level, data level, and feature level perturbations. For task consistency, the segmentation results and signed distance maps were used for both segmentation and distance estimation tasks. For data level perturbation, random flips (horizontal or vertical) were introduced for unlabeled data. Moreover, based on virtual adversarial training, we design a multi-layer feature perturbation in the structure of skipping connection. Our method is evaluated on the publicly available Left Atrium Segmentation Challenge dataset version 2018. For the model trained with a label rate of 20%, the evaluation metrics Dice, Jaccard, ASD, and 95HD are 91.69%, 84.71%, 1.43 voxel, and 5.44 voxel, respectively. The experimental results show that the proposed method outperforms other semi-supervised learning methods and even achieves better performance than the fully supervised V-Net.

Original languageEnglish
Article number122903
JournalExpert Systems with Applications
Publication statusPublished - 15 Jun 2024


  • Consistency regularization
  • Left atrium segmentation
  • Multi-level consistency
  • Semi-supervised learning


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