Medical image segmentation using dual-decoder mutual teaching with a mean teacher framework.

Semi-supervised medical image segmentation methods based on mean teacher (MT) framework provide a promising means for addressing the dense prediction problems with limited annotated images and numerous unlabeled images. However, the confirmation bias caused by the distribution difference between labeled and unlabeled data and the parameters-coupling problem of MT prevent the model from further improving the segmentationperformance. To reduce confirmation bias and alleviate the parameter coupling problem in MT framework, a novel data augmentation strategy and a cross exponential moving averaging (crossEMA) architecture are proposed in thiswork. Specifically, a dual swap mixing data augmentation method was first proposed, which exchanges the patches between labeled and unlabeled images twice to decrease the confirmation bias caused by distribution divergency. Subsequently, a novel architecture for both student and teacher networks was designed with structurally identical dual decoders, one of which adopted a dropout operation. Labeled, unlabeled, and mixed images are fed into this MT architecture. For unlabeled data, the pseudo-labels generated by the dual decoders of the teacher network were used to supervise the predictions of the corresponding decoders of the student network. For mixed data, the real labels of the labeled data are mixed with the pseudo-labels of the unlabeled data predicted by the teacher network to form the supervisory information, which is used to constrain the prediction consistency for mixed data between student and teacher networks. To overcome the parameter coupling problem between the student and teacher networks, the encoder parameters of the teacher network were updated using an exponential moving average (EMA) strategy, while its dual decoder parameters were updated using a cross EMA strategy, which means the perturbed decoder parameters of the student network were updated with the non-perturbed decoder parameters of the student network and viceversa. By comparing with several state-of-the-art (SOTA) semi-supervised segmentation methods on four publicly available datasets, we validated that the proposed method outperforms existing models. The Dice similarity coefficient (DSC) and volume similarity (VS) were improved by at least 2.33% and 1.86%, respectively, compared to the corresponding sub-optimal methods. Through multiple ablation experiments, we verified that the proposed dual swap strategy can reduce the distributional differences between unlabeled data and labeled+mixed data. In addition, the cross EMA strategy can avoid early convergence of the student and teachernetworks. The proposed strategies can alleviate the confirmation bias caused by the distribution discrepancy between labeled and unlabeled data in semi-supervised learning, as well as the issue of parameter coupling between the student and teacher networks in the MT architecture, providing therefore a promising approach to semi-supervised medical image segmentation.

Accurate segmentation of medical images is crucial for disease diagnosis and understanding disease changes. Deep learning methods, utilizing encoder-decoder structures, have demonstrated cutting-edge performance in various medical image segmentation tasks. However, the pooling operation in the encoding stage results in feature loss, which makes the network lack the ability to fuse multi-scale information at different levels, hinders its effective perception of multi-scale information, and leads to poor segmentation performance. Drawing inspiration from the U-shaped network, this study introduces a multi-branch feature hybrid attention and adaptive receptive field network (MFHARFNet) for medical image segmentation. Building upon the encoder-decoder framework, we initially devise a multi-branch feature hybrid attention module (MFHAM) to seamlessly integrate feature maps of varying scales, capturing both fine-grained features and coarse-grained semantics across the entire scale. Furthermore, we redesign the skip connection to amalgamate feature information from different branches in the encoder stage and efficiently transmit it to the decoder, providing the decoder with global context feature maps at different levels. Finally, the adaptive receptive field (ARF) module is introduced in the decoder feature reconstruction stage to adapt and focus on related fields, ensuring the model’s adaptation to different segmentation target features, and achieving different weights for the output of different convolution kernels to improve segmentation performance. We comprehensively evaluate our method on medical image segmentation tasks, by using four public datasets across CT and MRI. Remarkably, MFHARFNet method consistently outperforms other state-of-the-art methods, exceeding UNet by 2.1%, 0.9%, 6.6% and 1.0% on Dice on ATLAS, LiTs, BraTs2019 and Spine and intervertebral disc datasets, respectively. In addition, MFHARFNet minimizes network parameters and computational complexity as much as possible. The source codes are in https://github.com/OneHundred99/MFHARFNet.

Wide variety of medical images are degraded by different types of noise such as Impulse noise, Gaussian noise, Poisson or a mixed of all. These noise disruptions typically result from sensor malfunctions, transmission issues, or environmental factors [1]. They provide an essential basis for clinical diagnosis and treatment. Unlike natural photographs, medical images often contain significant signal-related noise during their creation, resulting in lower contrast and more visible noise [3].No single method or procedure is effective for noise removal hence care should be taken before cleaning noisy area of image. Factors such as type of image (CT scan, Ultrasound, MRI etc) and type of noise such as (Gaussian, the impulsive and speckle noise etc) should be identified and based on the type of input, an appropriate method should be selected. For accurate segmentation of medical images through the automatic means,it is necessary to remove the noise from the images. There are many methods such as learning based (traditional methods) and non-learning based methods(using various types neural networks).To measure the denoising accuracy, parameters like Signal-to-Noise Ratio (SNR), Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index Measure (SSIM) are used. This paper discusses the results obtained through the various methods and their comparison in terms of PSNR,SSIM. Removing noise is crucial for accurate segmentation of medical images. These noise-less images are to be treated as pre-processed images for the segmentation task.

Internet of Things (IoT) brings telemedicine a new chance. This enables the specialist to consult the patient’s condition despite the fact that they are in different places. Medical image segmentation is needed for analysis, storage, and protection of medical image in telemedicine. Therefore, a variety of methods have been researched for fast and accurate medical image segmentation. Performing segmentation in various organs, the accurate judgment of the region is needed in medical image. However, the removal of region occurs by the lack of information to determine the region in a small region. In this paper, we researched how to reconstruct segmentation region in a small region in order to improve the segmentation results. We generated predicted segmentation of slices using volume data with linear equation and proposed improvement method for small regions using the predicted segmentation. In order to verify the performance of the proposed method, lung region by chest CT images was segmented. As a result of experiments, volume data segmentation accuracy rose from 0.978 to 0.981 and from 0.281 to 0.187 with a standard deviation improvement confirmed.

Automatic and accurate segmentation of medical images plays an essential role in disease diagnosis and treatment planning. Convolution neural networks have achieved remarkable results in medical image segmentation in the past decade. Meanwhile, deep learning models based on Transformer architecture also succeeded tremendously in this domain. However, due to the ambiguity of the medical image boundary and the high complexity of physical organization structures, implementing effective structure extraction and accurate segmentation remains a problem requiring a solution. In this paper, we propose a novel Dual Encoder Network named DECTNet to alleviate this problem. Specifically, the DECTNet embraces four components, which are a convolution-based encoder, a Transformer-based encoder, a feature fusion decoder, and a deep supervision module. The convolutional structure encoder can extract fine spatial contextual details in images. Meanwhile, the Transformer structure encoder is designed using a hierarchical Swin Transformer architecture to model global contextual information. The novel feature fusion decoder integrates the multi-scale representation from two encoders and selects features that focus on segmentation tasks by channel attention mechanism. Further, a deep supervision module is used to accelerate the convergence of the proposed method. Extensive experiments demonstrate that, compared to the other seven models, the proposed method achieves state-of-the-art results on four segmentation tasks: skin lesion segmentation, polyp segmentation, Covid-19 lesion segmentation, and MRI cardiac segmentation.

Accurate segmentation of medical images plays a very important role in clinical diagnosis so that the segmentation technology for medical images attracts more and more attention. However, most medical images usually suffer from severe intensity inhomogeneity and make accurate segmentation difficult. In this study, the authors propose an efficient and robust active contour model for simultaneous image segmentation and correction. The proposed model not only can accurately segment images with severe intensity inhomogeneity and serious noise but also can eliminate the intensity varying information to get the homogeneous correction images. They first present the level set formulation of the two-phase model, which is then extended to the multi-phase formulation. The split Bregman method is applied to efficiently minimise the proposed energy functionals. The proposed model is tested with lots of synthetic images and medical images with promising results. Experimental results demonstrate that the proposed model can accurately segment and correct the inhomogeneous images with serious noise. Quantitative comparison results of the proposed model and other models illustrate the proposed model is more accurate and more efficient. What's more, the proposed model not only is insensitive to the initial contour, but also is robust to the noise.

Accurate segmentation of medical images is critical for disease diagnosis and treatment planning. However, challenges such as fuzzy boundaries, low contrast, and complex anatomical structures often hinder performance. We propose EGCF‐Net, a novel U‐Net‐based architecture that integrates a hybrid encoder with an edge‐aware graph‐based attention network to address these limitations. The hybrid encoder integrates the Swin transformer and Kronecker convolution to capture global and local contextual dependencies. Additionally, skip connections are enhanced using an edge‐aware graph‐based attention module, which combines graph spatial attention and graph channel attention to dynamically model spatial correlations and edge‐aware contextual affinities. This design leads to edge‐enhanced boundary delineation and improved regional consistency. We evaluate EGCF‐Net on four benchmark datasets (Synapse, Kidney Stone, ISIC 2016, and ISIC 2018), achieving Dice scores of 84.70%, 93.07%, 91.07%, and 88.62%, respectively, surpassing existing state‐of‐the‐art methods. Quantitative and qualitative results further validate the efficacy and robustness of the proposed approach, highlighting its potential for advancing medical image segmentation.

Medical imaging technologies, as essential tools for the precise visualization of internal anatomical structures, play a crucial role in early disease detection and ensuring accurate diagnosis. Recently, semi-supervised learning has become a key strategy in medical image segmentation to reduce reliance on scarce annotated data. However, existing frameworks, such as the Mean Teacher (MT), often suffer from low-quality pseudo-labels and limited robustness due to structural homogeneity and noise amplification in complex medical scenarios. To address these issues, this study presents a novel Dual-Student Mean Teacher Network (DSMT-Net), which enhances performance through a collaborative complementary architecture and pixel-level pseudo-label optimization. First, DSMT-Net combines U-Net and Mamba-UNet as dual students, utilizing the former's local boundary accuracy and the latter's global dependency modeling via a state-space model. Second, a pixel-level pseudo-label enhancement mechanism is introduced, combining pixel-level similarity analysis, adaptive confidence threshold setting, and iterative propagation to improve pseudo-label quality while maintaining structural consistency. Third, a self-supervised contrastive loss is adopted to enforce feature consistency between the dual students, alleviating noise propagation and improving the efficiency of unsupervised learning. Comprehensive evaluations on the ACDC and LA datasets confirm the effectiveness of DSMT-Net, highlighting its substantial capability to lower annotation requirements in medical image segmentation tasks. This provides a robust and scalable framework for semi-supervised learning in medical image segmentation, advancing clinical diagnostic efficiency and accuracy. Our code is available at https://github.com/sunwenlong1/DSMT.git.

TC-Net: A joint learning framework based on CNN and vision transformer for multi-lesion medical images segmentation

The imperative development of point-of-care diagnosis for accurate and rapid medical image segmentation, has become increasingly urgent in recent years. Although some pioneering work has applied complex modules to improve segmentation performance, resulting models are often heavy, which is not practical for the modern clinical setting of point-of-care diagnosis. To address these challenges, we propose UltraNet, a state-of-the-art lightweight model that achieves competitive performance in segmenting multiple parts of medical images with the lowest parameters and computational complexity. To extract a sufficient amount of feature information and replace cumbersome modules, the Shallow Focus Float Block (ShalFoFo) and the Dual-stream Synergy Feature Extraction (DuSem) are respectively proposed at both shallow and deep levels. ShalFoFo is designed to capture finer-grained features containing more pixels, while DuSem is capable of extracting distinct deep semantic features from two different perspectives. By jointly utilizing them, the accuracy and stability of UltraNet segmentation results are enhanced. To evaluate performance, UltraNet's generalization ability was assessed on five datasets with different tasks. Compared to UNet, UltraNet reduces the parameters and computational complexity by 46 times and 26 times, respectively. Experimental results demonstrate that UltraNet achieves a state-of-the-art balance among parameters, computational complexity, and segmentation performance. Codes are available at https://github.com/Ziii1/UltraNet .

Deep learning-based segmentation algorithms for medical image require massive training datasets with accurate annotations, which is costly since it takes much human effort to manually labeling from scratch. Therefore, interactive image segmentation is important and may greatly improve the efficiency and accuracy of medical image labeling. Some interactive segmentation methods (e.g. Deep Extreme Cut and Deepgrow) may improve the labeling through minimal interactive input. However these methods only utilize the initial manually input information, while existing segmentation results (such as annotations produced by nonprofessionals or conventional segmentation algorithms) cannot be utilized. In this paper, an interactive segmentation method is proposed to make use of both existing segmentation results and human interactive information to optimize the segmentation results progressively. In this framework, the user only needs to click on the foreground or background of the target individual on the medical image, the algorithm could adaptively learn the correlation between them, and automatically completes the segmentation of the target. The main contributions of this paper are: (1) We adjusted and applied a convolutional neural network which takes medical image data and user's clicks information as input to achieve more accurate segmentation of medical images. (2) We designed an iterative training strategy to realize the applicability of the model to deal with different number of clicks data input. (3) We designed an algorithm based on false positive and false negative regions to simulate the user's clicks, so as to provide enough training data. By applying the proposed method, users can easily extract the region of interest or modify the segmentation results by multiple clicks. The experimental results of 6 medical image segmentation tasks show that the proposed method achieves more accurate segmentation results by at most five clicks.

Fast and accurate segmentation of medical images is essential for the diagnosis and treatment of diseases. The automatic segmentation technology based on deep learning has achieved encouraging performance in segmentation accuracy. However, the improvement of segmentation accuracy usually requires a larger network structure, which also leads to a decrease in segmentation speed. In this study, we propose a medical image anticipation segmentation net (MIASNet), in order to further improve the segmentation speed under the premise of excellent segmentation accuracy. For 3D medical images, we use the spatial association of the previous frame and the current frame as input data to predict the segmentation results of the next frame. Our approach consists of three lightweight sub-networks, which are used to learn the mapping relationship of the spatial domain. In order to make full use of the generating ability of the deep learning network, we use group convolution to obtain diversified prediction results. On the multimodal brain tumor image segmentation (BraTS) 2020 dataset, MIASNet achieves excellent segmentation accuracy without using the target frame that need to be segmented as the network input. Therefore, our proposed segmentation network can be used in a wider range of real-time medical applications.

DSEU-net: A novel deep supervision SEU-net for medical ultrasound image segmentation

Semisupervised strategy has been utilized to alleviate issues from segmentation applications due to challenges in collecting abundant annotated segmentation masks, which is an essential prerequisite for training high-performance 3D convolutional neural networks (CNNs) . Existing semisupervised segmentation methods are mainly concerned with how to generate the pseudo labels with regularization but not evaluate the quality of the pseudo labels explicitly. To alleviate this problem, we offer a simple yet effective reciprocal learning strategy for semisupervised volumetric medical image segmentation, which generates more reliable pseudo labels for the unannotateddata. Our proposed reciprocal learning is achieved through a pair of networks, one as a teacher network and the other as a student network. The student network learns from pseudo labels generated by the teacher network. In addition, the teacher network autonomously optimizes its parameters based on the reciprocal feedback signals from the student's performance on the annotated images. The efficacy of the proposed method is evaluated on three medical image data sets, including 82 pancreas computed tomography (CT) scans (training/testing: 62/20), 100 left atrium gadolinium-enhanced magnetic resonance (MR) scans (training/testing: 80/20), and 200 breast cancer MR scans (training/testing: 68/132). The comparison methods include mean teacher (MT) model, uncertainty-aware MT (UA-MT) model, shape-aware adversarial network (SASSNet), and transformation-consistent self-ensembling model (TCSM). The evaluation metrics are Dice similarity coefficient (Dice), Jaccard index (Jaccard), 95% Hausdorff distance (95HD), and average surface distance (ASD). The Wilcoxon signed-rank test is used to conduct the statisticalanalyses. By utilizing 20% labeled data and 80% unlabeled data for training, our proposed method achieves an average Dice of 84.77%/90.46%/78.53%, Jaccard of 73.71%/82.67%/69.00%, ASD of 1.58/1.90/0.57, and 95HD of 6.24/5.97/4.34 on pancreas/left atrium/breast data sets, respectively. These results outperform several cutting-edge semisupervised approaches, showing the feasibility of our method for the challenging semisupervised segmentationapplications. The proposed reciprocal learning strategy is a general semisupervised solution and has the potential to be applied for other 3D segmentationtasks.

In endoscopic surgery, accurate segmentation of medical images is indispensable for surgical planning, navigation, and real-time guidance. However, existing segmentation methods often fall short in capturing complex anatomical details and long-range contextual information, which are critical for precise segmentation. To address these challenges, we introduce TR7Net TransUNet-RSU7-Network , a novel hybrid deep learning framework that integrates the strengths of TransUNet and RSU7 architectures. TransUNet excels in global context modeling through its transformer encoder-decoder structure, while RSU7 is renowned for its robust feature extraction capabilities, particularly in handling intricate image features. TR7Net synergizes these two architectures to achieve superior segmentation performance. Extensive experiments on spinal endoscopic datasets demonstrate that TR7Net outperforms both TransUNet and nnUNet regarding segmentation accuracy and robustness in surgical scenarios. This work presents a significant advancement in medical image segmentation for spinal endoscopic surgery, offering a more precise and reliable solution for surgical assistance.