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BACKGROUND: The method of semi-supervised semantic segmentation entails training with a limited number of labeled samples alongside many unlabeled samples, aiming to reduce dependence on pixel-level annotations. Most semi-supervised semantic segmentation methods primarily focus on sample augmentation in spatial dimensions to reduce the shortage of labeled samples. These methods tend to ignore the structural information of objects. In addition, frequency-domain information also supplies another perspective to evaluate information from images, which includes different properties compared to the spatial domain. PURPOSE: In this study, we attempt to answer these two questions: (1) is it helpful to provide structural information of objects in semi-supervised semantic segmentation tasks for medical images? (2) is it more effective to evaluate the segmentation performance in the frequency domain compared to the spatial domain for semi-supervised medical image segmentation? Therefore, we seek to introduce structural and frequency information to improve the performance of semi-supervised semantic segmentation for medical images. METHODS: We present a novel structural tensor loss (STL) to guide feature learning on the spatial domain for semi-supervised semantic segmentation. Specifically, STL utilizes the structural information encoded in the tensors to enforce the consistency of objects across spatial regions, thereby promoting more robust and accurate feature extraction. Additionally, we proposed a frequency-domain alignment loss (FAL) to enable the neural networks to learn frequency-domain information across different augmented samples. It leverages the inherent patterns present in frequency-domain representations to guide the network in capturing and aligning features across diverse augmentation variations, thereby enhancing the model's robustness for the inputting variations. RESULTS: We conduct our experiments on three benchmark datasets, which include MRI (ACDC) for cardiac, CT (Synapse) for abdomen organs, and ultrasound image (BUSI) for breast lesion segmentation. The experimental results demonstrate that our method outperforms state-of-the-art semi-supervised approaches regarding the Dice similarity coefficient. CONCLUSIONS: We find the proposed approach could improve the final performance of the semi-supervised medical image segmentation task. It will help reduce the need for medical image labels. Our code will are available at https://github.com/apple1986/STLFAL.

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BACKGROUND: Deep learning technology has made remarkable progress in pancreatic image segmentation tasks. However, annotating 3D medical images is time-consuming and requires expertise, and existing semi-supervised segmentation methods perform poorly in the segmentation task of organs with blurred edges in enhanced CT such as the pancreas. PURPOSE: To address the challenges of limited labeled data and indistinct boundaries of regions of interest (ROI). METHODS: We propose Edge-Biased Consistency Regularization (EBC-Net). 3D edge detection is employed to construct edge perturbations and integrate edge prior information into limited data, aiding the network in learning from unlabeled data. Additionally, due to the one-sidedness of a single perturbation space, we expand the dual-level perturbation space of both images and features to more efficiently focus the model's attention on the edges of the ROI. Finally, inspired by the clinical habits of doctors, we propose a 3D Anatomical Invariance Extraction Module and Anatomical Attention to capture anatomy-invariant features. RESULTS: Extensive experiments have demonstrated that our method outperforms state-of-the-art methods in semi-supervised pancreas image segmentation. Moreover, it can better preserve the morphology of pancreatic organs and excel at edges region accuracy. CONCLUSIONS: Incorporated with edge prior knowledge, our method mixes disturbances in dual-perturbation space, which shifts the network's attention to the fuzzy edge region using a few labeled samples. These ideas have been verified on the pancreas segmentation dataset.

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The automated segmentation of Intracranial Arteries (IA) in Digital Subtraction Angiography (DSA) plays a crucial role in the quantification of vascular morphology, significantly contributing to computer-assisted stroke research and clinical practice. Current research primarily focuses on the segmentation of single-frame DSA using proprietary datasets. However, these methods face challenges due to the inherent limitation of single-frame DSA, which only partially displays vascular contrast, thereby hindering accurate vascular structure representation. In this work, we introduce DIAS, a dataset specifically developed for IA segmentation in DSA sequences. We establish a comprehensive benchmark for evaluating DIAS, covering full, weak, and semi-supervised segmentation methods. Specifically, we propose the vessel sequence segmentation network, in which the sequence feature extraction module effectively captures spatiotemporal representations of intravascular contrast, achieving intracranial artery segmentation in 2D+Time DSA sequences. For weakly-supervised IA segmentation, we propose a novel scribble learning-based image segmentation framework, which, under the guidance of scribble labels, employs cross pseudo-supervision and consistency regularization to improve the performance of the segmentation network. Furthermore, we introduce the random patch-based self-training framework, aimed at alleviating the performance constraints encountered in IA segmentation due to the limited availability of annotated DSA data. Our extensive experiments on the DIAS dataset demonstrate the effectiveness of these methods as potential baselines for future research and clinical applications. The dataset and code are publicly available at https://doi.org/10.5281/zenodo.11401368 and https://github.com/lseventeen/DIAS.

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BACKGROUND: The issue of using deep learning to obtain accurate gross tumor volume (GTV) and metastatic lymph nodes (MLN) segmentation for nasopharyngeal carcinoma (NPC) on heterogeneous magnetic resonance imaging (MRI) images with limited labeling remains unsolved. METHOD: We collected 918 patients with MRI images from three hospitals to develop and validate models and proposed a semi-supervised framework for the fine delineation of multi-center NPC boundaries by integrating uncertainty-based implicit neural representations named SIMN. The framework utilizes the deep mutual learning approach with CNN and Transformer, incorporating dynamic thresholds. Additionally, domain adaptive algorithms are employed to enhance the performance. RESULTS: SIMN predictions have a high overlap ratio with the ground truth. Under the 20 % labeled cases, for the internal test cohorts, the average DSC in GTV and MLN are 0.7981 and 0.7804, respectively; for external test cohort Wu Zhou Red Cross Hospital, the average DSC in GTV and MLN are 0.7217 and 0.7581, respectively; for external test cohorts First People Hospital of Foshan, the average DSC in GTV and MLN are 0.7004 and 0.7692, respectively. No significant differences are found in DSC, HD95, ASD, and Recall for patients with different clinical categories. Moreover, SIMN outperformed existing classical semi-supervised methods. CONCLUSIONS: SIMN showed a highly accurate GTV and MLN segmentation for NPC on multi-center MRI images under Semi-Supervised Learning (SSL), which can easily transfer to other centers without fine-tuning. It suggests that it has the potential to act as a generalized delineation solution for heterogeneous MRI images with limited labels in clinical deployment.

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Radiomics features have been widely used as novel biomarkers in the diagnosis of various diseases, but whether radiomics features derived from hematoxylin and eosin (H&E) images can evaluate muscle atrophy has not been studied. Therefore, this study aims to establish a new biomarker based on H&E images using radiomics methods to quantitatively analyze H&E images, which is crucial for improving the accuracy of muscle atrophy assessment. Firstly, a weightless muscle atrophy model was established by laying macaques in bed, and H&E images of the shank muscle fibers of the control and bed rest (BR) macaques were collected. Muscle fibers were accurately segmented by designing a semi-supervised segmentation framework based on contrastive learning. Then, 77 radiomics features were extracted from the segmented muscle fibers, and a stable subset of features was selected through the LASSO method. Finally, the correlation between radiomics features and muscle atrophy was analyzed using a support vector machine (SVM) classifier. The semi-supervised segmentation results show that the proposed method had an average Spearman's and intra-class correlation coefficient (ICC) of 88% and 86% compared to manually extracted features, respectively. Radiomics analysis showed that the AUC of the muscle atrophy evaluation model based on H&E images was 96.87%. For individual features, GLSZM_SZE outperformed other features in terms of AUC (91.5%) and ACC (84.4%). In summary, the feature extraction based on the semi-supervised segmentation method is feasible and reliable for subsequent radiomics research. Texture features have greater advantages in evaluating muscle atrophy compared to other features. This study provides important biomarkers for accurate diagnosis of muscle atrophy.

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Semi-supervised segmentation plays an important role in computer vision and medical image analysis and can alleviate the burden of acquiring abundant expert-annotated images. In this paper, we developed a residual-driven semi-supervised segmentation method (termed RDMT) based on the classical mean teacher (MT) framework by introducing a novel model-level residual perturbation and an exponential Dice (eDice) loss. The introduced perturbation was integrated into the exponential moving average (EMA) scheme to enhance the performance of the MT, while the eDice loss was used to improve the detection sensitivity of a given network to object boundaries. We validated the developed method by applying it to segment 3D Left Atrium (LA) and 2D optic cup (OC) from the public LASC and REFUGE datasets based on the V-Net and U-Net, respectively. Extensive experiments demonstrated that the developed method achieved the average Dice score of 0.8776 and 0.7751, when trained on 10% and 20% labeled images, respectively for the LA and OC regions depicted on the LASC and REFUGE datasets. It significantly outperformed the MT and can compete with several existing semi-supervised segmentation methods (i.e., HCMT, UAMT, DTC and SASS).

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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.

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The cross-teaching based on Convolutional Neural Network (CNN) and Transformer has been successful in semi-supervised learning; however, the information interaction between local and global relations ignores the semantic features of the medium scale, and at the same time, the information in the process of feature coding is not fully utilized. To solve these problems, we proposed a new semi-supervised segmentation network. Based on the principle of complementary modeling information of different kernel convolutions, we design a dual CNN cross-supervised network with different kernel sizes under cross-teaching. We introduce global feature contrastive learning and generate contrast samples with the help of dual CNN architecture to make efficient use of coding features. We conducted plenty of experiments on the Automated Cardiac Diagnosis Challenge (ACDC) dataset to evaluate our approach. Our method achieves an average Dice Similarity Coefficient (DSC) of 87.2% and Hausdorff distance ([Formula: see text]) of 6.1 mm on 10% labeled data, which is significantly improved compared with many current popular models. Supervised learning is performed on the labeled data, and dual CNN cross-teaching supervised learning is performed on the unlabeled data. All data would be mapped by the two CNNs to generate features, which are used for contrastive learning to optimize the parameters.

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Deformable multimodal image registration plays a key role in medical image analysis. It remains a challenge to find accurate dense correspondences between multimodal images due to the significant intensity distortion and the large deformation. macJNet is proposed to align the multimodal medical images, which is a weakly-supervised multimodal image deformable registration method using a joint learning framework and multi-sampling cascaded modality independent neighborhood descriptor (macMIND). The joint learning framework consists of a multimodal image registration network and two segmentation networks. The proposed macMIND is a modality-independent image structure descriptor to provide dense correspondence for registration, which incorporates multi-orientation and multi-scale sampling patterns to build self-similarity context. It greatly enhances the representation ability of cross-modal features in the registration network. The semi-supervised segmentation networks generate anatomical labels to provide semantics correspondence for registration, and the registration network helps to improve the performance of multimodal image segmentation by providing the consistency of anatomical labels. 3D CT-MR liver image dataset with 118 samples is built for evaluation, and comprehensive experiments have been conducted to demonstrate that macJNet achieves superior performance over state-of-the-art multi-modality medical image registration methods.

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A network based on complementary consistency training, CC-Net, has been proposed for semi-supervised left atrium image segmentation. CC-Net efficiently utilizes unlabeled data from the perspective of complementary information, addressing the limited ability of existing semi-supervised segmentation algorithms to extract information from unlabeled data. The complementary symmetrical structure of CC-Net includes a main model and two auxiliary models. The complementary consistency is formed by the model-level perturbation between the main model and the auxiliary models, enforcing their consistency. The complementary information obtained by the two auxiliary models helps the main model effectively focus on ambiguous areas, while the enforced consistency between models facilitates the acquisition of low-uncertainty decision boundaries. CC-Net has been validated in two public datasets. Compared to current state-of-the-art algorithms under specific proportions of annotated data, CC-Net demonstrates the best performance in semi-supervised segmentation. Our code is publicly available at https://github.com/Cuthbert-Huang/CC-Net.

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Semi-supervised learning has greatly advanced medical image segmentation since it effectively alleviates the need of acquiring abundant annotations from experts, wherein the mean-teacher model, known as a milestone of perturbed consistency learning, commonly serves as a standard and simple baseline. Inherently, learning from consistency can be regarded as learning from stability under perturbations. Recent improvement leans toward more complex consistency learning frameworks, yet, little attention is paid to the consistency target selection. Considering that the ambiguous regions from unlabeled data contain more informative complementary clues, in this paper, we improve the mean-teacher model to a novel ambiguity-consensus mean-teacher (AC-MT) model. Particularly, we comprehensively introduce and benchmark a family of plug-and-play strategies for ambiguous target selection from the perspectives of entropy, model uncertainty and label noise self-identification, respectively. Then, the estimated ambiguity map is incorporated into the consistency loss to encourage consensus between the two models' predictions in these informative regions. In essence, our AC-MT aims to find out the most worthwhile voxel-wise targets from the unlabeled data, and the model especially learns from the perturbed stability of these informative regions. The proposed methods are extensively evaluated on left atrium segmentation and brain tumor segmentation. Encouragingly, our strategies bring substantial improvement over recent state-of-the-art methods. The ablation study further demonstrates our hypothesis and shows impressive results under various extreme annotation conditions.

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Purpose: A semi-supervised two-step methodology is proposed to obtain a volumetric estimation of COVID-19-related lesions on Computed Tomography (CT) images. Methods: First, damaged tissue was segmented from CT images using a probabilistic active contours approach. Second, lung parenchyma was extracted using a previously trained U-Net. Finally, volumetric estimation of COVID-19 lesions was calculated considering the lung parenchyma masks.Our approach was validated using a publicly available dataset containing 20 CT COVID-19 images previously labeled and manually segmented. Then, it was applied to 295 COVID-19 patients CT scans admitted to an intensive care unit. We compared the lesion estimation between deceased and survived patients for high and low-resolution images. Results: A comparable median Dice similarity coefficient of 0.66 for the 20 validation images was achieved. For the 295 images dataset, results show a significant difference in lesion percentages between deceased and survived patients, with a p-value of 9.1 × 10-4 in low-resolution and 5.1 × 10-5 in high-resolution images. Furthermore, the difference in lesion percentages between high and low-resolution images was 10 % on average. Conclusion: The proposed approach could help estimate the lesion size caused by COVID-19 in CT images and may be considered an alternative to getting a volumetric segmentation for this novel disease without the requirement of large amounts of COVID-19 labeled data to train an artificial intelligence algorithm. The low variation between the estimated percentage of lesions in high and low-resolution CT images suggests that the proposed approach is robust, and it may provide valuable information to differentiate between survived and deceased patients.

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BACKGROUND: Semi-supervised learning is becoming an effective solution for medical image segmentation because of the lack of a large amount of labeled data. PURPOSE: Consistency-based strategy is widely used in semi-supervised learning. However, it is still a challenging problem because of the coupling of CNN-based isomorphic models. In this study, we propose a new semi-supervised medical image segmentation network (DRS-Net) based on a dual-regularization scheme to address this challenge. METHODS: The proposed model consists of a CNN and a multidecoder hybrid Transformer, which adopts two regularization schemes to extract more generalized representations for unlabeled data. Considering the difference in learning paradigm, we introduce the cross-guidance between CNN and hybrid Transformer, which uses the pseudo label output from one model to supervise the other model better to excavate valid representations from unlabeled data. In addition, we use feature-level consistency regularization to effectively improve the feature extraction performance. We apply different perturbations to the feature maps output from the hybrid Transformer encoder and keep an invariance of the predictions to enhance the encoder's representations. RESULTS: We have extensively evaluated our approach on three typical medical image datasets, including CT slices from Spleen, MRI slices from the Heart, and FM Nuclei. We compare DRS-Net with state-of-the-art methods, and experiment results show that DRS-Net performs better on the Spleen dataset, where the dice similarity coefficient increased by about 3.5%. The experimental results on the Heart and Nuclei datasets show that DRS-Net also improves the segmentation effect of the two datasets. CONCLUSIONS: The proposed DRS-Net enhances the segmentation performance of the datasets with three different medical modalities, where the dual-regularization scheme extracts more generalized representations and solves the overfitting problem.

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Due to the difficulty in accessing a large amount of labeled data, semi-supervised learning is becoming an attractive solution in medical image segmentation. To make use of unlabeled data, current popular semi-supervised methods (e.g., temporal ensembling, mean teacher) mainly impose data-level and model-level consistency on unlabeled data. In this paper, we argue that in addition to these strategies, we could further utilize auxiliary tasks and consider task-level consistency to better excavate effective representations from unlabeled data for segmentation. Specifically, we introduce two auxiliary tasks, i.e., a foreground and background reconstruction task for capturing semantic information and a signed distance field (SDF) prediction task for imposing shape constraint, and explore the mutual promotion effect between the two auxiliary and the segmentation tasks based on mean teacher architecture. Moreover, to handle the potential bias of the teacher model caused by annotation scarcity, we develop a tripled-uncertainty guided framework to encourage the three tasks in the student model to learn more reliable knowledge from the teacher. When calculating uncertainty, we propose an uncertainty weighted integration (UWI) strategy for yielding the segmentation predictions of the teacher. In addition, following the advance of unsupervised learning in leveraging the unlabeled data, we also incorporate a contrastive learning based constraint to help the encoders extract more distinct representations to promote the medical image segmentation performance. Extensive experiments on the public 2017 ACDC dataset and the PROMISE12 dataset have demonstrated the effectiveness of our method.

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Typically, a medical image offers spatial information on the anatomy (and pathology) modulated by imaging specific characteristics. Many imaging modalities including Magnetic Resonance Imaging (MRI) and Computed Tomography (CT) can be interpreted in this way. We can venture further and consider that a medical image naturally factors into some spatial factors depicting anatomy and factors that denote the imaging characteristics. Here, we explicitly learn this decomposed (disentangled) representation of imaging data, focusing in particular on cardiac images. We propose Spatial Decomposition Network (SDNet), which factorises 2D medical images into spatial anatomical factors and non-spatial modality factors. We demonstrate that this high-level representation is ideally suited for several medical image analysis tasks, such as semi-supervised segmentation, multi-task segmentation and regression, and image-to-image synthesis. Specifically, we show that our model can match the performance of fully supervised segmentation models, using only a fraction of the labelled images. Critically, we show that our factorised representation also benefits from supervision obtained either when we use auxiliary tasks to train the model in a multi-task setting (e.g. regressing to known cardiac indices), or when aggregating multimodal data from different sources (e.g. pooling together MRI and CT data). To explore the properties of the learned factorisation, we perform latent-space arithmetic and show that we can synthesise CT from MR and vice versa, by swapping the modality factors. We also demonstrate that the factor holding image specific information can be used to predict the input modality with high accuracy. Code will be made available at https://github.com/agis85/anatomy_modality_decomposition.

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Ultrasound (US) imaging has the technical advantages for the functional evaluation of myocardium compared with other imaging modalities. However, it is a challenge of extracting the myocardial tissues from the background due to low quality of US imaging. To better extract the myocardial tissues, this study proposes a semi-supervised segmentation method of fast Superpixels and Neighborhood Patches based Continuous Min-Cut (fSP-CMC). The US image is represented by a graph, which is constructed depending on the features of superpixels and neighborhood patches. A novel similarity measure is defined to capture and enhance the features correlation using Pearson correlation coefficient and Pearson distance. Interactive labels provided by user play a subsidiary role in the semi-supervised segmentation. The continuous graph cut model is solved via a fast minimization algorithm based on augmented Lagrangian and operator splitting. Additionally, Non-Uniform Rational B-Spline (NURBS) curve fitting is used as post-processing to solve the low resolution problem caused by the graph-based method. 200 B-mode US images of left ventricle of the rats were collected in this study. The myocardial tissues were segmented using the proposed fSP-CMC method compared with the method of fast Neighborhood Patches based Continuous Min-Cut (fP-CMC). The results show that the fSP-CMC segmented the myocardial tissues with a higher agreement with the ground truth (GT) provided by medical experts. The mean absolute distance (MAD) and Hausdorff distance (HD) were significantly lower than those values of fP-CMC (p < 0.05), while the Dice was significantly higher (p < 0.05). In conclusion, the proposed fSP-CMC method accurately and effectively segments the myocardiumn in US images. This method has potentials to be a reliable segmentation method and useful for the functional evaluation of myocardium in the future study.

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