检索结果-内蒙古大学图书馆

IEEE International Symposium on biomedical imaging

作者： Guangzhengao Yang Li Zhang Jie Zhao Zifan Chen Haoshen Li Bin Dong Center for Data Science Peking University China National Engineering Laboratory for Big Data Analysis and Applications Peking University China Peking University Changsha Institute for Computing and Digital Economy China Beijing International Center for Mathematical Research Peking University China Center for Machine Learning Research Peking University China National Biomedical Imaging Center Peking University China

ISBN: (数字)9798331520526

ISBN: (纸本)9798331520533

Nuclei segmentation in Hematoxylin and Eosin (H&E) stained images plays a crucial role in cancer diagnosis and pathological evaluation, enabling pathologists to identify abnormal cells and assess their morphology and distribution. While current automated nuclei segmentation methods predominantly employ convolutional neural networks and attention mechanisms, the potential of element-wise multiplication has been largely unexplored. This paper introduces U-Star, a novel asymmetric segmentation network based on the star block that leverages element-wise multiplication. U-Star adopts the classic encoder-decoder architecture and innovatively implements star-connection as an alternative to traditional skip-connections. In experiments on an H&E stained image dataset, U-Star achieved superior performance with a Dice coefficient of 0.8783, accuracy of 0.9089, and IoU of 0.7929, significantly outperforming baseline models. Extensive ablation studies validate the effectiveness of the star-connection and demonstrate the advantages of our proposed framework. Beyond advancing the application of element-wise multiplication techniques, U-Star shows promising potential for broader applications in medical image segmentation.

关键词： Image segmentation Pathology Technological innovation Image analysis Stars Morphology Network architecture Medical diagnostic imaging Tuning Cancer

来源：评论

学校读者我要写书评

暂无评论

Ai-Driven Automated Tool for Abdominal CT Body Composition Analysis in Gastrointestinal Cancer Management

Ai-Driven Automated Tool for Abdominal CT Body Composition A...

引用

IEEE International Symposium on biomedical imaging

作者： Xinyu Nan Meng He Zifan Chen Bin Dong Lei Tang Li Zhang Center for Data Science Peking University China Department of Radiology Key Laboratory of Carcinogenesis and Translational Research(Ministry of Education) Peking University Cancer Hospital and Institute Beijing China Beijing International Center for Mathematical Research (BICMR) Peking University Beijing China Center for Machine Learning Research Peking University Beijing China National Biomedical Imaging Center Peking University Beijing China

ISBN: (数字)9798331520526

ISBN: (纸本)9798331520533

The incidence of gastrointestinal cancers remains significantly high, particularly in China, emphasizing the importance of accurate prognostic assessments and effective treatment strategies. Research shows a strong correlation between abdominal muscle and fat tissue composition and patient outcomes. However, existing manual methods for analyzing abdominal tissue composition are time-consuming and costly, limiting clinical research scalability. To address these challenges, we developed an AI-driven tool for automated analysis of abdominal CT scans to effectively identify and segment muscle, subcutaneous fat, and visceral fat. Our tool integrates a multi-view localization model and a high-precision 2D nnUNet-based segmentation model, demonstrating a localization accuracy of 90% and a Dice Score Coefficient of 0.967 for segmentation. Furthermore, it features an interactive interface that allows clinicians to refine the segmentation results, ensuring high-quality outcomes effectively. Our tool offers a standardized method for effectively extracting critical abdominal tissues, potentially enhancing the management and treatment for gastrointestinal cancers. The code is available at https://***/NanXinyu/***.

关键词： Location awareness Analytical models Accuracy Computed tomography Scalability Manuals Muscles Gastrointestinal tract Fats Cancer

来源：评论

学校读者我要写书评

暂无评论

RVPD: An Automated System for Calculating the Tortuosity and Bifurcation Angles of Retinal Vessels to Predict Diseases

RVPD: An Automated System for Calculating the Tortuosity and...

引用

IEEE International Symposium on biomedical imaging

作者： Guangzhengao Yang Xingyu Luo Jie Zhao Fangfang Fan Haoshen Li Bin Dong Li Zhang Yan Zhang Center for Data Science Peking University China Department of Cardiology Peking University First Hospital China National Engineering Laboratory for Big Data Analysis and Applications Peking University China Peking University Changsha Institute for Computing and Digital Economy China Beijing International Center for Mathematical Research Peking University China Center for Machine Learning Research Peking University China National Biomedical Imaging Center Peking University China

ISBN: (数字)9798331520526

ISBN: (纸本)9798331520533

Hypertension and diabetes are known to potentially cause morphological changes in the retinal capillary system, yet quantifying these changes presents significant challenges. This research addresses this issue by designing and developing an automated system capable of analyzing retinal images to detect and measure these changes. Our system automatically segments vessels and the optic disc, calculates tortuosity and bifurcation angles, and performs disease prediction based on these measurements along with clinical features. For tortuosity calculation, we adopted a novel curve fitting method, which provides more stable convergence compared to traditional discrete numerical methods. The system integrates clinical data and conducts prediction experiments on hypertensive and diabetic patients with varying sample sizes. Our results demonstrate the system's strong performance and scalability: in the hypertension group, the AUC reached a maximum of 0.814 ± 0.023, with an ACC of 0.735 ± 0.033, while in the diabetes group, the AUC reached a maximum of 0.710 ± 0.043, with an ACC of 0.641 ± 0.052. These findings suggest that our automated system could serve as a valuable tool for early detection and risk assessment of hypertension and diabetes, potentially improving patient outcomes through timely intervention.

关键词： Hypertension Image segmentation Scalability Bifurcation Optical variables measurement Optical imaging Retinal vessels Diabetes Risk management Diseases

来源：评论

学校读者我要写书评

暂无评论

Laser speckle contrast imaging with principal component and entropy analysis: a novel approach for depth-independent blood flow assessment

引用

Frontiers of Optoelectronics 2025年第1期18卷 1-19页

作者： Yu.Surkov P.Timoshina I.Serebryakova D.Stavtcev I.Kozlov G.Piavchenko I.Meglinski A.Konovalov D.Telyshev S.Kuznetcov E.Genina V.Tuchin Institution of Physics Saratov State UniversitySaratov 410012Russia Scientific Medical Center Saratov State UniversitySaratov 410012Russia Laboratory of Laser Molecular Imaging and Machine Learning Tomsk State UniversityTomsk 634050Russia Institute for Bionic Technologies and Engineering I.M.Sechenov First Moscow State Medical UniversityMoscow 119991Russia Institute of Biomedical Systems National Research University of Electronic TechnologyZelenogradMoscow 124498Russia Department of Human Anatomy and Histology Cytology and EmbryologyInstitute of Clinical Medicine N.V.SklifosovskyI.M.Sechenov First Moscow State Medical UniversityMoscow 119991Russia Aston Institute of Materials Research School of Engineering and Applied ScienceAston UniversityBirmingham B47ETUK Burdenko Neurosurgery Institute Moscow 125047Russia

Current study presents an advanced method for improving the visualization of subsurface blood vessels using laser speckle contrast imaging (LSCI), enhanced through principal component analysis (PCA) filtering. By combining LSCI and laser speckle entropy imaging with PCA filtering, the method effectively separates static and dynamic components of the speckle signal, significantly improving the accuracy of blood flow assessments, even in the presence of static scattering layers located above and below the vessel. Experiments conducted on optical phantoms, with the vessel depths ranging from 0.6 to 2 mm, and in vivo studies on a laboratory mouse ear demonstrate substantial improvements in image contrast and resolution. The method’s sensitivity to blood flow velocity within the physiologic range (0.98-19.66 mm/s) is significantly enhanced, while its sensitivity to vessel depth is minimized. These results highlight the method’s ability to assess blood flow velocity independently of vessel depth, overcoming a major limitation of conventional LSCI techniques. The proposed approach holds great potential for non-invasive biomedical imaging, offering improved diagnostic accuracy and contrast in vascular imaging. These findings may be particularly valuable for advancing the use of LSCI in clinical diagnostics and biomedical research, where high precision in blood flow monitoring is essential.

关键词： Laser speckle imaging Speckle contrast Entropy Principal component analysis Blood flow velocity Vascular imaging

来源：评论

学校读者我要写书评

暂无评论

ESR Bridges: lung cancer: new developments in imaging and treatment-a multidisciplinary view

引用

European radiology 2025年 2025 May 10页

作者： Lucian Beer Maximilian Hochmair Joachim Widder Mir Alireza Hoda Thomas Helmberger Department of Biomedical Imaging and Image-guided Therapy Medical University of Vienna Vienna Austria. lucian.beer@meduniwien.ac.at. Christian Doppler Laboratory for Machine Learning Driven Precision Medicine Department of Biomedical Imaging and Image-guided Therapy Medical University of Vienna Vienna Austria. lucian.beer@meduniwien.ac.at. Comprehensive Cancer Center Vienna Medical University of Vienna Vienna Austria. lucian.beer@meduniwien.ac.at. Department of Respiratory and Critical Care Medicine Karl Landsteiner Institute of Lung Research and Pulmonary Oncology Klinik Floridsdorf Vienna Austria. Comprehensive Cancer Center Vienna Medical University of Vienna Vienna Austria. Department of Radiation Oncology Medical University of Vienna Vienna Austria. Department of Thoracic Surgery Medical University of Vienna Vienna Austria. Department of Radiology Neuroradiology and Minimal-Invasive Therapy Klinikum Bogenhausen Munich Germany.

来源：评论

学校读者我要写书评

暂无评论

Non-separable spatiotemporal brain hemodynamics contain neural information

Non-separable spatiotemporal brain hemodynamics contain neur...

引用

International Workshop on machine learning and Interpretation in Neuroimaging, MLINI 2011, Held at Neural Information Processing, NIPS 2011

作者： Bießmann, Felix Murayama, Yusuke Logothetis, Nikos K. Müller, Klaus-Robert Meinecke, Frank C. Machine Learning Group Berlin Institute of Technology Germany Max-Planck Institute for Biological Cybernetics Tübingen Germany Division of Imaging Science and Biomedical Engineering University of Manchester United Kingdom Bernstein Center for Computational Neuroscience Berlin Germany

The goal of many functional Magnetic Resonance imaging (fMRI) studies is to infer neural activity from hemodynamic signals. Classical fMRI analysis approaches assume a canonical hemodynamic response function (HRF), which is identical in every voxel. Canonical HRFs imply space-time separability. Many studies explored the relevance of non-separable HRFs. These studies were focusing on the relationship between stimuli or electroencephalographic data and fMRI data. It is not clear from these studies whether non-separable spatiotemporal dynamics of fMRI signals contain neural information. This study provides direct empirical evidence that non-separable spatiotemporal deconvolutions of multivariate fMRI time series predict intracortical neural signals better than standard canonical HRF models. Our results demonstrate that there is more neural information in fMRI signals than detected by most analysis methods. © 2012 Springer-Verlag.

关键词： Hemodynamics

来源：评论

学校读者我要写书评

暂无评论

Probabilistic Decomposed Linear Dynamical Systems for Robust Discovery of Latent Neural Dynamics 38

Probabilistic Decomposed Linear Dynamical Systems for Robust...

引用

38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Chen, Yenho Mudrik, Noga Johnsen, Kyle A. Alagapan, Sankaraleengam Charles, Adam S. Rozell, Christopher J. Machine Learning Center Georgia Institute of Technology United States School of Electrical and Computer Engineering Georgia Institute of Technology United States Coulter Dept. of Biomedical Engineering Emory University Georgia Institute of Technology United States Department of Biomedical Engineering Mathematical Institute for Data Science Center for Imaging Science Kavli Neuroscience Discovery Institute Johns Hopkins University United States

Time-varying linear state-space models are powerful tools for obtaining mathematically interpretable representations of neural signals. For example, switching and decomposed models describe complex systems using latent variables that evolve according to simple locally linear dynamics. However, existing methods for latent variable estimation are not robust to dynamical noise and system nonlinearity due to noise-sensitive inference procedures and limited model formulations. This can lead to inconsistent results on signals with similar dynamics, limiting the model's ability to provide scientific insight. In this work, we address these limitations and propose a probabilistic approach to latent variable estimation in decomposed models that improves robustness against dynamical noise. Additionally, we introduce an extended latent dynamics model to improve robustness against system nonlinearities. We evaluate our approach on several synthetic dynamical systems, including an empirically-derived brain-computer interface experiment, and demonstrate more accurate latent variable inference in nonlinear systems with diverse noise conditions. Furthermore, we apply our method to a real-world clinical neurophysiology dataset, illustrating the ability to identify interpretable and coherent structure where previous models cannot. © 2024 Neural information processing systems foundation. All rights reserved.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Denoising Diffusion Models for Anomaly Localization in Medical Images

arXiv

引用

arXiv 2024年

作者： Bercea, Cosmin I. Cattin, Philippe C. Schnabel, Julia A. Wolleb, Julia School of Computation Information and Technology Technical University of Munich Germany Institute of Machine Learning in Biomedical Imaging and Helmholtz AI Helmholtz Munich Germany Department of Biomedical Engineering University of Basel Allschwil Switzerland Institute of Machine Learning in Biomedical Imaging Helmholtz Munich Germany School of Biomedical Engineering and Imaging Sciences King’s College London United Kingdom

This chapter explores anomaly localization in medical images using denoising diffusion models. After providing a brief methodological background of these models, including their application to image reconstruction and their conditioning using guidance mechanisms, we provide an overview of available datasets and evaluation metrics suitable for their application to anomaly localization in medical images. In this context, we discuss supervision schemes ranging from fully supervised segmentation to semi-supervised, weakly supervised, self-supervised, and unsupervised methods, and provide insights into the effectiveness and limitations of these approaches. Furthermore, we highlight open challenges in anomaly localization, including detection bias, domain shift, computational cost, and model interpretability. Our goal is to provide an overview of the current state of the art in the field, outline research gaps, and highlight the potential of diffusion models for robust anomaly localization in medical images. Copyright © 2024, The Authors. All rights reserved.

关键词： Image denoising

来源：评论

学校读者我要写书评

暂无评论

Multimodal spatial gradients to explain regional susceptibility to fibrillar tau in Alzheimer's disease

引用

Alzheimer's & dementia : the journal of the Alzheimer's Association 2025年第5期21卷 e70170页

作者： Ying Luan Lukai Zheng Jannis Denecke Amir Dehsarvi Sebastian N Roemer-Cassiano Anna Dewenter Anna Steward Sergey Shcherbinin Diana Otero Svaldi Vikas Kotari Ixavier Alonzo Higgins Michael J Pontecorvo Carolina Valentim Julia A Schnabel Francesco Paolo Casale Martin Dyrba Stefan Teipel Nicolai Franzmeier Michael Ewers Department of Radiology Zhongda Hospital School of Medicine Southeast University Nanjing China. Institute for Stroke and Dementia Research (ISD) University Hospital Ludwig Maximilian University (LMU) Munich Germany. Eli Lilly and Company Indianapolis Indiana USA. Institute of Machine Learning in Biomedical Imaging Helmholtz Munich Neuherberg Germany. TUM School of Computation Information and Technology & TUM Institute for Advanced Study Technical University of Munich Munich Germany. School of Biomedical Engineering and Imaging Sciences King's College London Strand London UK. German Center for Neurodegenerative Diseases (DZNE) Rostock Germany. Department of Psychosomatic Medicine Rostock University Medical Center Rostock Germany. Munich Cluster for Systems Neurology (SyNergy) Munich Germany. Department of Psychiatry and Neurochemistry The Sahlgrenska Academy Institute of Neuroscience and Physiology University of Gothenburg Gothenburg Sweden.

INTRODUCTION:In Alzheimer's disease (AD), fibrillar tau gradually progresses from initial seed to larger brain area. However, those brain properties underlying the region-dependent susceptibility to tau accumulation remain unclear. METHODS:We constructed multimodal spatial gradients to characterize molecular properties and connectomic architecture. A predictive model for regional tau deposition was developed by integrating embeddings in the principal gradients of global connectome gradients with gene expression, neurotransmitters, myelin, and amyloid-beta. The model was trained on amyloid-beta-positive participants from Alzheimer's Disease Neuroimaging Initiative (ADNI) and externally validated in independent datasets. RESULTS:The combination of gradients explained up to 77.7% of cross-sectional and 77.3% of longitudinal inter-regional variance of tau deposition. Gene set enrichment analysis of a major gene expression gradient points to synaptic transmission to confer increased susceptibility to tau. DISCUSSION:Our findings reveal a spatially heterogeneous molecular landscape shaping regional susceptibility to tau deposition, presenting a powerful system-level explanatory model of tau pathology in AD. HIGHLIGHTS:Spatial gradients of fundamental molecular brain properties associated with tau pathology. The explanatory power showed high consistency across studies. Genetic analyses suggested that synapse expression plays a vital role in tau accumulation.

关键词： Alzheimer's disease functional connectivity gene expression multimodal gradients neurotransmitters predictive model tau positron emission tomography

来源：评论

学校读者我要写书评

暂无评论

learning Physics-Inspired Regularization for Medical Image Registration with Hypernetworks

arXiv

引用

arXiv 2023年

作者： Reithmeir, Anna Schnabel, Julia A. Zimmer, Veronika A. School of Computation Information and Technology Technical University of Munich Munich Germany Munich Center for Machine Learning Munich Germany Institute of Machine Learning in Biomedical Imaging Helmholtz Munich Neuherberg Germany School of Biomedical Engineering and Imaging Sciences King’s College London London United Kingdom

Medical image registration aims to identify the spatial deformation between images of the same anatomical region and is fundamental to image-based diagnostics and therapy. To date, the majority of the deep learning-based registration methods employ regularizers that enforce global spatial smoothness, e.g., the diffusion regularizer. However, such regularizers are not tailored to the data and might not be capable of reflecting the complex underlying deformation. In contrast, physics-inspired regularizers promote physically plausible deformations. One such regularizer is the linear elastic regularizer, which models the deformation of elastic material. These regularizers are driven by parameters that define the material’s physical properties. For biological tissue, a wide range of estimations of such parameters can be found in the literature, and it remains an open challenge to identify suitable parameter values for successful registration. To overcome this problem and to incorporate physical properties into learning-based registration, we propose to use a hypernetwork that learns the effect of the physical parameters of a physics-inspired regularizer on the resulting spatial deformation field. In particular, we adapt the HyperMorph framework to learn the effect of the two elasticity parameters of the linear elastic regularizer. Our approach enables the efficient discovery of suitable, data-specific physical parameters at test time. To the best of our knowledge, we are the first to use a hypernetwork to learn physics-inspired regularization for medical image registration. We evaluate our approach on 3D intra-patient lung CT images. The results show that the linear elastic regularizer can yield comparable results to the diffusion regularizer in unsupervised learning-based registration while predicting deformations with fewer foldings. With our method, the adaptation of the physical parameters to the data can successfully be performed at test time. Our code is available

关键词： Physical properties

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：