Shape-aware Semi-supervised 3D Semantic Segmentation for Medical Images

By Shuailin Li, Chuyu Zhang, Xuming He. International Conference on Medical Image Computing and Computer-Assisted Intervention, 2020

506 citations recorded by the source corpus.

Semi-supervised learning has attracted much attention in medical image segmentation due to challenges in acquiring pixel-wise image annotations, which is a crucial step for building high-performance deep learning methods. Most existing semi-supervised segmentation approaches either tend to neglect geometric constraint in object segments, leading to incomplete object coverage, or impose strong shape prior that requires extra alignment. In this work, we propose a novel shapeaware semi-supervised segmentation strategy to leverage abundant unlabeled data and to enforce a geometric shape constraint on the segmentation output. To achieve this, we develop a multi-task deep network that jointly predicts semantic segmentation and signed distance map(SDM) of object surfaces. During training, we introduce an adversarial loss between the predicted SDMs of labeled and unlabeled data so that our network is able to capture shape-aware features more effectively. Experiments on the Atrial Segmentation Challenge dataset show that our method outperforms current state-of-the-art approaches with improved shape estimation, which validates its efficacy. Code is available at this https URL.

Subfields: Computational Mechanics, Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/1a07e5639abed22bc28937e0c043cdb194e0a486

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Citations in the corpus

Recent advances and clinical applications of deep learning in medical image analysis (2021, methodology): “Recent self-supervised learning approaches have shown great potential in improving performance of medical tasks lacking sufficient annotations (Bai et al., 2019; Tao et al., 2020; Li et al., 2020a; Shorfuzzaman and Hossain, 2021; Zhang et al., 2020a).”
Mutual consistency learning for semi-supervised medical image segmentation (2021, methodology): “…the average surface distance (ASD) and the 95% Hausdorﬀ Distance (95HD) for the quantitative evaluation. by UA-MT (Yu et al., 2019), SASSNet (Li et al., 2020b), DTC (Luo et al., 2021a), URPC (Luo et al., 2021a), MC-Net (Wu et al., 2021), and our MC-Net+ model on the Pancreas-CT dataset,…”
Learning with Limited Annotations: A Survey on Deep Semi-Supervised Learning for Medical Image Segmentation (2022, background): “SASS [66] Incorporating signed distance maps for shape regularization 8/72 87.”
Bidirectional Copy-Paste for Semi-Supervised Medical Image Segmentation (2023, unknown): “LA dataset We compare our framework on LA dataset with various competitors: UA-MT [41], SASSNet [12], DTC [16], URPC [17], MC-Net [36] and SS-Net [35].”
Semi-supervised Medical Image Segmentation through Dual-task Consistency (2020, methodology): “Li et al. (2020) introduced a transformation-consistent based semi-supervised segmentation method, which encourages consistent predictions of the network-in-training for the same input under different perturbations.”
SimCVD: Simple Contrastive Voxel-Wise Representation Distillation for Semi-Supervised Medical Image Segmentation (2021, methodology): “In recent years, a wide variety of semi-supervised methods [14], [15], [16], [17], [18], [19], [20], [21], [22], [23], [24], [25] have been designed to tackle these issues, which learn from limited labeled data along with a large amount of unlabeled data, achieving significant improvements in accura”