Doppler holography is an emerging retinal imaging technique that captures the dynamic behavior of blood flow with high temporal resolution, enabling quantitative assessment of retinal hemodynamics. This requires accurate segmentation of retinal arteries and veins, but traditional segmentation methods focus solely on spatial information and overlook the temporal richness of holographic data. In this work, we propose a simple yet effective approach for artery-vein segmentation in temporal Doppler holograms using standard segmentation architectures. By incorporating features derived from a dedicated pulse analysis pipeline, our method allows conventional U-Nets to exploit temporal dynamics and achieve performance comparable to more complex attention- or iteration-based models. These findings demonstrate that time-resolved preprocessing can unlock the full potential of deep learning for Doppler holography, opening new perspectives for quantitative exploration of retinal hemodynamics. The dataset is publicly available at https://huggingface.co/datasets/DigitalHolography/
Artery and vein (AV) classification of retinal images is a key to necessary tasks, such as automated measurement of arteriolar-to-venular diameter ratio (AVR). This paper comprehensively reviews the state-of-the art in AV classification methods. To improve on previous methods, a new Local Bi- nary Pattern-based method (LBP) is proposed. Beside its simplicity, LBP is robust against low contrast and low quality fundus images; and it helps the process by including additional AV texture and shape information. Experimental results compare the performance of the new method with the state-of-the art;…
Objective Automatic artery/vein (A/V) segmentation from fundus images is required to track blood vessel changes occurring with many pathologies including retinopathy and cardiovascular pathologies. One of the clinical measures that quantifies vessel changes is the arterio-venous ratio (AVR) which represents the ratio between artery and vein diameters. This measure significantly depends on the accuracy of vessel segmentation and classification into arteries and veins. This paper proposes a fast, novel method for semantic A/V segmentation combining deep learning and graph propagation. Methods A …
University of California, Los Angeles（加利福尼亚大学洛杉矶分校）；Stein Eye Institute（斯坦眼科研究所）；VoxelCloud, Inc.（体素云公司）
The retinal vasculature provides important clues in the diagnosis and monitoring of systemic diseases including hypertension and diabetes. The microvascular system is of primary involvement in such conditions, and the retina is the only anatomical site where the microvasculature can be directly observed. The objective assessment of retinal vessels has long been considered a surrogate biomarker for systemic vascular diseases, and with recent advancements in retinal imaging and computer vision technologies, this topic has become the subject of renewed attention. In this paper, we present a novel…
Identifying landmarks in the femoral area is crucial for ultrasound (US) -based robot-guided catheter insertion, and their presentation varies when imaged with different scanners. As such, the performance of past deep learning-based approaches is also narrowly limited to the training data distribution; this can be circumvented by fine-tuning all or part of the model, yet the effects of fine-tuning are seldom discussed. In this work, we study the US-based segmentation of multiple classes through transfer learning by fine-tuning different contiguous blocks within the model, and evaluating on a g…
Centro de Investigación CITIC（CITIC研究中心）；Universidade da Coruña（拉科鲁尼亚大学）；Instituto de Investigación Biomédica de A Coruña (INIBIC)（拉科鲁尼亚生物医学研究所（INIBIC））
The study of the retinal vasculature is a fundamental stage in the screening and diagnosis of many diseases. A complete retinal vascular analysis requires to segment and classify the blood vessels of the retina into arteries and veins (A/V). Early automatic methods approached these segmentation and classification tasks in two sequential stages. However, currently, these tasks are approached as a joint semantic segmentation task, as the classification results highly depend on the effectiveness of the vessel segmentation. In that regard, we propose a novel approach for the simultaneous segmentat…
Fuzhou University（福州大学）；Fujian Medical University Union Hospital（福建医科大学附属协和医院）
With the development of medical computer-aided diagnostic systems, pulmonary artery-vein(A/V) separation plays a crucial role in assisting doctors in preoperative planning for lung cancer surgery. However, distinguishing arterial from venous irrigation in chest CT images remains a challenge due to the similarity and complex structure of the arteries and veins. We propose a novel method for automatic separation of pulmonary arteries and veins from chest CT images. The method consists of three parts. First, global connection information and local feature information are used to construct a compl…
Robust automatic brain vessel segmentation in 3D CTA scans using dynamic 4D-CTA data
Northeastern University, Boston, USA（东北大学）；Washington University in St. Louis, St, Louis, USA（华盛顿大学）；Dana-Farber Cancer Institute, Boston, USA（达纳-法伯癌症研究所）；Brigham and Women's Hospital, Boston, USA（布里奇曼和妇女医院）；Harvard Medical School, Boston, USA（哈佛医学院）
Technical University of Kosice（科希策工业大学）；Pavol Jozef Safarik University（帕沃尔·约瑟夫·沙法里克大学）；Louis Pasteur University Hospital（路易斯·巴斯德大学医院）
The kidney cancer is one of the most common cancer types. The treatment frequently include surgical intervention. However, surgery is in this case particularly challenging due to regional anatomical relations. Organ delineation can significantly improve surgical planning and execution. In this contribution, we propose ensemble of two fully convolutional networks for segmentation of kidney, tumor, veins and arteries. While SegResNet architecture achieved better performance on tumor, the nnU-Net provided more precise segmentation for kidneys, arteries and veins. So in our proposed approach we co…
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）
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 …
Centre for Medical Image Computing, University College London（伦敦大学学院医学影像计算中心）；Department of Medical Physics and Biomedical Engineering, UCL（伦敦大学学院医学物理与生物医学工程系）；NIHR Biomedical Research Centre at Moorfields Eye Hospital（莫菲尔德眼科医院NIHR生物医学研究中心）；Wellcome/EPSRC Centre for Interventional and Surgical Sciences（惠康/EPSRC介入与外科科学中心）；Research Center for Healthcare Data Science, Zhejiang Lab（之江实验室医疗健康数据科学研究中心）；UCL Respiratory, University College London（伦敦大学学院呼吸病学中心）；UCL Institute of Ophthalmology, University College London（伦敦大学学院眼科学研究所）
Accurate multi-class segmentation is a long-standing challenge in medical imaging, especially in scenarios where classes share strong similarity. Segmenting retinal blood vessels in retinal photographs is one such scenario, in which arteries and veins need to be identified and differentiated from each other and from the background. Intra-segment misclassification, i.e. veins classified as arteries or vice versa, frequently occurs when arteries and veins intersect, whereas in binary retinal vessel segmentation, error rates are much lower. We thus propose a new approach that decomposes multi-cla…
Improve Retinal Artery/Vein Classification via Channel Couplin
Institute of Medical Technology, Peking University Health Science Center, Peking University, Beijing, China（北京大学医学部医学技术研究所）；Department of Biomedical Engineering, Peking University, Beijing, China（北京大学生物医学工程系）；National Biomedical Imaging Center, Peking University, Beijing, China（北京大学国家生物医学成像中心）；Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology（佐治亚理工学院生物医学工程系）；Emory University, Atlanta, GA, USA（埃默里大学）
Retinal artery/vein (A/V) classification lays the foundation for the quantitative analysis of retinal vessels, which is associated with potential risks of various cardiovascular and cerebral diseases. The topological connection relationship, which has been proved effective in improving the A/V classification performance for the conventional graph based method, has not been exploited by the deep learning based method. In this paper, we propose a Topology Ranking Generative Adversarial Network (TR-GAN) to improve the topology connectivity of the segmented arteries and veins, and further to boost…
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）
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…
Scaling up fine-grained intracranial vessel annotations in computed tomography angiography
Brigham and Women’s Hospital Harvard Medical School（哈佛医学院布莱尔妇女医院）；Khoury College of Computer Sciences Northeastern University（东北大学计算机科学学院）；Washington University in St. Louis（圣路易斯华盛顿大学）；Dana-Farber Cancer Institute Harvard Medical School（哈佛医学院丹娜-法伯癌症研究院）
SLOctolyzer: Fully automatic analysis toolkit for segmentation and feature extracting in scanning laser ophthalmoscopy images
University of Edinburgh（爱丁堡大学）；Institute for Regeneration and Repair, University of Edinburgh（爱丁堡大学再生与修复研究所）；Centre for Medical Informatics, University of Edinburgh（爱丁堡大学医学信息学中心）；School of Informatics, University of Edinburgh（爱丁堡大学信息学院）；Clinical Research Facility and Imaging, University of Edinburgh（爱丁堡大学临床研究设施与影像中心）；School of Mathematics, University of Edinburgh（爱丁堡大学数学学院）；Institute for Adaptive and Neural Computation, School of Informatics, University of Edinburgh（爱丁堡大学信息学院自适应与神经计算研究所）；Centre for Clinical Brain Sciences, University of Edinburgh（爱丁堡大学临床脑科学中心）
RWTH Aachen University（亚琛工业大学）；RWTH Aachen University Hospital（亚琛工业大学医院）；Fraunhofer Institute for Digital Medicine MEVIS（弗劳恩霍夫数字医学MEVIS研究所）
In digital pathology, many image analysis tasks are challenged by the need for large and time-consuming manual data annotations to cope with various sources of variability in the image domain. Unsupervised domain adaptation based on image-to-image translation is gaining importance in this field by addressing variabilities without the manual overhead. Here, we tackle the variation of different histological stains by unsupervised stain-to-stain translation to enable a stain-independent applicability of a deep learning segmentation model. We use CycleGANs for stain-to-stain translation in kidney …
