Shape-aware Semi-supervised 3D Semantic Segmentation for Medical Images (2007.10732v1)

Abstract: 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 https://github.com/kleinzcy/SASSnet.

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

The paper, "Shape-aware Semi-supervised 3D Semantic Segmentation for Medical Images," introduces a novel approach for 3D semantic segmentation in the context of medical imaging, particularly optimizing the utility of semi-supervised learning. In the domain of medical image analysis, semantic segmentation is crucial for accurately delineating structures such as cells, tissues, or organs from 3D images. However, the substantial cost and effort required for generating pixel-wise labeled data for training deep learning models necessitate an efficient semi-supervised learning approach.

The authors address the limitations of existing semi-supervised segmentation methodologies, which often neglect geometric characteristics of object segments or impose prohibitive alignment requirements through strict shape priors. Their proposition is a robust shape-aware strategy that incorporates geometric constraints without necessitating external alignment processes, thereby leveraging both labeled and a substantial volume of unlabeled data.

Methodological Contributions

1. Multi-task Network Architecture: The core innovation lies in the development of a multi-task deep network capable of concurrent semantic segmentation and signed distance map (SDM) prediction. Utilizing the SDM as a shape representation enforces a global shape constraint while enabling adaptation to objects with varying shapes and poses. This approach emphasizes the importance of capturing the object's global shape effectively as opposed to focusing solely on boundary prediction.
2. Adversarial Loss Integration: A critical component of their segmentation strategy is the implementation of adversarial learning to ensure consistency across the labeled and unlabeled data. By applying an adversarial loss to the SDM predictions, the network is trained to generate shape-aware features evenly distributed across both data subsets, thus bolstering the model's ability to generalize shape features efficiently.
3. Empirical Validation: The method was evaluated on the Atrial Segmentation Challenge dataset. Results demonstrated the network's superior performance compared to state-of-the-art models such as UA-MT and ASDNet, with improved global shape segmentation metrics, highlighting the effectiveness of integrating shape constraints in semi-supervised setups.

The paper's empirical findings show that even with a reduced number of labeled samples, their approach maintains competitive segmentation quality, surpassing traditional methods significantly when supplemented with shape-aware features. This translates into a model that not only achieves higher Dice and Jaccard scores but also better captures the semantic class interiors and produces accurate segmentation boundaries.

Implications and Future Directions

This work provides substantial improvements in the context of medical image segmentation, where constraints related to labeled data are prevalent. By efficiently integrating unlabeled data through shape-aware adversarial learning, it pushes the boundaries of how segmentation models can be trained with minimal supervision. The implications of such advances are profound, especially for real-world medical imaging applications where high-quality labeled datasets are limited.

Moving forward, this methodology could be adapted to other medical imaging modalities or expanded towards multi-class segmentation scenarios. Further research could focus on enhancing the robustness of shape-aware features under varying imaging conditions and exploring the scalability of the proposed adversarial loss across diverse datasets.

In conclusion, the shape-aware semi-supervised strategy offers a promising direction for the development of more effective medical image segmentation models, setting a foundation for future research aimed at overcoming the challenges posed by limited labeled datasets while maintaining high segmentation quality.