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检索条件"任意字段=8th International Conference on Medical Image Computing and Computer-Assisted Intervention"

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Agent with Tangent-Based Formulation and Anatomical Perception for Standard Plane Localization in 3D Ultrasound 1

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25th international conference on medical image computing and computer assisted intervention (MICCAI)

作者： Zou, Yuxin Dou, Haoran Huang, Yuhao Yang, Xin Qian, Jikuan Zhen, Chaojiong Ji, Xiaodan Ravikumar, Nishant Chen, Guoqiang Huang, Weijun Frangi, Alejandro F. Ni, Dong Shenzhen Univ Hlth Sci Ctr Sch Biomed Engn Natl Reg Key Technol Engn Lab Med Ultrasound Shenzhen Peoples R China Shenzhen Univ Med Ultrasound Image Comp MUSIC Lab Shenzhen Peoples R China Shenzhen Univ Marshall Lab Biomed Engn Shenzhen Peoples R China Univ Leeds Sch Comp Ctr Computat Imaging & Simulat Technol Biomed CIS Leeds W Yorkshire England Univ Leeds Sch Med Leeds Inst Cardiovasc & Metab Med LICAMM Biomed Imaging Dept Leeds W Yorkshire England Shenzhen RayShape Med Technol Co Ltd Shenzhen Peoples R China First Peoples Hosp Foshan Dept Ultrasound Foshan Peoples R China Katholieke Univ Leuven Dept Cardiovasc Sci Leuven Belgium Katholieke Univ Leuven Dept Elect Engn Leuven Belgium Alan Turing Inst London England

ISBN: (数字)9783031164408

ISBN: (纸本)9783031164408;9783031164392

Standard plane (SP) localization is essential in routine clinical ultrasound (US) diagnosis. Compared to 2D US, 3D US can acquire multiple view planes in one scan and provide complete anatomy with the addition of coronal plane. However, manually navigating SPs in 3D US is laborious and biased due to the orientation variability and huge search space. In this study, we introduce a novel reinforcement learning (RL) framework for automatic SP localization in 3D US. Our contribution is three-fold. First, we formulate SP localization in 3D US as a tangent-point-based problem in RL to restructure the action space and significantly reduce the search space. Second, we design an auxiliary task learning strategy to enhance the model's ability to recognize subtle differences crossing Non-SPs and SPs in plane search. Finally, we propose a spatial-anatomical reward to effectively guide learning trajectories by exploiting spatial and anatomical information simultaneously. We explore the efficacy of our approach on localizing four SPs on uterus and fetal brain datasets. the experiments indicate that our approach achieves a high localization accuracy as well as robust performance.

关键词： Reinforcement learning Standard plane localization Ultrasound

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DDPNet: A Novel Dual-Domain Parallel Network for Low-Dose CT Reconstruction 25th

DDPNet: A Novel Dual-Domain Parallel Network for Low-Dose CT...

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25th international conference on medical image computing and computer assisted intervention (MICCAI)

作者： Ge, Rongjun He, Yuting Xia, Cong Sun, Hailong Zhang, Yikun Hu, Dianlin Chen, Sijie Chen, Yang Li, Shuo Zhang, Daoqiang Nanjing Univ Aeronaut & Astronaut Coll Comp Sci & Technol MIIT Key Lab Pattern Anal & Machine Intelligence Nanjing Peoples R China Southeast Univ Sch Comp Sci & Engn Lab Image Sci & Technol Nanjing Peoples R China Southeast Univ Zhongda Hosp Dept Radiol Jiangsu Key Lab Mol & Funct ImagingMed Sch Nanjing Peoples R China Southeast Univ Key Lab Comp Network & Informat Integrat Minist Educ Nanjing Peoples R China Nanjing Med Univ Dept Radiol Nanjing Peoples R China Western Univ Digital Imaging Grp London Dept Med Imaging & Med Biophys London ON Canada

ISBN: (纸本)9783031164460;9783031164453

the low-dose computed tomography (CT) scan is clinically significant to reduce the radiation risk for radiologists and patients, especially in repeative examination. However, it inherently introduce more noise due to the radiation exposure. Nowadays, the existing LDCT reconstruction methods mainly focus on single domain of sinogram and image, or their cascade. But there still has limitations that the insufficient information in single domain, and the accumulation error in cascaded dual-domain. though dual-domain can provide more information in reconstruction, how to effectively organize dual-domain and make complementary fusion still remain open challenges. Besides, the details inter-pixel in reconstructed LDCT is essential for structure maintenance. We propose a Dual-domain parallel network (DDPNet) for high-quality reconstruction from widely accessible LDCT, which is the first powerful work making parallel optimization between sinogram and image domains to eliminate the accumulation error, and fusing dual-domain reconstructions for complementary. DDPNet is constituted by three special designs: 1) a dual-domain parallel architecture to make joint mutual optimization with interactive information flow;2) a unified fusion block to complement multi-results and further refine final reconstruction with triple-cross attention;3) a pair of coupled patch-discriminators to drive the reconstruction towards both realistic anatomic content and accurate inner-details with image-based and inter-pixel gradient-based adversarial constraints, respectively. the extensive experiments validated on public available Mayo dataset show that our DDPNet achieves promising PSNR up to 45.29 dB, SSIM up to 98.24%, and MAE down to 13.54 HU in quantitative evaluations, as well as gains high-quality readable visualizations in qualitative assessments. All of these findings suggest that our method has great clinical potential in CT imaging.

关键词： Dual-domain parallel network Unified fusion block Coupled patch-discriminators Low-dose computed tomography

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A Soft computing Based Approach for Pixel Labelling on 2D images Using Fine Tuned R-CNN 10th

A Soft Computing Based Approach for Pixel Labelling on 2D ...

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10th international conference on Innovations in computer Science and Engineering, ICICSE 2022

作者： Arappal, Nedumaran Singh, Ajeet Saidulu, D. School of Electrical and Computer Engineering Kombolcha Institute of Technology Wollo University Dessie Ethiopia School of Computer and Information Sciences University of Hyderabad Hyderabad Telangana500034 India Department of Information Technology Guru Nanak Institutions Technical Campus Hyderabad Telangana India

ISBN: (纸本)9789811974540

Nowadays Artificial Intelligence (AI) is an integral component of the core business model of several organisations and a crucial strategic component of many industries’ strategies such as—healthcare, finance, retail education etc. on a global scale. Machine Learning (ML) is a technology to achieve AI. Machine learning methods utilize a variety of statistical, A vast, unstructured, and complicated dataset may be analysed using probabilistic and optimization approaches to learn from the past and find relevant trends. Automated oriented text categorization, network intervention detection, spam email filtering, credit card deceit detection, consumer purchase behaviour detection, and manufacturing process optimization are just a few of the many uses for these algorithms, image categorization, object recognition and disease modelling. Allocating a different class oriented label to every pixel of a particular image is certainly one of the significant steps in constructing complex automated systems like driver-less vehicles/drones, human-friendly oriented robotic machines, robot-assisted medical procedure, and savvy military systems. Pixel labeling’s computational purpose is to give each individual pixel in an imagery a class label. It is critical long-range (pixel) label dependencies in a model must be captured pictures to provide strong visual coherence and high class accuracy. this may be accomplished in a feed-forward architecture by considering a suitably big input to the context patch surrounding each individual pixel to be labelled. We present a strategy in this chapter that consists of an ideally finetuned recurrent convolutional neural network that mainly allows us to incorporate a broad input context without restricting the model’s capacity. Our solution does not rely on any such segmentation techniques or task-specific characteristics, in contrast to the majority of the conventional methods. the system is trained on raw pixels from beginning to end, and it models

关键词： Pixels

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A Transfer Learning Based Model Ensemble Method for image Quality Assessment and Diabetic Retinopathy Grading 25th

A Transfer Learning Based Model Ensemble Method for Image Q...

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25th international conference on medical image computing and computer-assisted intervention , MICCAI 2022

作者： Yan, Xiaochao Li, Zhaopei Wen, Jianhui Pan, Lin College of Physics and Information Engineering Fuzhou University Fuzhou350108 China

ISBN: (纸本)9783031336577

Diabetic retinopathy (DR) is a chronic complication of diabetes that damages the retina and is one of the leading causes of blindness. In the process of diabetic retinopathy analysis, it is necessary to first assess the quality of images and select the images with better imaging quality. then DR analysis, such as DR grading, is performed. therefore, it is crucial to implement a flexible and robust method to achieve automatic image quality assessment and DR grading. In deep learning, due to the high complexity, weak individual differences, and noise interference of ultra-wide optical coherence tomography angiography (UW-OCTA) images, individual classification networks have not been able to achieve satisfactory accuracy on such tasks and do not generalize well. therefore, in this work, we use multiple models ensemble methods, by ensemble different baseline networks of RegNet and EfficientNetV2, which can simply and significantly improve the prediction accuracy and robustness. A transfer learning based solution is proposed for the problem of insufficient diabetic image data for retinopathy. After doing feature enhancement on the images, the UW-OCTA image task will be fine-tuned by combining the network pre-trained with imageNet data. our method achieves a quadratic weighted kappa of 0.778 and AUC of 0.887 in image quality assessment (IQA) and 0.807 kappa and AUC of 0.875 in diabetic retinopathy grading. © 2023, the Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Optical tomography

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DeepMIF: Deep Learning Based Cell Profiling for Multispectral Immunofluorescence images with Graphical User Interface 1

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25th international conference on medical image computing and computer assisted intervention (MICCAI)

作者： Hagos, Yeman Brhane Akarca, Ayse U. Ramsay, Alan Rossi, Riccardo L. Pomplun, Sabine Moioli, Alessia Gianatti, Andrea Mcnamara, Christopher Rambaldi, Alessandro Quezada, Sergio A. Linch, David Gritti, Giuseppe Marafioti, Teresa Yuan, Yinyin Inst Canc Res Ctr Evolut & Canc Div Mol Pathol London England UCL Canc Inst London England Univ Coll London Hosp Dept Histopathol London England Ist Nazl Genet Molecolare Bioinformat Milan Italy Osped Papa Giovanni XXIII Hematol Unit Bergamo Italy Osped Papa Giovanni XXIII Pathol Unit Bergamo Italy Univ Milan Dept Oncol & Hematol Oncol Milan Italy UCL Canc Inst Canc Immunol Unit London England UCL Canc Inst Res Dept Haematol London England Univ Coll London Hosp Dept Haematol London England

ISBN: (数字)9783031164408

ISBN: (纸本)9783031164408;9783031164392

Multispectral immunofluorescence (M-IF) analysis is used to investigate the cellular landscape of tissue sections and spatial interaction of cells. However, complex makeup of markers in the images hinders the accurate quantification of cell phenotypes. We developed DeepMIF, a new deep learning (DL) based tool with a graphical user interface (GUI) to detect and quantify cell phenotypes on M-IF images, and visualize whole slide image (WSI) and cell phenotypes. To identify cell phenotypes, we detected cells on the deconvoluted images followed by coexpression analysis to classify cells expressing single or multiple markers. We trained, tested and validated our model on > 50k expert single-cell annotations from multiple immune panels on 15 samples of follicular lymphoma patients. Our algorithm obtained a cell classification accuracy and area under the curve (AUC) >= 0.98 on an independent validation panel. the cell phenotype identification took on average 27.5 min per WSI, and rendering of the WSI took on average 0.07 minutes. DeepMIF is optimized to run on local computers or high-performance clusters independent of the host platform. these suggest that the DeepMIF is an accurate and efficient tool for the analysis and visualization of M-IF images, leading to the identification of novel prognostic cell phenotypes in tumours.

关键词： Deep learning Multispectral immunofluorescence Cell detection Cell classification image viewer

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Multi-task RetinaNet for Mitosis Detection 25th

Multi-task RetinaNet for Mitosis Detection

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25th international conference on medical image computing and computer-assisted intervention , MICCAI 2022

作者： Wang, Ziyue Chen, Yang Fang, Zijie Bian, Hao Zhang, Yongbing Department of Computer Science Harbin Institute of Technology Harbin China Shenzhen International Graduate School Tsinghua University Beijing China

ISBN: (纸本)9783031336577

the count of mitotic cells is a key feature in tumor diagnosis. However, due to the variability of mitotic cell morphology, detecting mitotic cells in tumor tissues is a highly challenging task. At the same time, the performance of the trained models often declines when there is a vast difference between the source domain and the target domain. (i.e., the different tumor types and scanners). therefore, it is necessary to develop algorithms for detecting mitotic cells with robustness in domain shift scenarios. Our work proposes a foreground detection and tumor classification task based on the baseline (Retinanet) and utilizes data augmentation to improve our model’s detection ability and domain generalization performance. We achieve excellent performance on the challenging preliminary test dataset (F1 score: 0.5809) and the Final test dataset (F1:0.6300). © 2023, the Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Object detection

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Automatic image Quality Assessment and DR Grading Method Based on Convolutional Neural Network 25th

Automatic Image Quality Assessment and DR Grading Method Ba...

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25th international conference on medical image computing and computer-assisted intervention , MICCAI 2022

作者： Zhang, Wen Chen, Hao Li, Daisong Zheng, Shaohua College of Physics and Information Engineering Fuzhou University FJ Fuzhou350108 China

ISBN: (纸本)9783031336577

Diabetic retinopathy (DR) is a common ocular complication in diabetic patients and is a major cause of blindness in the population. DR often leads to progressive changes in the structure of the vascular system and causes abnormalities. In the process of DR analysis, the image quality needs to be evaluated first, and images with better imaging quality are selected, followed by value-added proliferative diabetic retinopathy (PDR) detection. therefore, in this paper, the MixNet classification network was first used for image quality assessment (IQA), and then the ResNet50-CMBA network was used for DR grading of images, and both networks were combined with a k-fold cross-validation strategy. We evaluated our method at the 2022 Diabetic Retinopathy Analysis Challenge (DRAC), where image quality was evaluated on 1103 ultra-wide optical coherence tomography angiography (UW-OCTA) images and DR grading was detected on 997 UW-OCTA images. Our method achieved a Quadratic Weight Kappa of 0.7547 and 0.8010 in the test cases, respectively. © 2023, the Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Optical tomography

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Deep Learning and Radiomics Based PET/CT image Feature Extraction from Auto Segmented Tumor Volumes for Recurrence-Free Survival Prediction in Oropharyngeal Cancer Patients 3rd

Deep Learning and Radiomics Based PET/CT Image Feature Extra...

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3rd 3D Head and Neck Tumor Segmentation in PET/CT Challenge, HECKTOR 2022, held in Conjunction with the 25th international conference on medical image computing and computer assisted intervention, MICCAI 2022

作者： Ma, Baoqiang Li, Yan Chu, Hung Tang, Wei De la O Arévalo, Luis Ricardo Guo, Jiapan van Ooijen, Peter Both, Stefan Langendijk, Johannes Albertus van Dijk, Lisanne V. Sijtsema, Nanna Maria Department of Radiation Oncology University of Groningen University Medical Center Groningen Groningen Netherlands University of Groningen University Medical Center Groningen Groningen Netherlands Center for Information Technology University of Groningen Groningen Netherlands Department of Neurology University of Groningen University Medical Center Groningen Groningen Netherlands

ISBN: (纸本)9783031274190

Aim: the development and evaluation of deep learning (DL) and radiomics based models for recurrence-free survival (RFS) prediction in oropharyngeal squamous cell carcinoma (OPSCC) patients based on clinical features, positron emission tomography (PET) and computed tomography (CT) scans and GTV (Gross Tumor Volume) contours of primary tumors and pathological lymph nodes. Methods: A DL auto-segmentation algorithm generated the GTV contours (task 1) that were used for imaging biomarkers (IBMs) extraction and as input for the DL model. Multivariable cox regression analysis was used to develop radiomics models based on clinical and IBMs features. Clinical features with a significant correlation with the endpoint in a univariable analysis were selected. the most promising IBMs were selected by forward selection in 1000 times bootstrap resampling in five-fold cross validation. To optimize the DL models, different combinations of clinical features, PET/CT imaging, GTV contours, the selected radiomics features and the radiomics model predictions were used as input. the combination with the best average performance in five-fold cross validation was taken as the final input for the DL model. the final prediction in the test set, was an ensemble average of the predictions from the five models for the different folds. Results: the average C-index in the five-fold cross validation of the radiomics model and the DL model were 0.7069 and 0.7575, respectively. the radiomics and final DL models showed C-indexes of 0.6683 and 0.6455, respectively in the test set. Conclusion: the radiomics model for recurrence free survival prediction based on clinical, GTV and CT image features showed the best predictive performance in the test set with a C-index of 0.6683. © 2023, the Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Forecasting

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Automated Analysis of Diabetic Retinopathy Using Vessel Segmentation Maps as Inductive Bias 25th

Automated Analysis of Diabetic Retinopathy Using Vessel Seg...

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25th international conference on medical image computing and computer-assisted intervention , MICCAI 2022

作者： Kreitner, Linus Ezhov, Ivan Rueckert, Daniel Paetzold, Johannes C. Menten, Martin J. Lab for AI in Medicine Klinikum rechts der Isar Technical University of Munich Munich Germany BioMedIA Department of Computing Imperial College London London United Kingdom ITERM Institute Helmholtz Zentrum Muenchen Neuherberg Germany

ISBN: (纸本)9783031336577

Recent studies suggest that early stages of diabetic retinopathy (DR) can be diagnosed by monitoring vascular changes in the deep vascular complex. In this work, we investigate a novel method for automated DR grading based on ultra-wide optical coherence tomography angiography (UW-OCTA) images. Our work combines OCTA scans with their vessel segmentations, which then serve as inputs to task specific networks for lesion segmentation, image quality assessment and DR grading. For this, we generate synthetic OCTA images to train a segmentation network that can be directly applied on real OCTA data. We test our approach on MICCAI 2022’s DR analysis challenge (DRAC). In our experiments, the proposed method performs equally well as the baseline model. © 2023, the Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Optical tomography

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1st international Workshop on Connectomics in NeuroImaging, CNI 2017 held in conjunction with the 20th international conference on medical image computing and computer assisted intervention, MICCAI 2017

1st International Workshop on Connectomics in NeuroImaging, ...

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1st international Workshop on Connectomics in NeuroImaging, CNI 2017 held in conjunction with the 20th international conference on medical image computing and computer assisted intervention, MICCAI 2017

ISBN: (纸本)9783319671581

the proceedings contain 19 papers. the special focus in this conference is on Connectomics in NeuroImaging and medical image computing and computer assisted intervention. the topics include: Connectome of autistic brains, global versus local characterization;constructing multi-frequency high-order functional connectivity network for diagnosis of mild cognitive impairment;consciousness level and recovery outcome prediction using high-order brain functional connectivity network;discriminative log-euclidean kernels for learning on brain networks;interactive computation and visualization of structural connectomes in real-time;pairing-based ensemble classifier learning using convolutional brain multiplexes and multi-view brain networks for early dementia diagnosis;high-order connectomic manifold learning for autistic brain state identification;a unified bayesian approach to extract network-based functional differences from a heterogeneous patient cohort;a convolutional neural network for calculating functional connectivity from functional MRI;identifying subnetwork fingerprints in structural connectomes;a simple and efficient cylinder imposter approach to visualize DTI fiber tracts;revisiting abnormalities in brain network architecture underlying autism using topology-inspired statistical inference;early brain functional segregation and integration predict later cognitive performance;measuring brain connectivity via shape analysis of fMRI time courses and spectra;topological network analysis of electroencephalographic power maps;region-wise stochastic pattern modeling for autism spectrum disorder identification and temporal dynamics analysis;a whole-brain reconstruction approach for FOD modeling from multi-shell diffusion MRI and topological distances between brain networks.

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