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

共 2691 条记录，以下是621-630 订阅

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Structure-Consistent Restoration Network for Cataract Fundus image Enhancement 25th

Structure-Consistent Restoration Network for Cataract Fundus...

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

作者： Li, Heng Liu, Haofeng Fu, Huazhu Shu, Hai Zhao, Yitian Luo, Xiaoling Hu, Yan Liu, Jiang Southern Univ Sci & Technol Dept Comp Sci & Engn Shenzhen Peoples R China ASTAR IHPC Singapore Singapore New York Univ Sch Global Publ Hlth Dept Biostatist New York NY USA Chinese Acad Sci Cixi Inst Biomed Engn Beijing Peoples R China Shenzhen Peoples Hosp Shenzhen Peoples R China Southern Univ Sci & Technol Guangdong Prov Key Lab Brain Inspired Intelligent Shenzhen Peoples R China Southern Univ Sci & Technol Res Inst Trustworthy Autonomous Syst Shenzhen Peoples R China

ISBN: (纸本)9783031164347;9783031164330

Fundus photography is a routine examination in clinics to diagnose and monitor ocular diseases. However, for cataract patients, the fundus image always suffers quality degradation caused by the clouding lens. the degradation prevents reliable diagnosis by ophthalmologists or computer-aided systems. To improve the certainty in clinical diagnosis, restoration algorithms have been proposed to enhance the quality of fundus images. Unfortunately, challenges remain in the deployment of these algorithms, such as collecting sufficient training data and preserving retinal structures. In this paper, to circumvent the strict deployment requirement, a structure-consistent restoration network (SCR-Net) for cataract fundus images is developed from synthesized data that shares an identical structure. A cataract simulation model is firstly designed to collect synthesized cataract sets (SCS) formed by cataract fundus images sharing identical structures. then high-frequency components (HFCs) are extracted from the SCS to constrain structure consistency such that the structure preservation in SCR-Net is enforced. the experiments demonstrate the effectiveness of SCR-Net in the comparison with state-of-the-art methods and the follow-up clinical applications.

关键词： Cataract Fundus image enhancement High-frequency components Structure consistency

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Weakly-Supervised Biomechanically-Constrained CT/MRI Registration of the Spine 25th

Weakly-Supervised Biomechanically-Constrained CT/MRI Registr...

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

作者： Jian, Bailiang Azampour, Mohammad Farid De Benetti, Francesca Oberreuter, Johannes Bukas, Christina Gersing, Alexandra S. Foreman, Sarah C. Dietrich, Anna-Sophia Rischewski, Jon Kirschke, Jan S. Navab, Nassir Wendler, thomas Tech Univ Munich Chair Comp Aided Med Procedures & Augmented Real Garching Germany ScintHlth GmbH Munich Germany Reply SpA Munich Germany Helmholtz Zentrum Munchen Helmholtz AI Munich Germany Tech Univ Munich Dept Radiol Munich Germany Tech Univ Munich Dept Neuroradiol Munich Germany Johns Hopkins Univ Comp Aided Med Procedures Lab Lab Computat Sensing Robot Baltimore MD USA Sharif Univ Technol Dept Elect Engn Tehran Iran

ISBN: (纸本)9783031164460;9783031164453

Computed Tomography (CT) and Magnetic Resonance Imaging (MRI) are two of the most informative modalities in spinal diagnostics and treatment planning. CT is useful when analysing bony structures, while MRI gives information about the soft tissue. thus, fusing the information of both modalities can be very beneficial. Registration is the first step for this fusion. While the soft tissues around the vertebra are deformable, each vertebral body is constrained to move rigidly. We propose a weakly-supervised deep learning framework that preserves the rigidity and the volume of each vertebra while maximizing the accuracy of the registration. To achieve this goal, we introduce anatomy-aware losses for training the network. We specifically design these losses to depend only on the CT label maps since automatic vertebra segmentation in CT gives more accurate results contrary to MRI. We evaluate our method on an in-house dataset of 167 patients. Our results show that adding the anatomy-aware losses increases the plausibility of the inferred transformation while keeping the accuracy untouched.

关键词： CT/MRI registration Deep learning image registration Biomechanical constraints Spine

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Diffusion Deformable Model for 4D Temporal medical image Generation 25th

Diffusion Deformable Model for 4D Temporal Medical Image Gen...

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

作者： Kim, Boah Ye, Jong Chul Korea Adv Inst Sci & Technol Daejeon South Korea

ISBN: (纸本)9783031164316;9783031164309

Temporal volume images with 3D-ht (4D) information are often used in medical imaging to statistically analyze temporal dynamics or capture disease progression. Although deep-learning-based generative models for natural images have been extensively studied, approaches for temporal medical image generation such as 4D cardiac volume data are limited. In this work, we present a novel deep learning model that generates intermediate temporal volumes between source and target volumes. Specifically, we propose a diffusion deformable model (DDM) by adapting the denoising diffusion probabilistic model that has recently been widely investigated for realistic image generation. Our proposed DDM is composed of the diffusion and the deformation modules so that DDM can learn spatial deformation information between the source and target volumes and provide a latent code for generating intermediate frames along a geodesic path. Once our model is trained, the latent code estimated from the diffusion module is simply interpolated and fed into the deformation module, which enables DDM to generate temporal frames along the continuous trajectory while preserving the topology of the source image. We demonstrate the proposed method with the 4D cardiac MR image generation between the diastolic and systolic phases for each subject. Compared to the existing deformation methods, our DDM achieves high performance on temporal volume generation.

关键词： Deep learning medical image generation image deformation Diffusion model

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AlignFuse: Multi-feature Alignment and Fusion for medical image Segmentation 8

AlignFuse: Multi-feature Alignment and Fusion for Medical Im...

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8th international conference on Intelligent computing and Signal Processing, ICSP 2023

作者： Chao, Shengyang Liu, Zijun Huang, Lili Fu, Yanping Anhui University Anhui Provincial Key Laboratory of Multimodal Cognitive Computation School of Computer Science and Technology Anhui Hefei230601 China Institute of Artificial Intelligence Hefei Comprehensive National Science Center Anhui Hefei230088 China

ISBN: (纸本)9798350302455

U-Net, which is a U-shaped network, has made great progress on numerous medical image segmentation tracks. However, even if convolutional neural network (CNN) has a talent for extracting local information, it falls short in capturing global information due to the inherent drawback of the convolution operation. In contrast, whereby the design of tokenized images and self-attention mechanism, Transformer has a brilliant performance in modeling global contextual information but has a poor performance in grasping low-level details on account of the patch division which leads to ignorance of local correlation in each patch. In this paper, we develop a hybrid network structure, AlignFuse, which is in possession of the advantages of both CNN and Transformer. AlignFuse fits CNN and Transformer together in a parallel style, where Transformer leverages the high-level features and CNN pays more attention to details. Moreover, owing to the downsampling operation in CNNs, traditional context-free feature aggregation operations lead to misalignment issues between feature maps. To alleviate this problem, we propose to use Feature Aligned Fusion Module (FAFM) to effectively align and fuse the feature maps from CNN and Transformer in each decoder stage. We conduct experiments on Gland, breast cancer, and automatic cardiac segmentation, with competitive results obtained. © 2023 IEEE.

关键词： Convolution

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Longitudinal Segmentation of MS Lesions via Temporal Difference Weighting

Longitudinal Segmentation of MS Lesions via Temporal Diffe...

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Workshop on Longitudinal Disease Tracking and Modeling with medical images and Data, LDTM 2024, 5th international Workshop on Multiscale Multimodal medical Imaging, MMMI 2024, 1st Workshop on Machine Learning for Multimodal/-sensor Healthcare Data, ML4MHD2024 and Workshop on Multimodal Learning and Fusion Across Scales for Clinical Decision Support, ML-CDS 2024 held in conjunction with the 27th international conference on medical image computing and computer assisted intervention, MICCAI 2024

作者： Rokuss, Maximilian R. Kirchhoff, Yannick Roy, Saikat Kovacs, Balint Ulrich, Constantin Wald, Tassilo Zenk, Maximilian Denner, Stefan Isensee, Fabian Vollmuth, Philipp Kleesiek, Jens Maier-Hein, Klaus Division of Medical Image Computing Heidelberg Germany Faculty of Mathematics and Computer Science Heidelberg University Heidelberg Germany HIDSS4Health - Helmholtz Information and Data Science School for Health Karlsruhe Heidelberg Germany Medical Faculty Heidelberg Heidelberg University Heidelberg Germany Helmholtz Imaging German Cancer Research Center Heidelberg Germany Department of Neuroradiology Heidelberg University Hospital Heidelberg Germany Division for Computational Neuroimaging Department of Neuroradiology Heidelberg University Hospital Heidelberg Germany University Hospital Essen Essen Germany University Hospital Essen West German Cancer Center Essen Essen Germany Pattern Analysis and Learning Group Department of Radiation Oncology Heidelberg University Hospital Heidelberg Germany

ISBN: (纸本)9783031845246

Accurate segmentation of Multiple Sclerosis (MS) lesions in longitudinal MRI scans is crucial for monitoring disease progression and treatment efficacy. Although changes across time are taken into account when assessing images in clinical practice, most existing deep learning methods treat scans from different timepoints separately. Among studies utilizing longitudinal images, a simple channel-wise concatenation is the primary albeit suboptimal method employed to integrate timepoints. We introduce a novel approach that explicitly incorporates temporal differences between baseline and follow-up scans through a unique architectural inductive bias called Difference Weighting Block. It merges features from two timepoints, emphasizing changes between scans. We achieve superior scores in lesion segmentation (Dice Score, Hausdorff distance) as well as lesion detection (lesion-level F1 score) as compared to state-of-the-art longitudinal and single timepoint models across two datasets. Our code is made publicly available at: ***/MIC-DKFZ/Longitudinal-Difference-Weighting. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： image segmentation

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LIDP: A Lung image Dataset with Pathological Information for Lung Cancer Screening 1

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

作者： Shao, Yanbo Wang, Minghao Mai, Juanyun Fu, Xinliang Li, Mei Zheng, Jiayin Diao, Zhaoqi Yin, Airu Chen, Yulong Xiao, Jianyu You, Jian Yang, Yang Qiu, Xiangcheng Tao, Jinsheng Wang, Bo Ji, Hua Nankai Univ Adv Med Data Res Ctr Coll Comp Sci Tianjin Peoples R China Tianjin Med Univ Tianjin Lung Canc Ctr Dept Lung Canc Radiol Tianjin Peoples R China AnchorDx Med Co Guangzhou Peoples R China

ISBN: (数字)9783031164378

ISBN: (纸本)9783031164378;9783031164361

Lung cancer has been one of the greatest lethal cancers worldwide. Computed Tomograph (CT) makes it possible to diagnose lung cancer at an early stage, which can significantly reduce its mortality. In recent years, deep neural networks (DNN) have been widely used to improve the accuracy of benign and malignant pulmonary nodules classification. But the limitation of DNN approach is that AI model's performance and generalization highly depend on the size and quality of the training data. With our best knowledge, almost all existing public lung nodule datasets, e.g., LIDC-IDRI, obtain the crucial benign and malignant labels by radiographic analysis, instead of pathological examination. In this paper, we argue that, without pathology report and hence lack of labels' authenticity, LIDC-IDRI based machine-learning (ML) models are short of generalization. To prove our hypothesis, we introduce a new lung CT image dataset with pathological information (LIDP), for lung cancer screening. LIDP contains 990 samples, including 783 malignant samples and 207 benign samples. More critically, the labels of all samples have been all examined by pathological biopsy. We evaluate various of existing LIDC-based state-of-the-art (SOTA) models on LIDP. Our experimental results show the extreme poor generalization ability of existing SOTA models that are trained on LIDC-IDRI dataset. Our scientific conclusion is striking: the distributions of these datasets are significantly different. We claim that the LIDP dataset is a very valuable addition to the existing datasets like LIDC-IDRI. LIDP can be well used for independent testing or for training new ML models for lung cancer early detection.

关键词： Lung nodule Computed tomography (CT) computer-aided diagnosis (CAD) Benign and malignant classification

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Robust and Generalisable Segmentation of Subtle Epilepsy-Causing Lesions: A Graph Convolutional Approach 26th

Robust and Generalisable Segmentation of Subtle Epilepsy-Cau...

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26th international conference on medical image computing and computer-assisted intervention (MICCAI)

作者： Spitzer, Hannah Ripart, Mathilde Fawaz, Abdulah Williams, Logan Z. J. Robinson, Emma C. Iglesias, Juan Eugenio Adler, Sophie Wagstyl, Konrad Helmholtz Ctr Munich Inst Comp Biol D-85764 Munich Germany Ludwig Maximilians Univ Munchen LMU Univ Hosp Inst Stroke & Dementia Res ISD Munich Germany UCL Great Ormond St Inst Child Hlth Dept Dev Neurosci London WC1N 1EH England Kings Coll London Sch Biomed Engn & Imaging Sci Dept Biomed Engn London England MGH Martinos Ctr Biomed Imaging Boston MA USA Harvard Med Sch Boston MA 02115 USA MIT Comp Sci & Artificial Intelligence Lab Boston MA 02139 USA UCL Ctr Med Image Comp London WC1V 6LJ England UCL Wellcome Ctr Human Neuroimaging London WC1N 3AR England

ISBN: (纸本)9783031439926;9783031439933

Focal cortical dysplasia (FCD) is a leading cause of drugresistant focal epilepsy, which can be cured by surgery. these lesions are extremely subtle and often missed even by expert neuroradiologists. "Ground truth" manual lesion masks are therefore expensive, limited and have large inter-rater variability. Existing FCD detection methods are limited by high numbers of false positive predictions, primarily due to vertex- or patch-based approaches that lack whole-brain context. Here, we propose to approach the problem as semantic segmentation using graph convolutional networks (GCN), which allows our model to learn spatial relationships between brain regions. To address the specific challenges of FCD identification, our proposed model includes an auxiliary loss to predict distance from the lesion to reduce false positives and a weak supervision classification loss to facilitate learning from uncertain lesion masks. On a multi-centre dataset of 1015 participants with surface-based features and manual lesion masks from structural MRI data, the proposed GCN achieved an AUC of 0.74, a significant improvement against a previously used vertex-wise multi-layer perceptron (MLP) classifier (AUC 0.64). With sensitivity thresholded at 67%, the GCN had a specificity of 71% in comparison to 49% when using the MLP. this improvement in specificity is vital for clinical integration of lesion-detection tools into the radiological workflow, through increasing clinical confidence in the use of AI radiological adjuncts and reducing the number of areas requiring expert review.

关键词： Graph Convolutional Network lesion segmentation structural MRI

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image Quality Assessment with Model Fusion for Ultra-widefield Fundus 1st

Image Quality Assessment with Model Fusion for Ultra-widef...

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1st MICCAI Challenge on Ultra-Widefield Fundus Imaging for Diabetic Retinopathy, UWF4DR 2024, Held in Conjunction with 27th international conference on medical image computing and computer assisted intervention, MICCAI 2024

作者： Yang, Bo Yan, Yangyang Liu, Di Li, Qicheng Zhang, Yue AIFUTURE Lab Shenzhou Digital Building No. 16 Suzhou Street Haidian District Beijing100085 China Wonderful Things Lab No. 1 Xingfu Street Huairou Science City Huairou District Beijing101407 China College of Computer Science Nankai University No. 38 Tongyan Road Haihe Education Park Jinnan District Tianjin China Department of Ophthalmology Beijing Hospital National Center of Gerontology Institute of Geriatric Medicine Chinese Academy of Medical Sciences Beijing China Graduate School of Peking Union Medical College Beijing China

ISBN: (纸本)9783031893872

the study addresses the challenge of image quality assessment in ultra-widefield fundus imaging (UWF) for diabetic retinopathy (DR) diagnosis. With the rise of UWF, which offers a broader field of view and higher resolution than traditional methods, the need for accurate image quality assessment is paramount. the research focuses on the limitations of UWF image quality assessment, which is still in its infancy compared to color fundus photography (CFP). the study utilizes a dataset of 434 UWF images, categorized as "gradable" and "ungradable" quality, to develop and validate a deep learning model. the model incorporates data augmentation and pseudo-labeling to improve generalization and robustness. By integrating various lightweight models, the study achieves a significant boost in performance, reducing inference time while securing a first-place ranking in a competition. this research underscores the importance of UWF in DR diagnosis and the necessity for robust image quality assessment to ensure accurate lesion identification and early intervention. As the top team, we have open-sourced our code on Github at https://***/yeungbo/MICCAI-UWF4DR-2024. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Deep learning

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ReMix: A General and Efficient Framework for Multiple Instance Learning Based Whole Slide image Classification 25th

ReMix: A General and Efficient Framework for Multiple Instan...

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

作者： Yang, Jiawei Chen, Hanbo Zhao, Yu Yang, Fan Zhang, Yao He, Lei Yao, Jianhua Tencent Al Lab Bellevue WA USA Univ Calif Los Angeles Los Angeles CA USA Chinese Acad Sci Inst Comp Technol Beijing Peoples R China Univ Chinese Acad Sci Beijing Peoples R China

ISBN: (纸本)9783031164347;9783031164330

Whole slide image (WSI) classification often relies on deep weakly supervised multiple instance learning (MIL) methods to handle gigapixel resolution images and slide-level labels. Yet the decent performance of deep learning comes from harnessing massive datasets and diverse samples, urging the need for efficient training pipelines for scaling to large datasets and data augmentation techniques for diversifying samples. However, current MIL-based WSI classification pipelines are memory-expensive and computation-inefficient since they usually assemble tens of thousands of patches as bags for computation. On the other hand, despite their popularity in other tasks, data augmentations are unexplored for WSI MIL frameworks. To address them, we propose ReMix, a general and efficient framework for MIL based WSI classification. It comprises two steps: reduce and mix. First, it reduces the number of instances in WSI bags by substituting instances with instance prototypes, i.e., patch cluster centroids. then, we propose a "Mix-the-bag" augmentation that contains four online, stochastic and flexible latent space augmentations. It brings diverse and reliable class-identity-preserving semantic changes in the latent space while enforcing semantic-perturbation invariance. We evaluate ReMix on two public datasets with two state-of-the-art MIL methods. In our experiments, consistent improvements in precision, accuracy, and recall have been achieved but with orders of magnitude reduced training time and memory consumption, demonstrating ReMix's effectiveness and efficiency. Code is available at https://***/TencentAILabHealthcare/ReMix.

关键词： Multiple instance learning Whole slide image Data augmentation Deep learning

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A Self-attentive Meta-learning Approach for image-Based Few-Shot Disease Detection 1

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1st international Workshop on Resource-Efficient medical image Analysis (REMIA) / 25th international conference on medical image computing and computer-assisted intervention (MICCAI)

作者： Ouahab, Achraf Ben-Ahmed, Olfa Fernandez-Maloigne, Christine Univ Poitiers XLIM Res Inst URM CNRS 7252 Poitiers France

ISBN: (数字)9783031168765

ISBN: (纸本)9783031168765;9783031168758

In this paper, we propose a few-shot medical images classification framework for low prevalence disease detection. the proposed method leans to transfer medical knowledge from common diseases to low prevalence cases using meta-learning. Indeed, compared to natural images, medical images vary less diversely from one image to another, with complex patterns and less semantic information. Hence, extracting clinically relevant features and learning a disease-specific signature from few images is challenging. Inspired by clinician's cognitive and visual diagnosis, we integrate an attention mechanism in the meta-learning process and we revised the meta-loss function to learn clinically disease-specific features over tasks. the proposed approach has been evaluated on two image-based diagnosis problems namely low prevalence skin and low prevalence thorax diseases diagnosis. We obtained respectively for those two use-cases 84.3% and 73.4% of average AUC in 2-way 5-shot classification setting. Obtained results demonstrate the effectiveness of the proposed framework compared to baselines and state-of the-art few-shot disease detection methods.

关键词： Few-Shot Learning Meta-learning Visual attention Rare/Low prevalence disease diagnosis

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