Efficient Semi-supervised Gross Target Volume of Nasopharyngeal Carcinoma Segmentation via Uncertainty Rectified Pyramid Consistency
By Xiangde Luo, Wenjun Liao, Jieneng Chen, Tao Song, Yinan Chen, Shichuan Zhang, N. Chen, Guotai Wang, Shaoting Zhang. International Conference on Medical Image Computing and Computer-Assisted Intervention, 2020
325 citations recorded by the source corpus.
Gross Target Volume (GTV) segmentation plays an irreplaceable role in radiotherapy planning for Nasopharyngeal Carcinoma (NPC). Despite that Convolutional Neural Networks (CNN) have achieved good performance for this task, they rely on a large set of labeled images for training, which is expensive and time-consuming to acquire. In this paper, we propose a novel framework with Uncertainty Rectified Pyramid Consistency (URPC) regularization for semi-supervised NPC GTV segmentation. Concretely, we extend a backbone segmentation network to produce pyramid predictions at different scales. The pyramid predictions network (PPNet) is supervised by the ground truth of labeled images and a multi-scale consistency loss for unlabeled images, motivated by the fact that prediction at different scales for the same input should be similar and consistent. However, due to the different resolution of these predictions, encouraging them to be consistent at each pixel directly has low robustness and may lose some fine details. To address this problem, we further design a novel uncertainty rectifying module to enable the framework to gradually learn from meaningful and reliable consensual regions at different scales. Experimental results on a dataset with 258 NPC MR images showed that with only 10% or 20% images labeled, our method largely improved the segmentation performance by leveraging the unlabeled images, and it also outperformed five state-of-the-art semi-supervised segmentation methods. Moreover, when only 50% images labeled, URPC achieved an average Dice score of 82.74% that was close to fully supervised learning.
Subfields: Computer Vision and Pattern Recognition, Otorhinolaryngology
View Paper: https://www.semanticscholar.org/paper/36d37d260e515dfed88c6ad0af7276737ffc5ca6
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Citations in the corpus
- Mutual consistency learning for semi-supervised medical image segmentation (2021, methodology): “They also investigated a semi-supervised model to achieve the multi-scale consistency for the gross target volume segmentation (Luo et al., 2021b).”
- Learning with Limited Annotations: A Survey on Deep Semi-Supervised Learning for Medical Image Segmentation (2022, methodology): “Luo et al. [106, 50] calculate uncertainty using pyramid predictions in one forward pass and proposed an multi-level uncertainty rectified pyramid consistency regularization.”
- Semi-Supervised Medical Image Segmentation via Cross Teaching between CNN and Transformer (2021, methodology): “…and its extensions (Tarvainen and Valpola, 2017; Yu et al., 2019; Li et al., 2020; Reiß et al., 2021; You et al., 2021a,b), multi-task learning (Luo et al., 2021a; Kervadec et al., 2019; Chen et al., 2019), confidence learning (Vu et al., 2019), contrastive learning (Peng et al., 2021), and…”