Medical Image Segmentation Methods Research Articles

Segmenting medical images via explicit–implicit attention aggregation

Medical image segmentation methods using the attention mechanism have achieved great results. However, these attention mechanisms primarily focus on the evaluation of explicit response values, ignoring the important information provided by the implicit energy, i.e., the grayscale change frequency contained in the data. The deficiency in the attention mechanism mentioned above limits its ability to aggregate valid features in medical image segmentation tasks. We propose an efficient network for medical image segmentation, named TransMIS. This network innovatively designs an implicit attention mechanism to interoperate with the explicit attention mechanism and fuses multi-level features with long-range and local dependencies to construct a robust feature representation, effectively improving its segmentation performance. Specifically, TransMIS introduces two new components: (1) A Multi-Scale Compression Self-Attention Mechanism (MSCS) that uses the sparse representation to replace complex feature tensors for matrix multiplication, which can effectively enhance feature representation while reducing the amount of computation. (2) An Explicit–Implicit Channel Mixer Module (CMM) that combines implicit energy quantification and explicit response value evaluation methods, which can bridge the discrepancies between different semantic information and fuse multi-level features effectively. With extensive experiments on multiple benchmark datasets, it shows that TransMIS outperforms current state-of-the-art methods for medical image segmentation. We also conduct rigorous ablation experiments to verify the effectiveness of the proposed components.

Medical Image Segmentation Research Status and Development Trends

As one of the important steps in medical image processing, medical image segmentation plays a pivotal role in clinical surgery and is widely used in application scenarios such as preoperative diagnosis, intraoperative navigation, and postoperative evaluation. In this paper, medical image segmentation technology is studied, and a variety of medical image segmentation methods are categorized and compared in an attempt to explore the development law of medical image segmentation technology. Firstly, the medical image segmentation technology is classified and studied according to its different methods, and this paper mainly researches and organizes the deep learning method for medical image segmentation; secondly, the principle, advantages and disadvantages, and applicable scenarios of each model are analyzed; and lastly, the latest progress of the medical image segmentation technology is objectively described. inherent deficiencies and solutions of the existing techniques, and provides a direction for continued improvement in the future. As one of the important steps in medical image processing, medical image segmentation plays a pivotal role in clinical surgery and is widely used in application scenarios such as preoperative diagnosis, intraoperative navigation, and postoperative evaluation. In this paper, we explore the critical role of medical image segmentation in medical image processing, particularly in clinical surgery and its various applications such as preoperative diagnosis, intraoperative navigation, and postoperative evaluation. Our study focuses on categorizing and comparing different medical image segmentation methods, with a special emphasis on deep learning techniques. We delve into the principles, advantages, disadvantages, and suitable scenarios for each model. Additionally, we objectively present the latest progress in medical image segmentation technology, addressing existing deficiencies and proposing potential solutions. This research provides valuable insights to foster continuous advancements in the field.

Skin lesion image segmentation based on lightweight multi-scale U-shaped network

UNet, and more recently medical image segmentation methods, utilize many parameters and computational quantities to achieve higher performance. However, due to the increasing demand for real-time medical image segmentation tasks, it is important to trade between accuracy rates and computational complexity. To this end, we propose a lightweight multi-scale U-shaped network (LMUNet), a multi-scale inverted residual and an asymmetric atrous spatial pyramid pooling-based network for skin lesion image segmentation. We test LMUNet on multiple medical image segmentation datasets, which show that it reduces the number of parameters by 67X and decreases the computational complexity by 48X while obtaining better performance over the partial lightweight networks.

Deep Hyperspherical Clustering for Skin Lesion Medical Image Segmentation.

Diagnosis of skin lesions based on imaging techniques remains a challenging task because data (knowledge) uncertainty may reduce accuracy and lead to imprecise results. This paper investigates a new deep hyperspherical clustering (DHC) method for skin lesion medical image segmentation by combining deep convolutional neural networks and the theory of belief functions (TBF). The proposed DHC aims to eliminate the dependence on labeled data, improve segmentation performance, and characterize the imprecision caused by data (knowledge) uncertainty. First, the SLIC superpixel algorithm is employed to group the image into multiple meaningful superpixels, aiming to maximize the use of context without destroying the boundary information. Second, an autoencoder network is designed to transform the superpixels' information into potential features. Third, a hypersphere loss is developed to train the autoencoder network. The loss is defined to map the input to a pair of hyperspheres so that the network can perceive tiny differences. Finally, the result is redistributed to characterize the imprecision caused by data (knowledge) uncertainty based on the TBF. The proposed DHC method can well characterize the imprecision between skin lesions and non-lesions, which is particularly important for the medical procedures. A series of experiments on four dermoscopic benchmark datasets demonstrate that the proposed DHC yields better segmentation performance, increasing the accuracy of the predictions while can perceive imprecise regions compared to other typical methods.

Cross-dimensional transfer learning in medical image segmentation with deep learning.

Over the last decade, convolutional neural networks have emerged and advanced the state-of-the-art in various image analysis and computer vision applications. The performance of 2D image classification networks is constantly improving and being trained on databases made of millions of natural images. Conversely, in the field of medical image analysis, the progress is also remarkable but has mainly slowed down due to the relative lack of annotated data and besides, the inherent constraints related to the acquisition process. These limitations are even more pronounced given the volumetry of medical imaging data. In this paper, we introduce an efficient way to transfer the efficiency of a 2D classification network trained on natural images to 2D, 3D uni- and multi-modal medical image segmentation applications. In this direction, we designed novel architectures based on two key principles: weight transfer by embedding a 2D pre-trained encoder into a higher dimensional U-Net, and dimensional transfer by expanding a 2D segmentation network into a higher dimension one. The proposed networks were tested on benchmarks comprising different modalities: MR, CT, and ultrasound images. Our 2D network ranked first on the CAMUS challenge dedicated to echo-cardiographic data segmentation and surpassed the state-of-the-art. Regarding 2D/3D MR and CT abdominal images from the CHAOS challenge, our approach largely outperformed the other 2D-based methods described in the challenge paper on Dice, RAVD, ASSD, and MSSD scores and ranked third on the online evaluation platform. Our 3D network applied to the BraTS 2022 competition also achieved promising results, reaching an average Dice score of 91.69% (91.22%) for the whole tumor, 83.23% (84.77%) for the tumor core and 81.75% (83.88%) for enhanced tumor using the approach based on weight (dimensional) transfer. Experimental and qualitative results illustrate the effectiveness of our methods for multi-dimensional medical image segmentation.

Semi-Supervised Medical Image Segmentation With Voxel Stability and Reliability Constraints.

Semi-supervised learning is becoming an effective solution in medical image segmentation because annotations are costly and tedious to acquire. Methods based on the teacher-student model use consistency regularization and uncertainty estimation and have shown good potential in dealing with limited annotated data. Nevertheless, the existing teacher-student model is seriously limited by the exponential moving average algorithm, which leads to the optimization trap. Moreover, the classic uncertainty estimation method calculates the global uncertainty for images but does not consider local region-level uncertainty, which is unsuitable for medical images with blurry regions. In this paper, the Voxel Stability and Reliability Constraint (VSRC) model is proposed to address these issues. Specifically, the Voxel Stability Constraint (VSC) strategy is introduced to optimize parameters and exchange effective knowledge between two independent initialized models, which can break through the performance bottleneck and avoid model collapse. Moreover, a new uncertainty estimation strategy, the Voxel Reliability Constraint (VRC), is proposed for use in our semi-supervised model to consider the uncertainty at the local region level. We further extend our model to auxiliary tasks and propose a task-level consistency regularization with uncertainty estimation. Extensive experiments on two 3D medical image datasets demonstrate that our method outperforms other state-of-the-art semi-supervised medical image segmentation methods under limited supervision. The source code and pre-trained models of this method are available at https://github.com/zyvcks/JBHI-VSRC.

UcUNet: A lightweight and precise medical image segmentation network based on efficient large kernel U-shaped convolutional module design

In recent years, precise medical image segmentation methods based on the encoder–decoder structure have attracted much attention, but there are still some limitations. They mainly manifest in two aspects: one is that the network structure becomes increasingly complex in pursuit of segmentation accuracy, and the other is the insufficient multiscale information fusion ability between the encoder and decoder. To address these issues, we designed a novel lightweight precise medical image segmentation network called UcUNet, which has not only a very large receptive field and multiscale information fusion ability but also a low parameter count. Specifically, we first designed an efficient U-shaped convolution module that can increase the network depth with fewer parameters and effectively filter out invalid features while fusing shallow and deep features. Furthermore, we ingeniously introduced large-kernel convolution into this module, which significantly improved the network’s receptive field with very few parameters, providing new ideas for the use of large-kernel convolution in lightweight models. In addition, we designed a self-adjusting multiscale feature fusion module based on large-kernel convolution to replace the original simple skip connections, effectively enhancing the network’s multiscale information extraction and fusion ability. Finally, we conducted experiments on multiple datasets, and the results showed that our method can achieve precise medical image segmentation with a low parameter count.

Semi-Supervised Medical Image Segmentation with Co-Distribution Alignment.

Medical image segmentation has made significant progress when a large amount of labeled data are available. However, annotating medical image segmentation datasets is expensive due to the requirement of professional skills. Additionally, classes are often unevenly distributed in medical images, which severely affects the classification performance on minority classes. To address these problems, this paper proposes Co-Distribution Alignment (Co-DA) for semi-supervised medical image segmentation. Specifically, Co-DA aligns marginal predictions on unlabeled data to marginal predictions on labeled data in a class-wise manner with two differently initialized models before using the pseudo-labels generated by one model to supervise the other. Besides, we design an over-expectation cross-entropy loss for filtering the unlabeled pixels to reduce noise in their pseudo-labels. Quantitative and qualitative experiments on three public datasets demonstrate that the proposed approach outperforms existing state-of-the-art semi-supervised medical image segmentation methods on both the 2D CaDIS dataset and the 3D LGE-MRI and ACDC datasets, achieving an mIoU of 0.8515 with only 24% labeled data on CaDIS, and a Dice score of 0.8824 and 0.8773 with only 20% data on LGE-MRI and ACDC, respectively.

Medical image segmentation method based on multi‐scale feature and U‐net network

AbstractIn medical image segmentation (MIS), better segmentation results can be obtained by training the deeper neural network. However, directly building too deep network will cause problems such as gradient disappearance, which will affect the segmentation effect. Therefore, a dilated inception U‐Net (DIU)‐net network is constructed by combining the multi‐scale feature fusion (MSFF) method and the concept of Inception in Google net based on U‐net, and its effectiveness is verified by experiments. The DIU‐net network's training accuracy has been improved in the lung computed tomography (CT) and fundus vascular CT image data sets. And the attenuation of the loss function is relatively stable, with the highest accuracy of 99.6%. In comparison of evaluation indicators, the values of different indicators of DIU‐net in the two data sets are higher than those of the comparison network. The DICE coefficient of DIU‐net in the lung CT image in the experiment is 0.986 on average, which is 0.2% higher than that of ResU‐net. SE value is 0.985, which is 1.9% higher than SegNet. Specificity value is slightly higher than the second segmentation effect. F1 score is 0.985, 0.6% higher than ResU‐net, area under curve value is 0.99, 0.7% higher than FCN‐8 s. In general, the DIU‐net network proposed in the study will not cause gradient disappearance and other problems in the experiment. At the same time, this method also shows high efficiency and has strong feasibility for the actual MIS.

FRBNet: Feedback refinement boundary network for semantic segmentation in breast ultrasound images

Ultrasound breast tumour segmentation is a key step in computer-aided diagnosis and provides an important basis for clinical diagnosis and analysis. Accurate segmentation of breast tumours from ultrasound images is a challenging task due to the characteristics of black shadows, blurred boundaries and uneven colour intensity variations between classes. Currently, most breast tumour segmentation methods focus on extracting multi-scale information and fusing contextual information while underestimating the importance of feature information that can assist in identifying object boundaries in segmentation tasks. The loss of boundary feature information can easily lead to discontinuity or inaccuracy of the target boundary when the network generates the final prediction map. To address this problem, we propose a new feedback refinement boundary network (FRBNet) for accurate segmentation of breast tumour regions in ultrasound images, which mainly consists of a channel calibration module (CCM), boundary detection (BD) module, and feedback refinement module (FRM). Specifically, before fusing low-level feature maps with high-level feature maps, CCM first adopts the method of redistributing feature channel responses to enhance the channels carrying key target information and suppress the noisy channels in low-level feature maps. The BD module then improves the quality of the boundaries in the segmentation results by additionally learning the boundaries of breast tumours to provide accurate boundary feature information for subsequent prediction. The FRM employs a feedback mechanism that complementarily fuses the coarse prediction map and the feature map containing the target boundary feature information, thus achieving the best prediction results before generating the final prediction map. Experimental results on a public ultrasound breast dataset show that our network outperforms other medical image segmentation methods.

Semi-Supervised 3D Medical Image Segmentation Based on Dual-Task Consistent Joint Learning and Task-Level Regularization.

Semi-supervised learning has attracted wide attention from many researchers since its ability to utilize a few data with labels and relatively more data without labels to learn information. Some existing semi-supervised methods for medical image segmentation enforce the regularization of training by implicitly perturbing data or networks to perform the consistency. Most consistency regularization methods focus on data level or network structure level, and rarely of them focus on the task level. It may not directly lead to an improvement in task accuracy. To overcome the problem, this work proposes a semi-supervised dual-task consistent joint learning framework with task-level regularization for 3D medical image segmentation. Two branches are utilized to simultaneously predict the segmented and signed distance maps, and they can learn useful information from each other by constructing a consistency loss function between the two tasks. The segmentation branch learns rich information from both labeled and unlabeled data to strengthen the constraints on the geometric structure of the target. Experimental results on two benchmark datasets show that the proposed method can achieve better performance compared with other state-of-the-art works. It illustrates our method improves segmentation performance by utilizing unlabeled data and consistent regularization.

Medical Image Segmentation Method Based on Improved Unet

This paper is an attempt to apply 3DUnet system to medical image segmentation. The classifying model is built based on actual medical images from MSD Cardiac dataset. The 3DUnet system, which is developed on Convolutional Neural Networks will be of great significance in medical diagnosis and analysis. The paper analyzes and contains details of the 3DUnet system we built and the algorithms we applied. We applied the method of cutting the input images into patches and K-fold cross validation to obtain better results. The final average value of Dice is over 87%. Codes are available: https://github.com/LRXuanxuan/jubilant-chainsaw.

Learning what and where to segment: A new perspective on medical image few-shot segmentation.

Traditional medical image segmentation methods based on deep learning require experts to provide extensive manual delineations for model training. Few-shot learning aims to reduce the dependence on the scale of training data but usually shows poor generalizability to the new target. The trained model tends to favor the training classes rather than being absolutely class-agnostic. In this work, we propose a novel two-branch segmentation network based on unique medical prior knowledge to alleviate the above problem. Specifically, we explicitly introduce a spatial branch to provide the spatial information of the target. In addition, we build a segmentation branch based on the classical encoder-decoder structure in supervised learning and integrate prototype similarity and spatial information as prior knowledge. To achieve effective information integration, we propose an attention-based fusion module (AF) that enables the content interaction of decoder features and prior knowledge. Experiments on an echocardiography dataset and an abdominal MRI dataset show that the proposed model achieves substantial improvements over state-of-the-art methods. Moreover, some results are comparable to those of the fully supervised model. The source code is available at github.com/warmestwind/RAPNet.

Image segmentation technology based on transformer in medical decision‐making system

AbstractDue to the improvement in computing power and the development of computer technology, deep learning has pene‐trated into various fields of the medical industry. Segmenting lesion areas in medical scans can help clinicians make accurate diagnoses. In particular, convolutional neural networks (CNNs) are a dominant tool in computer vision tasks. They can accurately locate and classify lesion areas. However, due to their inherent inductive bias, CNNs may lack an understanding of long‐term dependencies in medical images, leading to less accurate grasping of details in the images. To address this problem, we explored a Transformer‐based solution and studied its feasibility in medical imaging tasks (OstT). First, we performed super‐resolution reconstruction on the original MRI image of osteosarcoma and improved the texture features of the tissue structure to reduce the error caused by the unclear tissue structure in the image during model training. Then, we propose a Transformer‐based method for medical image segmentation. A gated axial attention model is used, which augments existing architectures by introducing an additional control mechanism in the self‐attention module to improve segmentation accuracy. Experiments on real datasets show that our method outper‐forms existing models such as Unet. It can effectively assist doctors in imaging examinations.

Attention-guided residual W-Net for supervised cardiac magnetic resonance imaging segmentation

ObjectiveWith latest developments in deep learning approaches, automated, accurate, fast, and generalized segmentation model for left atrium, left ventricle, right ventricle, and myocardium from MR images is becoming increasingly desirable. In deep learning-based approaches, model generalizability is an essential concern. The strength of an approach that has proven competent for one dataset, and then executed for other without fine-tuning, started to decline. MethodsIn medical image segmentation, U-Net-based architecture with its skip-connection has attained the highest levels of success. This article, therefore presents a novel Attention-guided Residual W-Net (ARW-Net), a deep learning-based segmentation approach with residual links, attention gates at each feature dimensions, and upgraded deep supervision for cardiac MRI (CMRI) segmentation. As the basic building block, the W-Net, a unique methodology for medical image segmentation (centred on U-Net layout), is used. In a W-Net-based structure, ARW-Net suggests residual connections in encoder and attention gates at each feature dimension in decoder. Combination of Dice and Cross-entropy losses is used for model training. ResultsFour CMRI datasets, (i) ACDC 2017, (ii) 2018 ASC, (iii) M&Ms 2020, and (iv) LAScarQS 2022, which are publicly available, have been utilized to examine effectiveness of ARW-Net. It accomplished improved segmentation results and was among the top two for several metrics. ConclusionNumerous comparisons have demonstrated that ARW-Net is profoundly promising, with several segmentation findings delivering innovative state-of-the-art outcomes on four datasets. SignificanceThe results of experiments demonstrate that ARW-Net is able to effectively segment cardiac MR images, as demonstrated by comparisons with existing algorithms.

Detect, Grow, Seg: A weakly supervision method for medical image segmentation based on bounding box

Weakly supervised semantic segmentation based on bounding box has been fully developed in natural scenes, but in medical image scenes it often faces the difficulties of blurred edges, imaging noise, and mutual interference between multiple organs, which makes the methods suitable in natural scenes tend to show poor performance in medical images. In this paper, we propose a weakly supervised segmentation method for medical images based on Detection, Growth, and Segmentation (DGS). For weak labels in the form of bounding boxes, the detector YOLOV5 and GradCAM are introduced to obtain the heatmap representation, while the additional unlabeled data can be also utilized through the detector. The thresholded heatmap is used to generate seed points for adaptive regional growth so that better pseudo labels are obtained. Finally, the pseudo labels are used for supervised training on UNet to obtain end to end segmentation. Sufficient experiments show that, compared with other methods, the pseudo label generation and segmentation model formed by our method achieve the best performance, while the use of unlabeled data by the detector brings significant improvement for segmentation in low-label scenarios, which greatly reduce the time and demand of labeling.

Effective hybrid attention network based on pseudo-color enhancement in ultrasound image segmentation

Ultrasound image segmentation plays a vital role in the early diagnosis of human diseases. It helps diagnose many diseases, such as breast cancer, hemangioma, and other gynecological disorders. However, the intrinsic imaging characteristics of ultrasound images result in substantially lower resolution and clarity than CT, MRI, and other imaging modalities, and they are sensitive to interference from external influences. With its inherent artifacts, blurred lesion boundaries, and uneven intensity distribution, ultrasound images present a challenging task when it comes to segmenting lesion areas accurately. In recent years, convolutional neural networks (CNNs) have achieved remarkable results in medical image segmentation tasks. However, CNNs are limited in capturing the remote dependencies of the input image, leading to degraded accuracy in segmenting ultrasound lesions. In this paper, we developed a deep convolutional neural network that incorporates the pseudo-color enhancement algorithm and hybrid attention modules that enhance the network’s ability to extract fine features and remote modeling capabilities. We propose a novel multi-scale channel attention-based decoder that efficiently uses the feature maps from the encoder as a complement and fuses them with the upsampled feature maps. The hybrid attention combination captures cross-channel interactions efficiently and enhances the context modeling capability, further improving the extraction of coarse and delicate features, and resulting in significant performance improvements. We found that the dice performance improved by 2.54%, 2.47%, 1.39%, 0.99%, and 1.23% on the BUL, BUSI, Hemangioma, BP, and VUI. Results from four public datasets and one self-collected dataset indicate that the proposed method outperforms other medical image segmentation methods for ultrasound image lesion segmentation.

LcmUNet: A Lightweight Network Combining CNN and MLP for Real-Time Medical Image Segmentation.

In recent years, UNet and its improved variants have become the main methods for medical image segmentation. Although these models have achieved excellent results in segmentation accuracy, their large number of network parameters and high computational complexity make it difficult to achieve medical image segmentation in real-time therapy and diagnosis rapidly. To address this problem, we introduce a lightweight medical image segmentation network (LcmUNet) based on CNN and MLP. We designed LcmUNet's structure in terms of model performance, parameters, and computational complexity. The first three layers are convolutional layers, and the last two layers are MLP layers. In the convolution part, we propose an LDA module that combines asymmetric convolution, depth-wise separable convolution, and an attention mechanism to reduce the number of network parameters while maintaining a strong feature-extraction capability. In the MLP part, we propose an LMLP module that helps enhance contextual information while focusing on local information and improves segmentation accuracy while maintaining high inference speed. This network also covers skip connections between the encoder and decoder at various levels. Our network achieves real-time segmentation results accurately in extensive experiments. With only 1.49 million model parameters and without pre-training, LcmUNet demonstrated impressive performance on different datasets. On the ISIC2018 dataset, it achieved an IoU of 85.19%, 92.07% recall, and 92.99% precision. On the BUSI dataset, it achieved an IoU of 63.99%, 79.96% recall, and 76.69% precision. Lastly, on the Kvasir-SEG dataset, LcmUNet achieved an IoU of 81.89%, 88.93% recall, and 91.79% precision.

Review of Medical Image Segmentation Methods

Medical image is one of the key factors in the process of medical diagnosis and treatment. By analyzing the medical image obtained, doctors make judgments on the patient's condition and plan the next treatment process. Medical image segmentation is the process of segmenting areas of interest in medical images according to specific needs, which is a key step in medical image processing and analysis. With the great improvement of computer processing power, how to quickly and effectively segment huge image data and mining valuable information is the research hotspot of segmentation algorithm at present. This paper introduces several traditional medical image segmentation methods such as threshold method and region method, as well as convolutional neural network and Transformer segmentation method based on deep learning technology. Finally, this paper makes a summary and prospects for future development.

Consistency and adversarial semi-supervised learning for medical image segmentation

Medical image segmentation based on deep learning has made enormous progress in recent years. However, the performance of existing methods generally heavily relies on a large amount of labeled data, which are commonly expensive and time-consuming to obtain. To settle above issue, in this paper, a novel semi-supervised medical image segmentation method is proposed, in which the adversarial training mechanism and the collaborative consistency learning strategy are introduced into the mean teacher model. With the adversarial training mechanism, the discriminator can generate confidence maps for unlabeled data, such that more reliable supervised information for the student network is exploited. In the process of adversarial training, we further propose a collaborative consistency learning strategy by which the auxiliary discriminator can assist the primary discriminator in achieving supervised information with higher quality. We extensively evaluate our method on three representative yet challenging medical image segmentation tasks: (1) skin lesion segmentation from dermoscopy images in the International Skin Imaging Collaboration (ISIC) 2017 dataset; (2) optic cup and optic disk (OC/OD) segmentation from fundus images in the Retinal Fundus Glaucoma Challenge (REFUGE) dataset; and (3) tumor segmentation from lower-grade glioma (LGG) tumors images. The experimental results validate the superiority and effectiveness of our proposal when compared with the state-of-the-art semi-supervised medical image segmentation methods.