Deep learning, especially convolutional neural networks (CNNs) and Transformer architectures, have become the focus of extensive research in medical image segmentation, achieving impressive results. However, CNNs come with inductive biases that limit their effectiveness in more complex, varied segmentation scenarios. Conversely, while Transformer-based methods excel at capturing global and long-range semantic details, they suffer from high computational demands. In this study, we propose CSWin-UNet, a novel U-shaped segmentation method that incorporates the CSWin self-attention mechanism into the UNet to facilitate horizontal and vertical stripes self-attention. This method significantly enhances both computational efficiency and receptive field interactions. Additionally, our innovative decoder utilizes a content-aware reassembly operator that strategically reassembles features, guided by predicted kernels, for precise image resolution restoration. Our extensive empirical evaluations on diverse datasets, including synapse multi-organ CT, cardiac MRI, and skin lesions, demonstrate that CSWin-UNet maintains low model complexity while delivering high segmentation accuracy. Codes are available at https://github.com/eatbeanss/CSWin-UNet.
UNet with Self-Adaptive Mamba-Like Attention and Causal-Resonance Learning for Medical Image Segmentation
Faculty of Computing and IT (FCIT)（计算与信息技术学院）；Sohar University（索哈尔大学）；Department of Intelligent Systems（智能系统系）；KTH Royal Institute of Technology（皇家理工学院）；College of Computer and Information Sciences（计算机与信息科学学院）；Imam Mohammad Ibn Saud Islamic University (IMSIU)（伊玛目穆罕默德·本·沙特伊斯兰大学）；University of Auckland（奥克兰大学）；Department of Computer Science, Faculty of Science（科学学院计算机科学系）；Northern Border University（北方边境大学）
TSUBF-Net: Trans-Spatial UNet-like Network with Bi-direction Fusion for Segmentation of Adenoid Hypertrophy in CT
Shenzhen University General Hospital（深圳大学总医院）；College of Mechatronics and Engineering, Shenzhen University（深圳大学机电与工程学院）；College of Electronics and Information Engineering, Shenzhen University（深圳大学电子与信息工程学院）；Department of Electronic Engineering, The Chinese University of Hong Kong（香港中文大学电子工程系）；Research Center of Medical Plasma Technology, Shenzhen University（深圳大学医用等离子体技术研究中心）；Shenzhen Lihuy Medical Tech. co. Ltd.（深圳利护医疗科技有限公司）
Isfahan University of Technology（伊斯法罕理工大学）；McMaster University（麦克马斯特大学）；Seattle University（西雅图大学）
Recently, many attempts have been made to construct a transformer base U-shaped architecture, and new methods have been proposed that outperformed CNN-based rivals. However, serious problems such as blockiness and cropped edges in predicted masks remain because of transformers' patch partitioning operations. In this work, we propose a new U-shaped architecture for medical image segmentation with the help of the newly introduced focal modulation mechanism. The proposed architecture has asymmetric depths for the encoder and decoder. Due to the ability of the focal module to aggregate local and g…
An Effective UNet Using Feature Interaction and Fusion for Organ Segmentation in Medical Image
National Key Laboratory of Fundamental Science on Synthetic Vision, College of Computer Science, Sichuan University（合成视觉基础科学国家重点实验室，计算机科学学院，四川大学）；College of Computer Science, Sichuan University（计算机科学学院，四川大学）；College of Software Engineering, Sichuan University（软件工程学院，四川大学）
本文提出含通道空间交互、通道注意力和多级融合三模块的 U 形网络，以增强预训练特征利用，并在多器官与心脏分割基准上取得最优精度且计算开销适中。
Technische Universität München（慕尼黑工业大学）；Fudan University（复旦大学）；Huawei Technologies（华为技术有限公司）
In the past few years, convolutional neural networks (CNNs) have achieved milestones in medical image analysis. Especially, the deep neural networks based on U-shaped architecture and skip-connections have been widely applied in a variety of medical image tasks. However, although CNN has achieved excellent performance, it cannot learn global and long-range semantic information interaction well due to the locality of the convolution operation. In this paper, we propose Swin-Unet, which is an Unet-like pure Transformer for medical image segmentation. The tokenized image patches are fed into the …
WiTUnet: A U-Shaped Architecture Integrating CNN and Transformer for Improved Feature Alignment and Local Information Fusion
College of Computer Science and Cyber Security, Chengdu University of Technology（成都理工大学计算机与网络安全学院）；College of Geophysics, Chengdu University of Technology（成都理工大学地球物理学院）；College of Mechanical and Vehicle Engineering, Changchun University（长春大学机械与车辆工程学院）
Tianjin University（天津大学）；Institute of High Performance Computing, A*STAR（A*STAR高性能计算研究所）；Imperial College London（帝国理工学院）；The Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））；The Hong Kong University of Science and Technology（香港科技大学）
In recent years, Denoising Diffusion Models have demonstrated remarkable success in generating semantically valuable pixel-wise representations for image generative modeling. In this study, we propose a novel end-to-end framework, called Diff-UNet, for medical volumetric segmentation. Our approach integrates the diffusion model into a standard U-shaped architecture to extract semantic information from the input volume effectively, resulting in excellent pixel-level representations for medical volumetric segmentation. To enhance the robustness of the diffusion model's prediction results, we als…
Bangladesh University of Engineering and Technology（孟加拉国工程技术大学）；Princeton University（普林斯顿大学）
Automatic lung lesions segmentation of chest CT scans is considered a pivotal stage towards accurate diagnosis and severity measurement of COVID-19. Traditional U-shaped encoder-decoder architecture and its variants suffer from diminutions of contextual information in pooling/upsampling operations with increased semantic gaps among encoded and decoded feature maps as well as instigate vanishing gradient problems for its sequential gradient propagation that result in sub-optimal performance. Moreover, operating with 3D CT-volume poses further limitations due to the exponential increase of compu…
Fujian Agriculture and Forestry University（福建农林大学）；Digital Fujian Research Institute of Big Data for Agriculture and Forestry（数字福建农林大数据研究院）
Accurate segmentation of the blood vessels of the retina is an important step in clinical diagnosis of ophthalmic diseases. Many deep learning frameworks have come up for retinal blood vessels segmentation tasks. However, the complex vascular structure and uncertain pathological features make the blood vessel segmentation still very challenging. A novel U-shaped network named Multi-module Concatenation which is based on Atrous convolution and multi-kernel pooling is put forward to retinal vessels segmentation in this paper. The proposed network structure retains three layers the essential stru…
Budapest University of Technology and Economics（布达佩斯技术与经济大学）；Hospital of the Peoples Liberation Army Joint Logistics Support Force（中国人民解放军联勤保障部队医院）；Jiangxi Normal University（江西师范大学）；Shenyang Institute of Computing Technology, Chinese Academy of Science（中国科学院沈阳计算技术研究所）
Retinal vessel segmentation is a vital step for the diagnosis of many early eye-related diseases. In this work, we propose a new deep learning model, namely Channel Attention Residual U-Net (CAR-UNet), to accurately segment retinal vascular and non-vascular pixels. In this model, we introduced a novel Modified Efficient Channel Attention (MECA) to enhance the discriminative ability of the network by considering the interdependence between feature maps. On the one hand, we apply MECA to the "skip connections" in the traditional U-shaped networks, instead of simply copying the feature maps of th…
Fully Convolutional Neural Networks (FCNNs) with contracting and expanding paths have shown prominence for the majority of medical image segmentation applications since the past decade. In FCNNs, the encoder plays an integral role by learning both global and local features and contextual representations which can be utilized for semantic output prediction by the decoder. Despite their success, the locality of convolutional layers in FCNNs, limits the capability of learning long-range spatial dependencies. Inspired by the recent success of transformers for Natural Language Processing (NLP) in l…
A lightweight Convolutional Neural Network based on U shape structure and Attention Mechanism for Anterior Mediastinum Segmentation
University of Saskatchewan（萨斯喀彻温大学）；Chonnam National University Hwasun Hospital（全南大学和顺医院）；Tarbiat Modares University（塔比亚特·莫达雷斯大学）；National Institute of Technology, Trichy（特里吉国家理工学院）；Jeonbuk National University Medical School（全北国立大学医学院）
Semantic segmentation of brain tumors is a fundamental medical image analysis task involving multiple MRI imaging modalities that can assist clinicians in diagnosing the patient and successively studying the progression of the malignant entity. In recent years, Fully Convolutional Neural Networks (FCNNs) approaches have become the de facto standard for 3D medical image segmentation. The popular "U-shaped" network architecture has achieved state-of-the-art performance benchmarks on different 2D and 3D semantic segmentation tasks and across various imaging modalities. However, due to the limited…
Though U-Net has achieved tremendous success in medical image segmentation tasks, it lacks the ability to explicitly model long-range dependencies. Therefore, Vision Transformers have emerged as alternative segmentation structures recently, for their innate ability of capturing long-range correlations through Self-Attention (SA). However, Transformers usually rely on large-scale pre-training and have high computational complexity. Furthermore, SA can only model self-affinities within a single sample, ignoring the potential correlations of the overall dataset. To address these problems, we prop…
Google Research（谷歌研究院）；University of Texas at Austin（德克萨斯大学奥斯汀分校）
Recent progress on Transformers and multi-layer perceptron (MLP) models provide new network architectural designs for computer vision tasks. Although these models proved to be effective in many vision tasks such as image recognition, there remain challenges in adapting them for low-level vision. The inflexibility to support high-resolution images and limitations of local attention are perhaps the main bottlenecks. In this work, we present a multi-axis MLP based architecture called MAXIM, that can serve as an efficient and flexible general-purpose vision backbone for image processing tasks. MAX…
Force Sensing Guided Artery-Vein Segmentation via Sequential Ultrasound Images
Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；School of Artificial Intelligence, University of Chinese Academy of Sciences（中国科学院大学人工智能学院）；Centre for Artificial Intelligence and Robotics, HK Institute of Science & Innovation, Chinese Academy of Sciences（中国科学院香港创新研究院人工智能与机器人中心）；City University of Hong Kong（香港城市大学）
EAGLE: An Efficient Global Attention Lesion Segmentation Model for Hepatic Echinococcosis
School of Computer Technology and Application, Qinghai University（青海大学计算机技术与应用学院）；Department of Computer Science and Technology, Tsinghua University（清华大学计算机科学与技术系）；Intelligent Computing and Application Laboratory of Qinghai Province, Qinghai University（青海省智能计算与应用实验室）；School of Chemical Engineering, Qinghai University（青海大学化学工程学院）；Department of General Surgery, Affiliated Hospital of Qinghai University（青海大学附属医院普外科）；Department of Medical Engineering Integration and Translational Application, Affiliated Hospital of Qinghai University（青海大学附属医院医学工程整合与转化应用系）
DA-TransUNet: Integrating Spatial and Channel Dual Attention with Transformer U-Net for Medical Image Segmentation
Hangzhou Medical College（杭州医学院）；Japan Advanced Institute of Science and Technology（日本北陆先端科学技术大学院大学）；Tsinghua University（清华大学）；Osaka University（大阪大学）；Hosei University（法政大学）
本文提出 DA-TransUNet，在 U 形编解码结构中结合 Transformer 与空间、通道双重注意力块，以增强多类特征融合并提升医学图像分割精度与效率。
