Automatic medical image segmentation technology has the potential to expedite pathological diagnoses, thereby enhancing the efficiency of patient care. However, medical images often have complex textures and structures, and the models often face the problem of reduced image resolution and information loss due to downsampling. To address this issue, we propose HC-Mamba, a new medical image segmentation model based on the modern state space model Mamba. Specifically, we introduce the technique of dilated convolution in the HC-Mamba model to capture a more extensive range of contextual information without increasing the computational cost by extending the perceptual field of the convolution kernel. In addition, the HC-Mamba model employs depthwise separable convolutions, significantly reducing the number of parameters and the computational power of the model. By combining dilated convolution and depthwise separable convolutions, HC-Mamba is able to process large-scale medical image data at a much lower computational cost while maintaining a high level of performance. We conduct comprehensive experiments on segmentation tasks including organ segmentation and skin lesion, and conduct extensive experiments on Synapse, ISIC17 and ISIC18 to demonstrate the potential of the HC-Mamba model in medical image segmentation. The experimental results show that HC-Mamba exhibits competitive performance on all these datasets, thereby proving its effectiveness and usefulness in medical image segmentation.
UD-Mamba: A pixel-level uncertainty-driven Mamba model for medical image segmentation
University of California, Santa Cruz（加州大学圣克鲁兹分校）；Johns Hopkins University（约翰霍普金斯大学）；Stanford University（斯坦福大学）；Mohamed bin Zayed University of Artificial Intelligence（穆罕默德·本·扎耶德人工智能大学）；University of Adelaide（阿德莱德大学）
LightM-UNet: Mamba Assists in Lightweight UNet for Medical Image Segmentation
School of Computer Science, Peking University（北京大学计算机学院）；Institute of Artificial Intelligence, Beihang University（北京航空航天大学人工智能研究院）；Key Laboratory of High Confidence Software Technologies, Ministry of Education（教育部高可信软件技术重点实验室）；National Engineering Research Center for Software Engineering, Peking University（北京大学国家软件工程研究中心）
VM-UNET-V2 Rethinking Vision Mamba UNet for Medical Image Segmentation
State Key Laboratory for Novel Software Technology, Nanjing University（南京大学计算机软件新技术国家重点实验室）；Huazhong University of Science and Technology（华中科技大学）；Jiangsu Province Hospital of Chinese Medicine（江苏省中医院）
Mamba Goes HoME: Hierarchical Soft Mixture-of-Experts for 3D Medical Image Segmentation
Faculty of Mathematics, Informatics, and Mechanics, University of Warsaw（华沙大学数学、信息学与力学系）；Faculty of Mathematics and Computer Science, Jagiellonian University（雅盖隆大学数学与计算机科学系）；Institute of AI for Health, Helmholtz Munich（海德堡医学院人工智能与健康研究所）；Faculty of Electronics and Information Technology, Warsaw University of Technology（华沙理工大学电子与信息技术系）；NASK - National Research Institute（国家研究 institute）；Faculty of Radiology, Massachusetts General Hospital（麻省总医院放射学系）；Department of Radiology, Harvard Medical School（哈佛医学院放射学系）
VM-UNet: Vision Mamba UNet for Medical Image Segmentation
School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University（上海交通大学电子信息与电气工程学院）；School of Biomedical Engineering, Shanghai Jiao Tong University（上海交通大学生物医学工程学院）
Mamba-Sea: A Mamba-based Framework with Global-to-Local Sequence Augmentation for Generalizable Medical Image Segmentation
Shanghai Jiao Tong University School of Medicine（上海交通大学医学院）；National Institute of Healthcare Data Science（健康数据科学国家研究院）；State Key Laboratory for Novel Software Technology（新型软件技术国家重点实验室）；School of Computer Science and Engineering（计算机科学与工程学院）；Key Lab of Computer Network and Information Integration（计算机网络与信息集成重点实验室）；School of Electrical and Information Engineering（电气与信息工程学院）；The University of Sydney（悉尼大学）
Diversity-enhanced Collaborative Mamba for Semi-supervised Medical Image Segmentation
School of Intelligence Science and Technology, Nanjing University（智能科学与技术学院、南京大学）；School of Electrical and Information Engineering, The University of Sydney（电气与信息工程学院、悉尼大学）
TK-Mamba: Marrying KAN With Mamba for Text-Driven 3D Medical Image Segmentation
Zhejiang University（浙江大学）；Research Center, National Certification Technology (Hangzhou) Co., Ltd（国家认证技术（杭州）有限公司研究部）；Shanghai Jiao Tong University（上海交通大学）；Shanghai Artificial Intelligence Laboratory（上海人工智能实验室）；East China Normal University（华东师范大学）
LKM-UNet: Large Kernel Vision Mamba UNet for Medical Image Segmentation
College of Computer Science and Technology, Zhejiang University（浙江大学计算机科学与技术学院）；Institute of Wenzhou, Zhejiang University（浙江大学温州研究院）；The Second Affiliated Hospital, Zhejiang University School of Medicine（浙江大学医学院附属第二医院）；University of Illinois Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）；University of Notre Dame（圣母大学）
SegMamba: Long-range Sequential Modeling Mamba For 3D Medical Image Segmentation
The Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））；Beijing Academy of Artificial Intelligence（北京智源人工智能研究院）；The Hong Kong University of Science and Technology（香港科技大学）
Mamba, a State Space Model (SSM), has recently shown competitive performance to Convolutional Neural Networks (CNNs) and Transformers in Natural Language Processing and general sequence modeling. Various attempts have been made to adapt Mamba to Computer Vision tasks, including medical image segmentation (MIS). Vision Mamba (VM)-based networks are particularly attractive due to their ability to achieve global receptive fields, similar to Vision Transformers, while also maintaining linear complexity in the number of tokens. However, the existing VM models still struggle to maintain both spatial…
BiSegMamba: Efficient Bidirectional Tri-Oriented Mamba for 3D Medical Image Segmentation
School of Computer Science and Engineering, Beihang University（北航计算机科学与工程学院）；State Key Laboratory of Virtual Reality Technology and Systems, Beihang University（北航虚拟现实技术与系统国家重点实验室）
MS-UMamba: An Improved Vision Mamba Unet for Fetal Abdominal Medical Image Segmentation
Guangxi Key Laboratory of Machine Vision and Intelligent Control（广西机器视觉与智能控制重点实验室）；Wuzhou University（梧州大学）；College of Electronical and Information Engineering（电子信息工程学院）；Wuzhou Medical College（梧州医学院）；School of Microelectronics（微电子学院）；Southern Medical University（南方医科大学）
Swin-UMamba: Mamba-based UNet with ImageNet-based pretraining
Paul C. Lauterbur Research Center for Biomedical Imaging, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences（中国科学院深圳先进技术研究院保罗·C·劳特伯生物医学成像研究中心）；Peng Cheng Laboratory（鹏城实验室）；University of Chinese Academy of Sciences（中国科学院大学）；The University of Hong Kong（香港大学）；Shanghai Artificial Intelligence Laboratory（上海人工智能实验室）
School of Advanced Technology, Xi’an Jiaotong-Liverpool University（西交利物浦大学先进技术学院）；School of Electronics and Information Engineering, Beihang University（北京航空航天大学电子信息工程学院）；Cambridge University（剑桥大学）；Beijing Shijitan Hospital, Capital Medical University（首都医科大学附属北京世纪坛医院）；University of Liverpool（利物浦大学）
HyM-UNet: Synergizing Local Texture and Global Context via Hybrid CNN-Mamba Architecture for Medical Image Segmentation
School of Information and Communication Engineering, Beijing University of Posts and Communications (BUPT)（信息与通信工程学院，北京邮电大学）；Alibaba Cloud AI Labs（阿里云人工智能实验室）
ACM-UNet: Adaptive Integration of CNNs and Mamba for Efficient Medical Image Segmentation
Wuhan University of Technology（武汉理工大学）；Chinese Academy of Medical Sciences and Peking Union Medical College（中国医学科学院北京协和医学院）；Zhongnan Hospital of Wuhan University（武汉大学中南医院）
MambaPanoptic: A Vision Mamba-based Structured State Space Framework for Panoptic Segmentation
Technical University of Munich（慕尼黑技术大学）；Munich Center for Machine Learning (MCML)（慕尼黑机器学习中心）；Polytechnic University of Milan（米兰理工大学）；University of Stuttgart（斯图加特大学）；Wuhan University（武汉大学）；Karlsruhe University of Applied Sciences（卡尔斯鲁厄应用科学大学）
U-Mamba: Enhancing Long-range Dependency for Biomedical Image Segmentation
Peter Munk Cardiac Centre, University Health Network（彼得·蒙克心脏中心，大学健康网络）；University of Toronto（多伦多大学）；Vector Institute for Artificial Intelligence（向量人工智能研究所）
SpineMamba: Enhancing 3D Spinal Segmentation in Clinical Imaging through Residual Visual Mamba Layers and Shape Priors
Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences（中国科学院深圳先进技术研究院）；University of Chinese Academy of Sciences（中国科学院大学）；Northeast Petroleum University（东北石油大学）；Northeastern University（东北大学）；Southern Medical University（南方医科大学）
Unified Medical Image Segmentation with State Space Modeling Snake
Sun Yat-sen University（中山大学）；Tsinghua Shenzhen International Graduate School（清华大学深圳国际研究生院）；Beijing University of Posts and Telecommunications（北京邮电大学）；The Australian National University（澳大利亚国立大学）
Mamba-Driven Topology Fusion for Monocular 3D Human Pose Estimation
College of Sciences, Northeastern University（东北大学理学院）；Key Laboratory of Differential Equations and Their Applications, Northeastern University（东北大学偏微分方程及其应用重点实验室）；Nanjing University of Science and Technology（南京理工大学）
MambaMIR: An Arbitrary-Masked Mamba for Joint Medical Image Reconstruction and Uncertainty Estimation
Imperial College London（伦敦帝国学院）；Royal Brompton Hospital（皇家布朗普顿医院）；Nanjing University of Aeronautics and Astronautics（南京航空航天大学）；University of Cambridge（剑桥大学）
National Institute of Technology Karnataka（卡纳塔克邦国家技术研究所）
Computer-aided medical image analysis plays a significant role in assisting medical practitioners for expert clinical diagnosis and deciding the optimal treatment plan. At present, convolutional neural networks (CNN) are the preferred choice for medical image analysis. In addition, with the rapid advancements in three-dimensional (3D) imaging systems and the availability of excellent hardware and software support to process large volumes of data, 3D deep learning methods are gaining popularity in medical image analysis. Here, we present an extensive review of the recently evolved 3D deep learn…
In this paper, we propose a novel medical image segmentation using iterative deep learning framework. We have combined an iterative learning approach and an encoder-decoder network to improve segmentation results, which enables to precisely localize the regions of interest (ROIs) including complex shapes or detailed textures of medical images in an iterative manner. The proposed iterative deep convolutional encoder-decoder network consists of two main paths: convolutional encoder path and convolutional decoder path with iterative learning. Experimental results show that the proposed iterative …
{S$^3$-Mamba}: Small-Size-Sensitive Mamba for Lesion Segmentation
Shenzhen University（深圳大学）；University of Nottingham Ningbo China（宁波诺丁汉大学）；Guangxi Medical University（广西医科大学）；City University of Hong Kong（香港城市大学）；University of Nottingham（诺丁汉大学）
We propose a novel technique to incorporate attention within convolutional neural networks using feature maps generated by a separate convolutional autoencoder. Our attention architecture is well suited for incorporation with deep convolutional networks. We evaluate our model on benchmark segmentation datasets in skin cancer segmentation and lung lesion segmentation. Results show highly competitive performance when compared with U-Net and it's residual variant.
GIGP: A Global Information Interacting and Geometric Priors Focusing Framework for Semi-supervised Medical Image Segmentation
The School of Mathematical Science, Capital Normal University（首都师范大学数学科学学院）；The Academy for Multidisciplinary Studies, Capital Normal University（首都师范大学交叉科学研究院）
