Retinal vessel segmentation serves as a critical prerequisite for automated diagnosis of retinal pathologies. While recent advances in Convolutional Neural Networks (CNNs) have demonstrated promising performance in this task, significant performance degradation occurs when domain shifts exist between training and testing data. To address these limitations, we propose a novel domain transfer framework that leverages latent vascular similarity across domains and iterative co-optimization of generation and segmentation networks. Specifically, we first pre-train generation networks for source and target domains. Subsequently, the pretrained source-domain conditional diffusion model performs deterministic inversion to establish intermediate latent representations of vascular images, creating domain-agnostic prototypes for target synthesis. Finally, we develop an iterative refinement strategy where segmentation network and generative model undergo mutual optimization through cyclic parameter updating. This co-evolution process enables simultaneous enhancement of cross-domain image synthesis quality and segmentation accuracy. Experiments demonstrate that our framework achieves state-of-the-art performance in cross-domain retinal vessel segmentation, particularly in challenging clinical scenarios with significant modality discrepancies.
Diffusion model is the most advanced method in image generation and has been successfully applied to MRI reconstruction. However, the existing methods do not consider the characteristics of multi-coil acquisition of MRI data. Therefore, we give a new diffusion model, called SPIRiT-Diffusion, based on the SPIRiT iterative reconstruction algorithm. Specifically, SPIRiT-Diffusion characterizes the prior distribution of coil-by-coil images by score matching and characterizes the k-space redundant prior between coils based on self-consistency. With sufficient prior constraint utilized, we achieve s…
Wuhan University of Technology（武汉理工大学）；Institute of Geographic Sciences and Natural Resources Research, CAS（中国科学院地理科学与资源研究所）；Institute of High Performance Computing, A*Star (Agency for Science, Technology and Research)（新加坡科技研究局高性能计算研究所）
To achieve reliable mining results for massive vessel trajectories, one of the most important challenges is how to efficiently compute the similarities between different vessel trajectories. The computation of vessel trajectory similarity has recently attracted increasing attention in the maritime data mining research community. However, traditional shape- and warping-based methods often suffer from several drawbacks such as high computational cost and sensitivity to unwanted artifacts and non-uniform sampling rates, etc. To eliminate these drawbacks, we propose an unsupervised learning method…
Peripancreatic vessel segmentation and anatomical labeling play extremely important roles to assist the early diagnosis, surgery planning and prognosis for patients with pancreatic tumors. However, most current techniques cannot achieve satisfactory segmentation performance for peripancreatic veins and usually make predictions with poor integrity and connectivity. Besides, unsupervised labeling algorithms cannot deal with complex anatomical variation while fully supervised methods require a large number of voxel-wise annotations for training, which is very labor-intensive and time-consuming. T…
TempDiffReg: Temporal Diffusion Model for Non-Rigid 2D-3D Vascular Registration
School of Computer Science and Engineering, Beihang University, Beijing, China（北京航空航天大学计算机科学与工程学院）；State Key Laboratory of Virtual Reality Technology and Systems, Beihang University, Beijing, China（北京航空航天大学虚拟现实技术与系统国家重点实验室）；Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China（中国科学院深圳先进技术研究所）；School of Computer Science and Control Engineering, Shenzhen University of Advanced Technology, Shenzhen, China（深圳先进技术大学计算机科学与控制工程学院）；Department of Interventional Radiology, Shenzhen People’s Hospital, Shenzhen, China（深圳人民医院介入放射科）
We present a fully automated method for top-down segmentation of the pulmonary arterial tree in low-dose thoracic CT images. The main basal pulmonary arteries are identified near the lung hilum by searching for candidate vessels adjacent to known airways, identified by our previously reported airway segmentation method. Model cylinders are iteratively fit to the vessels to track them into the lungs. Vessel bifurcations are detected by measuring the rate of change of vessel radii, and child vessels are segmented by initiating new trackers at bifurcation points. Validation is accomplished using …
University of São Paulo（圣保罗大学）；Pontifical Catholic University of Rio de Janeiro（里约热内卢天主教大学）；Leibniz University Hannover（汉诺威莱布尼茨大学）；Rio de Janeiro State University（里约热内卢州立大学）
The identification of vascular networks is an important topic in the medical image analysis community. While most methods focus on single vessel tracking, the few solutions that exist for tracking complete vascular networks are usually computationally intensive and require a lot of user interaction. In this paper we present a method to track full vascular networks iteratively using a single starting point. Our approach is based on a cloud of sampling points distributed over concentric spherical layers. We also proposed a vessel model and a metric of how well a sample point fits this model. The…
Given image labels as the only supervisory signal, we focus on harvesting, or mining, thoracic disease localizations from chest X-ray images. Harvesting such localizations from existing datasets allows for the creation of improved data sources for computer-aided diagnosis and retrospective analyses. We train a convolutional neural network (CNN) for image classification and propose an attention mining (AM) strategy to improve the model's sensitivity or saliency to disease patterns. The intuition of AM is that once the most salient disease area is blocked or hidden from the CNN model, it will pa…
Tsinghua University（清华大学）；DATA61-CSIRO（联邦科学与工业研究组织DATA61）；Australian National University（澳大利亚国立大学）
Weakly-supervised semantic segmentation under image tags supervision is a challenging task as it directly associates high-level semantic to low-level appearance. To bridge this gap, in this paper, we propose an iterative bottom-up and top-down framework which alternatively expands object regions and optimizes segmentation network. We start from initial localization produced by classification networks. While classification networks are only responsive to small and coarse discriminative object regions, we argue that, these regions contain significant common features about objects. So in the bott…
Ubisoft（育碧）；The European Institute for Molecular Imaging（欧洲分子影像研究所）
Recent advances in 3D imaging technologies provide novel insights to researchers and reveal finer and more detail of examined specimen, especially in the biomedical domain, but also impose huge challenges regarding scalability for automated analysis algorithms due to rapidly increasing dataset sizes. In particular, existing research towards automated vessel network analysis does not consider memory requirements of proposed algorithms and often generates a large number of spurious branches for structures consisting of many voxels. Additionally, very often these algorithms have further restricti…
Cross-Domain Few-Shot Segmentation via Iterative Support-Query Correspondence Mining
Interdisciplinary Graduate Programme, Nanyang Technological University（南洋理工大学跨学科研究生项目）；Nanyang Technological University（南洋理工大学）；Black Sesame Technologies（黑芝麻智能科技）；Huazhong University of Science and Technology（华中科技大学）
Dalian University of Technology（大连理工大学）；Ritsumeikan University（立命馆大学）
Many deep learning based methods have been proposed for retinal vessel segmentation, however few of them focus on the connectivity of segmented vessels, which is quite important for a practical computer-aided diagnosis system on retinal images. In this paper, we propose an efficient network to address this problem. A U-shape network is enhanced by introducing a semantics-guided module, which integrates the enriched semantics information to shallow layers for guiding the network to explore more powerful features. Besides, a recursive refinement iteratively applies the same network over the prev…
DeepEraser: Deep Iterative Context Mining for Generic Text Eraser
University of Science and Technology of China（中国科学技术大学）；Zhangjiang Lab（张江实验室）；The University of Adelaide（阿德莱德大学）；Australian Institute for Machine Learning（澳大利亚机器学习研究所）
Northwestern Polytechnical University（西北工业大学）；Mohamed bin Zayed University of Artificial Intelligence（穆罕默德·本·扎耶德人工智能大学）
Few-shot semantic segmentation aims to segment the target objects in query under the condition of a few annotated support images. Most previous works strive to mine more effective category information from the support to match with the corresponding objects in query. However, they all ignored the category information gap between query and support images. If the objects in them show large intra-class diversity, forcibly migrating the category information from the support to the query is ineffective. To solve this problem, we are the first to introduce an intermediate prototype for mining both d…
China University of Petroleum (East China)（中国石油大学（华东））；Qingdao University（青岛大学）；Jiangxi University of Finance and Economics（江西财经大学）
The existing state-of-the-art (SOTA) video salient object detection (VSOD) models have widely followed short-term methodology, which dynamically determines the balance between spatial and temporal saliency fusion by solely considering the current consecutive limited frames. However, the short-term methodology has one critical limitation, which conflicts with the real mechanism of our visual system -- a typical long-term methodology. As a result, failure cases keep showing up in the results of the current SOTA models, and the short-term methodology becomes the major technical bottleneck. To sol…
School of Biomedical Engineering, Shanghai Jiao Tong University（上海交通大学生物医学工程学院）；School of Continuing Education, Shanghai Jiao Tong University（上海交通大学继续教育学院）；Shanghai Jiao Tong University Affiliated Sixth People’s Hospital（上海交通大学附属第六人民医院）；Ren Ji Hospital, School of Medicine, Shanghai Jiao Tong University（上海交通大学医学院附属仁济医院）；Center for Advanced Neuroimaging, University of California, Riverside（加州大学河滨分校高级神经影像中心）
Although robust PCA has been increasingly adopted to extract vessels from X-ray coronary angiography (XCA) images, challenging problems such as inefficient vessel-sparsity modelling, noisy and dynamic background artefacts, and high computational cost still remain unsolved. Therefore, we propose a novel robust PCA unrolling network with sparse feature selection for super-resolution XCA vessel imaging. Being embedded within a patch-wise spatiotemporal super-resolution framework that is built upon a pooling layer and a convolutional long short-term memory network, the proposed network can not onl…
Global Control for Local SO(3)-Equivariant Scale-Invariant Vessel Segmentation
University of Twente（特文特大学）；Amsterdam UMC location Vrije Universiteit Amsterdam（阿姆斯特丹大学医学中心（阿姆斯特丹自由大学医学中心））；Amsterdam Cardiovascular Sciences（阿姆斯特丹心血管科学研究院）
Universitat Oberta de Catalunya（加泰罗尼亚开放大学）；Computer Vision Lab, ETH Zürich（苏黎世联邦理工学院计算机视觉实验室）
This paper tackles the task of estimating the topology of filamentary networks such as retinal vessels and road networks. Building on top of a global model that performs a dense semantical classification of the pixels of the image, we design a Convolutional Neural Network (CNN) that predicts the local connectivity between the central pixel of an input patch and its border points. By iterating this local connectivity we sweep the whole image and infer the global topology of the filamentary network, inspired by a human delineating a complex network with the tip of their finger. We perform an ext…
Universitat Oberta de Catalunya（加泰罗尼亚开放大学）；ETH Zürich（苏黎世联邦理工学院）
This paper tackles the task of estimating the topology of road networks from aerial images. Building on top of a global model that performs a dense semantical classification of the pixels of the image, we design a Convolutional Neural Network (CNN) that predicts the local connectivity among the central pixel of an input patch and its border points. By iterating this local connectivity we sweep the whole image and infer the global topology of the road network, inspired by a human delineating a complex network with the tip of their finger. We perform an extensive and comprehensive qualitative an…
A Self-training Framework for Semi-supervised Pulmonary Vessel Segmentation and Its Application in COPD
Northeastern University（东北大学）；The First Affiliated Hospital of Guangzhou Medical University（广州医科大学附属第一医院）；Shenyang University of Technology（沈阳工业大学）；Hetao Institute of Guangzhou National Laboratory（广州国家实验室河套研究院）；China Medical University（中国医科大学）
Korea Advanced Institute of Science and Technology (KAIST)（韩国高等科学技术院（KAIST））
Semantic segmentation algorithms require access to well-annotated datasets captured under diverse illumination conditions to ensure consistent performance. However, poor visibility conditions at varying illumination conditions result in laborious and error-prone labeling. Alternatively, using synthetic samples to train segmentation algorithms has gained interest with the drawback of domain gap that results in sub-optimal performance. While current state-of-the-art (SoTA) have proposed different mechanisms to bridge the domain gap, they still perform poorly in low illumination conditions with a…
Martinos Center for Biomedical Imaging, Massachusetts General Hospital（马萨诸塞州总医院马丁诺斯生物医学成像中心）；Harvard Medical School（哈佛医学院）；Cornell University（康奈尔大学）；Philips Research Laboratories（飞利浦研究实验室）；Boston Children’s Hospital（波士顿儿童医院）
We propose a new iterative segmentation model which can be accurately learned from a small dataset. A common approach is to train a model to directly segment an image, requiring a large collection of manually annotated images to capture the anatomical variability in a cohort. In contrast, we develop a segmentation model that recursively evolves a segmentation in several steps, and implement it as a recurrent neural network. We learn model parameters by optimizing the interme- diate steps of the evolution in addition to the final segmentation. To this end, we train our segmentation propagation …
Co-occurrent visual pattern makes context aggregation become an essential paradigm for semantic segmentation.The existing studies focus on modeling the contexts within image while neglecting the valuable semantics of the corresponding category beyond image. To this end, we propose a novel soft mining contextual information beyond image paradigm named MCIBI++ to further boost the pixel-level representations. Specifically, we first set up a dynamically updated memory module to store the dataset-level distribution information of various categories and then leverage the information to yield the da…
Discriminative Co-Saliency and Background Mining Transformer for Co-Salient Object Detection
Northwestern Polytechnical University（西北工业大学）；Mohamed bin Zayed University of Artificial Intelligence（穆罕默德·本·扎耶德人工智能大学）；Australian National University（澳大利亚国立大学）；Linköping University（林雪平大学）
Southern University of Science and Technology（南方科技大学）；The University of Hong Kong（香港大学）；Jiaxing Research Institute, Southern University of Science and Technology（南方科技大学嘉兴研究院）
Unsupervised domain adaptation has been proposed recently to tackle the so-called domain shift between training data and test data with different distributions. However, most of them only focus on single-target domain adaptation and cannot be applied to the scenario with multiple target domains. In this paper, we propose RVms, a novel unsupervised multi-target domain adaptation approach to segment retinal vessels (RVs) from multimodal and multicenter retinal images. RVms mainly consists of a style augmentation and transfer (SAT) module and a dual-teacher knowledge distillation (DTKD) module. S…
Institute of High Performance Computing, Agency for Science, Technology, and Research（高性能计算研究所，科技研究局）；Centre for Artificial Intelligence, University of Technology Sydney（悉尼科技大学人工智能中心）；Pattern Recognition and Intelligent System Laboratory, Beijing University of Posts and Telecommunications（北京邮电大学模式识别与智能系统实验室）；Brookhaven National Laboratory（布鲁克海文国家实验室）；InnoPeak Technology Inc.；Key Laboratory of Medical Molecular Virology, School of Basic Medical Sciences, Fudan University（复旦大学基础医学院医学分子病毒学重点实验室）
In this paper, we propose a simple yet effective approach, named Point Adversarial Self Mining (PASM), to improve the recognition accuracy in facial expression recognition. Unlike previous works focusing on designing specific architectures or loss functions to solve this problem, PASM boosts the network capability by simulating human learning processes: providing updated learning materials and guidance from more capable teachers. Specifically, to generate new learning materials, PASM leverages a point adversarial attack method and a trained teacher network to locate the most informative positi…
Skelite: Compact Neural Networks for Efficient Iterative Skeletonization
Technical University of Munich（慕尼黑工业大学）；Imperial College London（伦敦帝国理工学院）；Cornell University（康奈尔大学）；Weill Cornell Medicine（威尔康奈尔医学院）；Munich Center for Machine Learning（慕尼黑机器学习中心）
School of Computer Science and Technology, Beijing Institute of Technology（北京理工大学计算机科学与技术学院）；Computer Vision Laboratory, ETH Zurich（苏黎世联邦理工学院计算机视觉实验室）；School of Computer Science and Engineering, Southeast University（东南大学计算机科学与工程学院）；Center for Research on Intelligent Perception and Computing, CASIA（中国科学院自动化研究所智能感知与计算研究中心）
Acquiring sufficient ground-truth supervision to train deep visual models has been a bottleneck over the years due to the data-hungry nature of deep learning. This is exacerbated in some structured prediction tasks, such as semantic segmentation, which requires pixel-level annotations. This work addresses weakly supervised semantic segmentation (WSSS), with the goal of bridging the gap between image-level annotations and pixel-level segmentation. We formulate WSSS as a novel group-wise learning task that explicitly models semantic dependencies in a group of images to estimate more reliable pse…
Deep learning-driven pulmonary artery and vein segmentation reveals demography-associated vasculature anatomical differences
King Abdullah University of Science and Technology (KAUST)（阿卜杜拉国王科技大学）；The First Affiliated Hospital of Harbin Medical University（哈尔滨医科大学附属第一医院）；Northeast Forestry University（东北林业大学）；The Fourth Hospital of Harbin Medical University（哈尔滨医科大学附属第四医院）；China-Japan Friendship Hospital（中日友好医院）；Affiliated Nanjing Drum Tower Hospital of Nanjing University Medical School（南京大学医学院附属南京鼓楼医院）；Renji Hospital, School of Medicine, Shanghai Jiao Tong University（上海交通大学医学院附属仁济医院）
CMFN: Cross-Modal Fusion Network for Irregular Scene Text Recognition
Intelligent Software Research Center, Institute of Software Chinese Academy of Sciences（中国科学院软件研究所智能软件研究中心）；University of Chinese Academy of Sciences（中国科学院大学）；The Laboratory of Cognition and Decision Intelligence for Complex Systems, Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所复杂系统认知与决策智能实验室）；State Key Laboratory of Computer Science, Institute of Software Chinese Academy of Sciences（中国科学院软件研究所计算机科学国家重点实验室）
Neighborhood-Adaptive Generalized Linear Graph Embedding with Latent Pattern Mining
School of Computer and Information, Anhui Normal University（安徽师范大学计算机与信息学院）；Anhui Provincial Key Laboratory of Industrial Intelligent Data Security（安徽省工业智能数据安全重点实验室）；Institute of Automation, State Key Laboratory of Multimodal Artificial Intelligence Systems, Chinese Academy of Sciences（自动化研究所，多模态人工智能系统国家重点实验室，中国科学院）；School of Artificial Intelligence, University of Chinese Academy of Sciences（中国科学院大学人工智能学院）
Huazhong University of Science & Technology（华中科技大学）；University of Technology Sydney（悉尼科技大学）；Farsee2 Tech. Co.（Farsee2科技公司）；Baidu Research（百度研究院）
We aim at the problem named One-Shot Unsupervised Domain Adaptation. Unlike traditional Unsupervised Domain Adaptation, it assumes that only one unlabeled target sample can be available when learning to adapt. This setting is realistic but more challenging, in which conventional adaptation approaches are prone to failure due to the scarce of unlabeled target data. To this end, we propose a novel Adversarial Style Mining approach, which combines the style transfer module and task-specific module into an adversarial manner. Specifically, the style transfer module iteratively searches for harder …
CRISP: Rank-Guided Iterative Squeezing for Robust Medical Image Segmentation under Domain Shift
Southern University of Science and Technology（南方科技大学）；The University of Hong Kong（香港大学）；Jiaxing Research Institute, Southern University of Science and Technology（南方科技大学嘉兴研究院）
Coupled Reconstruction of 2D Blood Flow and Vessel Geometry from Noisy Images via Physics-Informed Neural Networks and Quasi-Conformal Mapping
Department of Mathematics, City University of Hong Kong（香港城市大学数学系）；Hong Kong Centre for Cerebro-Cardiovascular Health Engineering（香港心脑血管健康工程研究中心）；Norwegian Research Centre (NORCE)（挪威研究中心）；Department of Operations and Risk Management and the School of Data Science, Lingnan University（岭南大学运营与风险管理系及数据科学学院）
Towards Adaptive Open-Set Object Detection via Category-Level Collaboration Knowledge Mining
School of Electronic Engineering, Xidian University（西安电子科技大学电子工程学院）；Interdisciplinary Institute of Artificial Intelligence, Faculty of Infor-X, Xidian University（西安电子科技大学信息交叉研究院人工智能跨学科研究所）；School of Artificial Intelligence, Beijing Normal University（北京师范大学人工智能学院）
Neovascularization Segmentation via a Multilateral Interaction-Enhanced Graph Convolutional Network
Laboratory of Advanced Theranostic Materials and Technology, Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences（先进诊疗材料与技术实验室，宁波材料技术研究所，中国科学院）；School of Cyber Science and Engineering, Ningbo University of Technology（网络安全与工程学院，宁波工程学院）；Shandong Artificial Intelligence Institute, Qilu University of Technology, Shandong Academy of Sciences（山东省人工智能研究所，齐鲁工业大学，山东省科学院）；Institute of High Performance Computing, A*STAR（高性能计算研究所，A*STAR）；Eye Center, The Second Affiliated Hospital, Zhejiang University（眼科学中心，浙江大学医学院附属第二医院）；Shenzhen Institute of Advanced Technology, Chinese Academy of Science（深圳先进技术研究所，中国科学院）；University Paris Dauphine, PSL Research University, CNRS, UMR 7534, CEREMADE（巴黎第聂尔大学，PSL研究大学，CNRS，UMR 7534，CEREMADE）；Ningbo Eye Hospital, Wenzhou Medical University（宁波眼医院，温州医科大学）
Uttarakhand Technical University（北阿坎德技术大学）；Maverick Quality Advisory services Pvt, Ltd（Maverick质量咨询服务私人有限公司）；Bipin Tripathi Kumaon Institute of Technology（比平·特里帕蒂库马翁理工学院）
Automation of feature analysis in the dynamic image frame dataset deals with complexity of intensity mapping with normal and abnormal class. The threshold-based data clustering and feature analysis requires iterative model to learn the component of image frame in multi-pattern for different image frame data type. This paper proposed a novel model of feature analysis method with the CNN based on Convoluted Pattern of Wavelet Transform (CPWT) feature vectors that are optimized by Grey Wolf Optimization (GWO) algorithm. Initially, the image frame gets normalized by applying median filter to the i…
Semi-Supervised Medical Image Segmentation via Knowledge Mining from Large Models
University of Cambridge（剑桥大学）；Harvard Medical School（哈佛医学院）；Hohai University（河海大学）；Athena Research Centre（雅典娜研究中心）；Tongji University（同济大学）；University of Edinburgh（爱丁堡大学）；Heriot-Watt University（赫瑞瓦特大学）
