Field Of Image Segmentation Research Articles

MCI Net: Mamba- Convolutional lightweight self-attention medical image segmentation network.

With the development of deep learning in the field of medical image segmentation, various network segmentation models have been developed. Currently, the most common network models in medical image segmentation can be roughly categorized into pure convolutional networks, Transformer-based networks, and networks combining convolution and Transformer architectures. However, when dealing with complex variations and irregular shapes in medical images, existing networks face issues such as incomplete information extraction, large model parameter sizes, high computational complexity, and long processing times. In contrast, models with lower parameter counts and complexity can efficiently, quickly, and accurately identify lesion areas, significantly reducing diagnosis time and providing valuable time for subsequent treatments. Therefore, this paper proposes a lightweight network named MCI-Net, with only 5.48 M parameters, a computational complexity of 4.41, and a time complexity of just 0.263. By performing linear modeling on sequences, MCI-Net permanently marks effective features and filters out irrelevant information. It efficiently captures local-global information with a small number of channels, reduces the number of parameters, and utilizes attention calculations with exchange value mapping. This achieves model lightweighting and enables thorough interaction of local-global information within the computation, establishing an overall semantic relationship of local-global information. To verify the effectiveness of the MCI-Net network, we conducted comparative experiments with other advanced representative networks on five public datasets: X-ray, Lung, ISIC-2016, ISIC-2018, and capsule endoscopy and gastrointestinal segmentation. We also performed ablation experiments on the first four datasets. The experimental results outperformed the other compared networks, confirming the effectiveness of MCI-Net. This research provides a valuable reference for achieving lightweight, accurate, and high-performance medical image segmentation network models.

Instance Segmentation XXL-CT Challenge of a Historic Airplane

Instance segmentation of compound objects in XXL-CT imagery poses a unique challenge in non-destructive testing. This complexity arises from the lack of known reference segmentation labels, limited applicable segmentation tools, as well as partially degraded image quality. To asses recent advancements in the field of machine learning-based image segmentation, the ‘Instance Segmentation XXL-CT Challenge of a Historic Airplane’ was conducted. The challenge aimed to explore automatic or interactive instance segmentation methods for an efficient delineation of the different aircraft components, such as screws, rivets, metal sheets or pressure tubes. We report the organization and outcome of this challenge and describe the capabilities and limitations of the submitted segmentation methods.

A multi-scale, multi-task fusion UNet model for accurate breast tumor segmentation

Background and Objective:Breast cancer is the most common cancer type among women worldwide and a leading cause of female death. Accurately interpreting these complex tumors, involving small size and morphology, requires a significant amount of expertise and time. Developing a breast tumor segmentation model to assist clinicians in treatment, therefore, holds great practical significance. Methods:We propose a multi-scale, multi-task model framework named MTF-UNet. Firstly, we differ from the common approach of using different convolution kernel sizes to extract multi-scale features, and instead use the same convolution kernel size with different numbers of convolutions to obtain multi-scale, multi-level features. Additionally, to better integrate features from different levels and sizes, we extract a new multi-branch feature fusion block (ADF). This block differs from using channel and spatial attention to fuse features, but considers fusion weights between various branches. Secondly, we propose to use the number of pixels predicted to be related to tumors and background to assist segmentation, which is different from the conventional approach of using classification tasks to assist segmentation. Results:We conducted extensive experiments on our proprietary DCE-MRI dataset, as well as two public datasets (BUSI and Kvasir-SEG). In the aforementioned datasets, our model achieved excellent MIoU scores of 90.4516%, 89.8408%, and 92.8431% on the respective test sets. Furthermore, our ablation study has demonstrated the efficacy of each component and the effective integration of our auxiliary prediction branch into other models. Conclusion:Through comprehensive experiments and comparisons with other algorithms, the effectiveness, adaptability, and robustness of our proposed method have been demonstrated. We believe that MTF-UNet has great potential for further development in the field of medical image segmentation. The relevant code and data can be found at https://github.com/LCUDai/MTF-UNet.git.

Using Convolutional Neural Networks for Image Semantic Segmentation and Object Detection

Convolutional neural networks are widely used for feature extraction in the fields of object detection and image segmentation. However, traditional CNN models often struggle to ensure accuracy in high noise environments. A study proposes an enhanced CNN model to improve its ability to recognize targets of different scales. This model combines multi-scale perceptual aggregation and feature alignment (MPAFA) mechanisms. This new method effectively combines low-level and high-level features, which helps to better identify objects of different sizes. The experimental results show that the proposed model achieved a segmentation accuracy of 99.6% on the Cityscapes dataset, and maintained an accuracy of 97.3% even with increased noise. Further experiments have shown that the model outperforms existing methods in terms of accuracy and recall. The experimental results show that the model exhibits excellent performance in object detection and segmentation tasks. This study provides a more effective strategy for processing complex images by optimizing network structure and enhancing feature fusion.

Automatic Segmentation Model and Parameter Extraction Algorithm for Lightning Whistlers

AbstractBased on the magnetic field data recorded by the ZH‐1 electromagnetic satellite, we cerat a training set of 1,300 spectrograms containing the dispersion spectrum of lightning whistlers (LW). The Segment Anything Model (SAM) in the field of image segmentation is trained through the training set to obtain a fine‐tuned SAM model that can be used to detect and segment the dispersion spectrum of LW at pixel level. All track regions of LW are effectively separated from other non‐lightning whistlers regions in the spectrograms after being segmented by the model. The segmentation effect is excellent and detection accuracy is 96.89%, which is better than the previous segmentation model for LW based on ground station data. Then we apply the traditional image processing methods to extract the dispersion spectrum of LW one by one, and develop an algorithm to automatically extract the physical parameters of each LW. The root mean square error between the automatically extracted dispersion parameter and the manually extracted dispersion parameter is only 0.1654 s1/2. The model and algorithm studied in this paper are employed to analyze the dispersion of LW received by the ZH‐1 satellite over China. It is found that the whistlers dispersion received by satellites during summer in the northern hemisphere and summer in the southern hemisphere shows opposite trends with receiving latitude. Both trends can be explained by the relationship between the dispersion and the length of propagation paths of LW.

ShiftTransUNet: An Efficient Deep Learning Model for Medical Image Segmentation Using ShiftViT Framework

Deep learning has significantly advanced the field of medical image segmentation. However, the complexity of network structures often leads to high computational demands, limiting their practical efficiency. To enhance the efficiency of image segmentation, this paper introduces an innovative, concise, and lightweight deep learning network. First, to reduce model complexity, we replaced the attention mechanism in the traditional vision transformer (ViT) structure with a shift operation, creating the ShiftViT architecture. This substitution significantly decreased computation and the number of parameters while preserving model performance. Second, to retain and enhance fine-grained features and facilitate more precise information transfer across different layers, we employed a full-scale progressive skip connection strategy. This approach effectively integrates multi-scale feature information, further enhancing model performance. Additionally, to further reduce network complexity, inspired by the independence of probabilities, we opted for depth-wise separable convolution over traditional convolution. This enhances the relative independence between layers. Together, these modifications achieved superior segmentation results on both the Synapse and Automated Cardiac Diagnostic Challenge (ACDC) datasets compared to mainstream models, with substantial advantages in terms of computational efficiency and parameter count. The proposed approach represents an effective solution for medical image applications with limited computational resources and holds great promise for clinical practice.

An Object-Aware Network Embedding Deep Superpixel for Semantic Segmentation of Remote Sensing Images

Semantic segmentation forms the foundation for understanding very high resolution (VHR) remote sensing images, with extensive demand and practical application value. The convolutional neural networks (CNNs), known for their prowess in hierarchical feature representation, have dominated the field of semantic image segmentation. Recently, hierarchical vision transformers such as Swin have also shown excellent performance for semantic segmentation tasks. However, the hierarchical structure enlarges the receptive field to accumulate features and inevitably leads to the blurring of object boundaries. We introduce a novel object-aware network, Embedding deep SuperPixel, for VHR image semantic segmentation called ESPNet, which integrates advanced ConvNeXt and the learnable superpixel algorithm. Specifically, the developed task-oriented superpixel generation module can refine the results of the semantic segmentation branch by preserving object boundaries. This study reveals the capability of utilizing deep convolutional neural networks to accomplish both superpixel generation and semantic segmentation of VHR images within an integrated end-to-end framework. The proposed method achieved mIoU scores of 84.32, 90.13, and 55.73 on the Vaihingen, Potsdam, and LoveDA datasets, respectively. These results indicate that our model surpasses the current advanced methods, thus demonstrating the effectiveness of the proposed scheme.

Learning with Explicit Shape Priors for Medical Image Segmentation.

Medical image segmentation is a fundamental task for medical image analysis and surgical planning. In recent years, UNet-based networks have prevailed in the field of medical image segmentation. However, convolutional neural networks (CNNs) suffer from limited receptive fields, which fail to model the long-range dependency of organs or tumors. Besides, these models are heavily dependent on the training of the final segmentation head. And existing methods can not well address aforementioned limitations simultaneously. Hence, in our work, we proposed a novel shape prior module (SPM), which can explicitly introduce shape priors to promote the segmentation performance of UNet-based models. The explicit shape priors consist of global and local shape priors. The former with coarse shape representations provides networks with capabilities to model global contexts. The latter with finer shape information serves as additional guidance to relieve the heavy dependence on the learnable prototype in the segmentation head. To evaluate the effectiveness of SPM, we conduct experiments on three challenging public datasets. And our proposed model achieves state-of-the-art performance. Furthermore, SPM can serve as a plug-and-play structure into classic CNNs and Transformer-based backbones, facilitating the segmentation task on different datasets. Source codes are available at https://github. com/AlexYouXin/Explicit-Shape-Priors.

TPAFNet: Transformer-Driven Pyramid Attention Fusion Network for 3D Medical Image Segmentation.

The field of 3D medical image segmentation is witnessing a growing trend in the utilization of combined networks that integrate convolutional neural networks and transformers. Nevertheless, prevailing hybrid networks are confronted with limitations in their straightforward serial or parallel combination methods and lack an effective mechanism to fuse channel and spatial feature attention. To address these limitations, we present a robust multi-scale 3D medical image segmentation network, the Transformer-Driven Pyramid Attention Fusion Network, which is denoted as TPAFNet, leveraging a hybrid structure of CNN and transformer. Within this framework, we exploit the characteristics of atrous convolution to extract multi-scale information effectively, thereby enhancing the encoding results of the transformer. Furthermore, we introduce the TPAF block in the encoder to seamlessly fuse channel and spatial feature attention from multi-scale feature inputs. In contrast to conventional skip connections that simply concatenate or add features, our decoder is enriched with a TPAF connection, elevating the integration of feature attention between low-level and high-level features. Additionally, we propose a low-level encoding shortcut from the original input to the decoder output, preserving more original image features and contributing to enhanced results. Finally, the deep supervision is implemented using a novel CNN-based voxel-wise classifier to facilitate better network convergence. Experimental results demonstrate that TPAFNet significantly outperforms other state-of-the-art networks on two public datasets, indicating that our research can effectively improve the accuracy of medical image segmentation, thereby assisting doctors in making more precise diagnoses.

AFC-Unet: Attention-fused full-scale CNN-transformer unet for medical image segmentation

In the field of medical image segmentation, although U-Net has achieved significant achievements, it still exposes some inherent disadvantages when dealing with complex anatomical structures and small targets, such as inaccurate target localization, blurry edges, and insufficient integration of contextual information. To address these challenges, this study proposes the Attention-Fused Full-Scale CNN-Transformer Unet (AFC-Unet), aiming to effectively overcome the limitations of traditional U-Net through multi-scale feature fusion, attention mechanisms, and CNN-Transformer hybrid modules. Specifically, we adopt an encoder–decoder architecture, incorporating full-scale feature block fusion and pyramid sampling modules to enhance the model’s ability to recognize fine to overall structural features by integrating cross-level multi-scale features. We propose the Multi-feature Fusion Attention Gates (MFAG) module, which focuses on and highlights discriminative information of potential lesions and key anatomical boundaries, effectively suppressing irrelevant background interference. We design a module Convolutional Hybrid Attention Transformer (CHAT) that integrates CNN and Transformer to address the shortcomings of traditional single models in handling long-range dependencies and global context understanding. Experimental results on three datasets of different scales demonstrate that the model’s segmentation performance for medical images surpasses state-of-the-art models, showcasing high generalization ability.

Cross-domain additive learning of new knowledge rather than replacement.

In medical clinical scenarios for reasons such as patient privacy, information protection and data migration, when domain adaptation is needed for real scenarios, the source-domain data is often inaccessible and only the pre-trained source model on the source-domain is available. Existing solutions for this type of problem tend to forget the rich task experience previously learned on the source domain after adapting, which means that the model simply overfits the target-domain data when adapting and does not learn robust features that facilitate real task decisions. We address this problem by exploring the particular application of source-free domain adaptation in medical image segmentation and propose a two-stage additive source-free adaptation framework. We generalize the domain-invariant features by constraining the core pathological structure and semantic consistency between different perspectives. And we reduce the segmentation generated by locating and filtering elements that may have errors through Monte-Carlo uncertainty estimation. We conduct comparison experiments with some other methods on a cross-device polyp segmentation and a cross-modal brain tumor segmentation dataset, the results in both the target and source domains verify that the proposed method can effectively solve the domain offset problem and the model retains its dominance on the source domain after learning new knowledge of the target domain.This work provides valuable exploration for achieving additive learning on the target and source domains in the absence of source data and offers new ideas and methods for adaptation research in the field of medical image segmentation.

Image Multi-threshold Segmentation Based on an Ameliorated Harmony Search Optimization Algorithm

Image segmentation is the basis and premise of image processing, though traditional multi-threshold image segmentation methods are simple and effective, they suffer the problems of low accuracy and slow convergence rate. For that reason, this paper introduces the multi-threshold image segmentation scheme by combining the harmony search (HS) optimization algorithm and the maximum between-class variance (Otsu) to solve them. Firstly, to further improve the performance of the basic HS, an ameliorated harmony search (AHS) is put forward by modifying the generation method of the new harmony improvisation and introducing a convergence coefficient. Secondly, the AHS algorithm, which takes the maximum between-class variance as its objective function, namely AHS-Otsu, is applied to image multi-level threshold segmentation. Finally, six test images are selected to verify the multilevel segmentation performance of AHS-Otsu. Peak signal-to-noise ratio (PSNR) and structural similarity index (SSIM) are two commonly used metrics for evaluating the effectiveness of image segmentation, which are both used in this article. Comprehensive experimental results indicate that the AHS-Otsu does not only has fast segmentation processing speed, but also can obtain more accurate segmentation performance than others, which prove the effectiveness and potential of the AHS-Otsu algorithm in the field of image segmentation especially for the multi-threshold.

VSmTrans: A hybrid paradigm integrating self-attention and convolution for 3D medical image segmentation

PurposeVision Transformers recently achieved a competitive performance compared with CNNs due to their excellent capability of learning global representation. However, there are two major challenges when applying them to 3D image segmentation: i) Because of the large size of 3D medical images, comprehensive global information is hard to capture due to the enormous computational costs. ii) Insufficient local inductive bias in Transformers affects the ability to segment detailed features such as ambiguous and subtly defined boundaries. Hence, to apply the Vision Transformer mechanism in the medical image segmentation field, the above challenges need to be overcome adequately. MethodsWe propose a hybrid paradigm, called Variable-Shape Mixed Transformer (VSmTrans), that integrates self-attention and convolution and can enjoy the benefits of free learning of both complex relationships from the self-attention mechanism and the local prior knowledge from convolution. Specifically, we designed a Variable-Shape self-attention mechanism, which can rapidly expand the receptive field without extra computing cost and achieve a good trade-off between global awareness and local details. In addition, the parallel convolution paradigm introduces strong local inductive bias to facilitate the ability to excavate details. Meanwhile, a pair of learnable parameters can automatically adjust the importance of the above two paradigms. Extensive experiments were conducted on two public medical image datasets with different modalities: the AMOS CT dataset and the BraTS2021 MRI dataset. ResultsOur method achieves the best average Dice scores of 88.3 % and 89.7 % on these datasets, which are superior to the previous state-of-the-art Swin Transformer-based and CNN-based architectures. A series of ablation experiments were also conducted to verify the efficiency of the proposed hybrid mechanism and the components and explore the effectiveness of those key parameters in VSmTrans. ConclusionsThe proposed hybrid Transformer-based backbone network for 3D medical image segmentation can tightly integrate self-attention and convolution to exploit the advantages of these two paradigms. The experimental results demonstrate our method's superiority compared to other state-of-the-art methods. The hybrid paradigm seems to be most appropriate to the medical image segmentation field. The ablation experiments also demonstrate that the proposed hybrid mechanism can effectively balance large receptive fields with local inductive biases, resulting in highly accurate segmentation results, especially in capturing details. Our code is available at https://github.com/qingze-bai/VSmTrans.

A Novel Multi-Dimensional Joint Search Method for the Compression of Medical Image Segmentation Models.

Due to the excellent results achieved by transformers in computer vision, more and more scholars have introduced transformers into the field of medical image segmentation. However, the use of transformers will make the model's parameters very large, which occupies a large amount of the computer's resources, making them very time-consuming during training. In order to alleviate this disadvantage, this paper explores a flexible and efficient search strategy that can find the best subnet from a continuous transformer network. The method is based on a learnable and uniform L1 sparsity constraint, which contains factors that reflect the global importance of the continuous search space in different dimensions, while the search process is simple and efficient, containing a single round of training. At the same time, in order to compensate for the loss of accuracy caused by the search, a pixel classification module is introduced into the model to compensate for the loss of accuracy in the model search process. Our experiments show that the model in this paper compresses 30% of the parameters and FLOPs used, while also showing a slight increase in the accuracy of the model on the Automatic Cardiac Diagnosis Challenge (ACDC) dataset.

SFE-TRANSUNET: A TRANSFORMER-BASED U-NET WITH SKIPPED FEATURES ENHANCER FOR MEDICAL IMAGE SEGMENTATION

In the field of medical image segmentation, deep convolutional neural networks have achieved satisfying performance in the past decade or so. However, there are some shortcomings. First, the convolutional neural network model cannot provide good insight into the remote dependencies in the image. Second, medical imaging datasets are typically small, which leads to a much higher risk of overfitting in model training. To address these limitations, we innovatively designed the skipped features enhancer (SFE) to enhance the impact of preserved details. To gain insight into remote dependencies in images, this model (SFE-TransUNet) is based on transformer. Additionally, a different scale convolutional layer (additional information capturer) before and after the Transformer Encoder to fuse the features to retain more information of the original data. In addition, a gate mechanism was introduced in the multi-head self-attention (MHSA), and. Finally, an attention block with residuals. SFE-TransUNet was evaluated on two public medical image segmentation datasets. Experimental results show that it achieves better performance than other related Transformer-based architectures. Code available at https://github.com/xackz/SFE-TransUNet .

A teacher-guided early-learning method for medical image segmentation from noisy labels

The success of current deep learning models depends on a large number of precise labels. However, in the field of medical image segmentation, acquiring precise labels is labor-intensive and time-consuming. Hence, the challenge of achieving a high-performance model via datasets containing noisy labels has attracted significant research interest. Some existing methods are unable to exclude samples containing noisy labels and some methods still have high requirements on datasets. To solve this problem, we propose a noisy label learning method for medical image segmentation using a mixture of high and low quality labels based on the architecture of mean teacher. Firstly, considering the teacher model’s capacity to aggregate all previously learned information following each training step, we propose to leverage a teacher model to correct noisy label adaptively during the training phase. Secondly, to enhance the model’s robustness, we propose to infuse feature perturbations into the student model. This strategy aims to bolster the model’s ability to handle variations in input data and improve its resilience to noisy labels. Finally, we simulate noisy labels by destroying labels in two medical image datasets: the Automated Cardiac Diagnosis Challenge (ACDC) dataset and the 3D Left Atrium (LA) dataset. Experiments show that the proposed method demonstrates considerable effectiveness. With a noisy ratio of 0.8, compared with other methods, the mean Dice score of our proposed method is improved by 2.58% and 0.31% on ACDC and LA datasets, respectively.

IEA-Net: Internal and External Dual-Attention Medical Segmentation Network with High-Performance Convolutional Blocks.

Currently, deep learning is developing rapidly in the field of image segmentation, and medical image segmentation is one of the key applications in this field. Conventional CNN has achieved great success in general medical image segmentation tasks, but it has feature loss in the feature extraction part and lacks the ability to explicitly model remote dependencies, which makes it difficult to adapt to the task of human organ segmentation. Although methods containing attention mechanisms have made good progress in the field of semantic segmentation, most of the current attention mechanisms are limited to a single sample, while the number of samples of human organ images is large, ignoring the correlation between the samples is not conducive to image segmentation. In order to solve these problems, an internal and external dual-attention segmentation network (IEA-Net) is proposed in this paper, and the ICSwR (interleaved convolutional system with residual) module and the IEAM module are designed in this network. The ICSwR contains interleaved convolution and hopping connection, which are used for the initial extraction of the features in the encoder part. The IEAM module (internal and external dual-attention module) consists of the LGGW-SA (local-global Gaussian-weighted self-attention) module and the EA module, which are in a tandem structure. The LGGW-SA module focuses on learning local-global feature correlations within individual samples for efficient feature extraction. Meanwhile, the EA module is designed to capture inter-sample connections, addressing multi-sample complexities. Additionally, skip connections will be incorporated into each IEAM module within both the encoder and decoder to reduce feature loss. We tested our method on the Synapse multi-organ segmentation dataset and the ACDC cardiac segmentation dataset, and the experimental results show that the proposed method achieves better performance than other state-of-the-art methods.

HRDLNet: a semantic segmentation network with high resolution representation for urban street view images

Semantic segmentation of urban street scenes has attracted much attention in the field of autonomous driving, which not only helps vehicles perceive the environment in real time, but also significantly improves the decision-making ability of autonomous driving systems. However, most of the current methods based on Convolutional Neural Network (CNN) mainly use coding the input image to a low resolution and then try to recover the high resolution, which leads to problems such as loss of spatial information, accumulation of errors, and difficulty in dealing with large-scale changes. To address these problems, in this paper, we propose a new semantic segmentation network (HRDLNet) for urban street scene images with high-resolution representation, which improves the accuracy of segmentation by always maintaining a high-resolution representation of the image. Specifically, we propose a feature extraction module (FHR) with high-resolution representation, which efficiently handles multi-scale targets and high-resolution image information by efficiently fusing high-resolution information and multi-scale features. Secondly, we design a multi-scale feature extraction enhancement (MFE) module, which significantly expands the sensory field of the network, thus enhancing the ability to capture correlations between image details and global contextual information. In addition, we introduce a dual-attention mechanism module (CSD), which dynamically adjusts the network to more accurately capture subtle features and rich semantic information in images. We trained and evaluated HRDLNet on the Cityscapes Dataset and the PASCAL VOC 2012 Augmented Dataset, and verified the model’s excellent performance in the field of urban streetscape image segmentation. The unique advantages of our proposed HRDLNet in the field of semantic segmentation of urban streetscapes are also verified by comparing it with the state-of-the-art methods.

Boundary-Aware Gradient Operator Network for Medical Image Segmentation.

Medical image segmentation is a crucial task in computer-aided diagnosis. Although convolutional neural networks (CNNs) have made significant progress in the field of medical image segmentation, the convolution kernels of CNNs are optimized from random initialization without explicitly encoding gradient information, leading to a lack of specificity for certain features, such as blurred boundary features. Furthermore, the frequently applied down-sampling operation also loses the fine structural features in shallow layers. Therefore, we propose a boundary-aware gradient operator network (BG-Net) for medical image segmentation, in which the gradient convolution (GConv) and the boundary-aware mechanism (BAM) modules are developed to simulate image boundary features and the remote dependencies between channels. The GConv module transforms the gradient operator into a convolutional operation that can extract gradient features; it attempts to extract more features such as images boundaries and textures, thereby fully utilizing limited input to capture more features representing boundaries. In addition, the BAM can increase the amount of global contextual information while suppressing invalid information by focusing on feature dependencies and the weight ratios between channels. Thus, the boundary perception ability of BG-Net is improved. Finally, we use a multi-modal fusion mechanism to effectively fuse lightweight gradient convolution and U-shaped branch features into a multilevel feature, enabling global dependencies and low-level spatial details to be effectively captured in a shallower manner. We conduct extensive experiments on eight datasets that broadly cover medical images to evaluate the effectiveness of the proposed BG-Net. The experimental results demonstrate that BG-Net outperforms the state-of-the-art methods, particularly those focused on boundary segmentation.

FCSU-Net: A novel full-scale Cross-dimension Self-attention U-Net with collaborative fusion of multi-scale feature for medical image segmentation

Recently, ViT and CNNs based on encoder–decoder architecture have become the dominant model in the field of medical image segmentation. However, there are some deficiencies for each of them: (1) It is difficult for CNNs to capture the interaction between two locations with consideration of the longer distance. (2) ViT cannot acquire the interaction of local context information and carries high computational complexity. To optimize the above deficiencies, we propose a new network for medical image segmentation, which is called FCSU-Net. FCSU-Net uses the proposed collaborative fusion of multi-scale feature block that enables the network to obtain more abundant and more accurate features. In addition, FCSU-Net fuses full-scale feature information through the FFF (Full-scale Feature Fusion) structure instead of simple skip connections, and establishes long-range dependencies on multiple dimensions through the CS (Cross-dimension Self-attention) mechanism. Meantime, every dimension is complementary to each other. Also, CS mechanism has the advantage of convolutions capturing local contextual weights. Finally, FCSU-Net is validated on several datasets, and the results show that FCSU-Net not only has a relatively small number of parameters, but also has a leading segmentation performance.