MDT: semi-supervised medical image segmentation with mixup-decoupling training.

Objective. In the field of medicine, semi-supervised segmentation algorithms hold crucial research significance while also facing substantial challenges, primarily due to the extreme scarcity of expert-level annotated medical image data. However, many existing semi-supervised methods still process labeled and unlabeled data in inconsistent ways, which can lead to knowledge learned from labeled data being discarded to some extent. This not only lacks a variety of perturbations to explore potential robust information in unlabeled data but also ignores the confirmation bias and class imbalance issues in pseudo-labeling methods.Approach. To solve these problems, this paper proposes a semi-supervised medical image segmentation method 'mixup-decoupling training (MDT)' that combines the idea of consistency and pseudo-labeling. Firstly, MDT introduces a new perturbation strategy 'mixup-decoupling' to fully regularize training data. It not only mixes labeled and unlabeled data at the data level but also performs decoupling operations between the output predictions of mixed target data and labeled data at the feature level to obtain strong version predictions of unlabeled data. Then it establishes a dual learning paradigm based on consistency and pseudo-labeling. Secondly, MDT employs a novel categorical entropy filtering approach to pick high-confidence pseudo-labels for unlabeled data, facilitating more refined supervision.Main results. This paper compares MDT with other advanced semi-supervised methods on 2D and 3D datasets separately. A large number of experimental results show that MDT achieves competitive segmentation performance and outperforms other state-of-the-art semi-supervised segmentation methods.Significance. This paper proposes a semi-supervised medical image segmentation method MDT, which greatly reduces the demand for manually labeled data and eases the difficulty of data annotation to a great extent. In addition, MDT not only outperforms many advanced semi-supervised image segmentation methods in quantitative and qualitative experimental results, but also provides a new and developable idea for semi-supervised learning and computer-aided diagnosis technology research.

Semi-supervised medical image segmentation via feature similarity and reliable-region enhancement.

Semantic segmentation is a crucial task in the field of computer vision, and medical image segmentation, as its downstream task, has made significant breakthroughs in recent years. However, the issue of requiring a large number of annotations in medical image segmentation has remained a major challenge. Semi-supervised semantic segmentation has provided a powerful approach to address the annotation problem. Nevertheless, existing semi-supervised semantic segmentation methods in medical images have drawbacks, such as insufficient exploitation of unlabeled data information and inefficient utilization of all pseudo-label information. We introduces a novel segmentation model, the Feature Similarity and Reliable-region Enhancement Network (FSRENet), to overcome these limitations. Firstly, this paper proposes a Feature Similarity Module (FSM), which combines the dense feature prediction ability of true labels for unlabeled images with segmentation features as additional constraints, utilizing the similarity relationship between dense features to constrain segmentation features, and thus fully exploiting the dense feature information of unlabeled data. Additionally, the Reliable-region Enhancement Module (REM) designs a high-confidence network structure by fusing two networks that can learn from each other, forming a triple-network structure. The high-confidence network generates reliable pseudo-labels that further constrain the predictions of the two networks, achieving the goal of enhancing the weight of reliable regions, reducing the noise interference of pseudo-labels, and efficiently utilizing all pseudo-label information. Experimental results on the ACDC and LA datasets demonstrate that the FSRENet model proposed in this paper excels in the task of semi-supervised semantic segmentation of medical images and outperforms the majority of existing methods. Our code is available at: https://github.com/gdghds0/FSRENet-master.