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检索条件"任意字段=3rd International Conference on Medical Image Computing and Computer-Assisted Intervention"

共 4110 条记录，以下是51-60 订阅

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Importance of Aligning Training Strategy with Evaluation for Diffusion Models in 3D Multiclass Segmentation 3rd

Importance of Aligning Training Strategy with Evaluation for...

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3rd Workshop on Deep Generative Models for medical image computing and computer assisted intervention (DGM4MICCAI) at the 26th international conference on medical image computing and computer assisted intervention (MICCAI)

作者： Fu, Yunguan Li, Yiwen Saeed, Shaheer U. Clarkson, Matthew J. Hu, Yipeng UCL London England InstaDeep London England Univ Oxford Oxford England

ISBN: (纸本)9783031537660;9783031537677

Recently, denoising diffusion probabilistic models (DDPM) have been applied to image segmentation by generating segmentation masks conditioned on images, while the applications were mainly limited to 2D networks without exploiting potential benefits from the 3D formulation. In this work, we studied the DDPM-based segmentation model for 3D multiclass segmentation on two large multiclass data sets (prostate MR and abdominal CT). We observed that the difference between training and test methods led to inferior performance for existing DDPM methods. To mitigate the inconsistency, we proposed a recycling method which generated corrupted masks based on the model's prediction at a previous time step instead of using ground truth. The proposed method achieved statistically significantly improved performance compared to existing DDPMs, independent of a number of other techniques for reducing train-test discrepancy, including performing mask prediction, using Dice loss, and reducing the number of diffusion time steps during training. The performance of diffusion models was also competitive and visually similar to non-diffusion-based U-net, within the same compute budget. The JAX-based diffusion framework has been released at https:// ***/mathpluscode/ImgX-DiffSeg.

关键词： image Segmentation Diffusion Model Prostate MR Abdominal CT

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Brain Tumor Segmentation of MRI images with U-Net and DeepLabV3+ 3

Brain Tumor Segmentation of MRI images with U-Net and DeepLa...

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3rd international conference on computing and Machine Intelligence (ICMI)

作者： Akagic, Amila Kapo, Medina Kandic, Elma Becirovic, Merjem Kadric, Nerma Univ Sarajevo Fac Elect Engn Sarajevo 71000 Bosnia & Herceg

ISBN: (纸本)9798350372977;9798350372984

In recent years, notable advancements have been made in medical imaging technology, with Magnetic Resonance Imaging (MRI) assuming a pivotal role in the diagnosis of brain tumors. Despite these advancements, medical image segmentation continues to pose a formidable challenge, as highlighted by various factors documented in existing literature. This study delves into the cutting-edge developments in Deep Learning for semantic segmentation, specifically concentrating on the precise identification of brain tumor pixels in 2D images. Employing U-Net and DeepLabV3+ architectures, the research provides experimental evidence that underscores the unparalleled performance of DeepLabV3+ with the Binary Cross Entropy loss function, offering valuable insights for enhancing the accuracy of brain tumor segmentation in medical imaging.

关键词： Biomedical Imaging medical image Segmentation MRI Brain tumor computer Vision Semantic Segmentation

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3rd international Workshop and Challenge on Computational Methods and Clinical Applications for Spine Imaging, CSI 2015 held in conjunction with 18th international conference on medical image computing and computer-assisted intervention, MICCAI 2015

3rd International Workshop and Challenge on Computational Me...

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3rd international Workshop and Challenge on Computational Methods and Clinical Applications for Spine Imaging, CSI 2015 held in conjunction with 18th international conference on medical image computing and computer-assisted intervention, MICCAI 2015

ISBN: (纸本)9783319418261

The proceedings contain 15 papers. The special focus in this conference is on Clinical Applications for Spine Imaging and Automatic Intervertebral Disc Localization. The topics include: Automated pedicle screw size and trajectory planning by maximization of fastening strength;automatic modic changes classification in spinal MRI;patient registration via topologically encoded depth projection images in spine surgery;automatic localisation of vertebrae in DXA images using random forest regression voting;robust ct to us 3D-3D registration by using principal component analysis and kalman filtering;cortical bone thickness estimation in ct images;multi-atlas segmentation with joint label fusion of osteoporotic vertebral compression fractures on ct;statistical shape model construction of lumbar vertebrae and intervertebral discs in segmentation for discectomy surgery simulation;automatic intervertebral discs localization and segmentation;segmentation of intervertebral discs in 3D MRI data using multi-atlas based registration;deformable model-based segmentation of intervertebral discs from MR spine images by using the SSC descriptor;3d intervertebral disc segmentation from MRI using supervoxel-based CRFS;automatic intervertebral disc localization and segmentation in 3D MR images based on regression forests and active contours;localization and segmentation of 3D intervertebral discs from MR images via a learning based method and automated intervertebral disc segmentation using probabilistic shape estimation and active shape models.

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A brief discussion on edge computing and smart medical care 24

A brief discussion on edge computing and smart medical care

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3rd international conference on Public Health and Data Science, ICPHDS 2024

作者： Chen, Peihua Huang, Xueping First Clinical Medical College School of Information and Engineering Wenzhou Medical University Zhejiang Wenzhou China

ISBN: (纸本)9798400711671

This paper introduces the basic concepts of edge computing, the history of the rise of edge computing, and the common application areas of edge computing. It focuses on analyzing the technical advantages of edge computing in the field of smart healthcare. It lists a variety of typical application scenarios such as remote monitoring, mobile intelligent image analysis, smart ward monitoring, emergency support, drug management, and robot-assisted surgery. It introduces the common intelligent data acquisition terminal devices involved in medical edge computing systems, the mainstream manufacturers of professional edge computing equipment in the medical field, and typical representative products. It demonstrates the potential value of this technology in improving the quality of medical services, response speed, and data privacy protection. It looks forward to the prospects of the combination of edge computing and smart healthcare, and discusses the challenges that may be faced by the deep integration of edge computing and smart healthcare in the future. © 2024 Copyright held by the owner/author(s).

关键词： Electronic health record

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Investigating Data Memorization in 3D Latent Diffusion Models for medical image Synthesis 3rd

Investigating Data Memorization in 3D Latent Diffusion Model...

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作者： Dar, Salman Ul Hassan Ghanaat, Arman Kahmann, Jannik Ayx, Isabelle Papavassiliu, Theano Schoenberg, Stefan O. Engelhardt, Sandy Heidelberg Univ Hosp Dept Internal Med 3 Grp Artificial Intelligence CardiovascularMed D-69120 Heidelberg Germany AI Hlth Innovat Cluster Heidelberg Germany German Ctr Cardiovasc Res DZHK Partner Site Heidelberg Mannheim Heidelberg Germany Heidelberg Univ Univ Med Ctr Mannheim Dept Radiol & Nucl Med Theodor Kutzer Ufer 1-3 D-68167 Mannheim Germany Univ Med Ctr Mannheim Dept Med Cardiol 1 Theodor Kutzer Ufer 1-3 D-68167 Mannheim Germany

ISBN: (纸本)9783031537660;9783031537677

Generative latent diffusion models have been established as state-of-the-art in data generation. One promising application is generation of realistic synthetic medical imaging data for open data sharing without compromising patient privacy. Despite the promise, the capacity of such models to memorize sensitive patient training data and synthesize samples showing high resemblance to training data samples is relatively unexplored. Here, we assess the memorization capacity of 3D latent diffusion models on photon-counting coronary computed tomography angiography and knee magnetic resonance imaging datasets. To detect potential memorization of training samples, we utilize self-supervised models based on contrastive learning. Our results suggest that such latent diffusion models indeed memorize training data, and there is a dire need for devising strategies to mitigate memorization.

关键词： Deep generative models Latent diffusion Data memorization Patient privacy Contrastive learning

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4th Workshop on Deep Generative Models for medical image computing and computer assisted intervention, DGM4MICCAI 2024, held in Conjunction with 27th international conference on medical image computing and computer assisted intervention, MICCAI 2024

4th Workshop on Deep Generative Models for Medical Image Com...

引用

4th Workshop on Deep Generative Models for medical image computing and computer assisted intervention, DGM4MICCAI 2024, held in Conjunction with 27th international conference on medical image computing and computer assisted intervention, MICCAI 2024

ISBN: (纸本)9783031727436

The proceedings contain 21 papers. The special focus in this conference is on Deep Generative Models for medical image computing and computer assisted intervention. The topics include: Energy-Based Prior Latent Space Diffusion Model for Reconstruction of Lumbar Vertebrae from Thick Slice MRI;anatomically-Guided Inpainting for Local Synthesis of Normal Chest Radiographs;enhancing Cross-Modal medical image Segmentation Through Compositionality;unpaired Modality Translation for Pseudo Labeling of Histology images;SNAFusion: Distilling 2D Axial Plane Diffusion Priors for Sparse-View 3D Cone-Beam CT Imaging;SynthBrainGrow: Synthetic Diffusion Brain Aging for Longitudinal MRI Data Generation in Young People;denoising Diffusion Models for 3D Healthy Brain Tissue Inpainting;panoptic Segmentation of Mammograms with Text-to-image Diffusion Model;interactive Generation of Laparoscopic Videos with Diffusion Models;Multi-parametric MRI to FMISO PET Synthesis for Hypoxia Prediction in Brain Tumors;qMRI Diffuser: Quantitative T1 Mapping of the Brain Using a Denoising Diffusion Probabilistic Model;on Differentially Private 3D medical image Synthesis with Controllable Latent Diffusion Models;five Pitfalls When Assessing Synthetic medical images with Reference Metrics;Augmenting Prostate MRI Dataset with Synthetic Volumetric images from Zone-Conditioned Diffusion Generative Model;tiBiX: Leveraging Temporal Information for Bidirectional X-Ray and Report Generation;Segmentation-Guided MRI Reconstruction for Meaningfully Diverse Reconstructions;Non-reference Quality Assessment for medical Imaging: Application to Synthetic Brain MRIs;latentArtiFusion: An Effective and Efficient Histological Artifacts Restoration Framework;How to Segment in 3D Using 2D Models: Automated 3D Segmentation of Prostate Cancer Metastatic Lesions on PET Volumes Using Multi-angle Maximum Intensity Projections and Diffusion Models.

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A 3D Generative Model of Pathological Multi-modal MR images and Segmentations 3rd

A 3D Generative Model of Pathological Multi-modal MR Images ...

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作者： Fernandez, Virginia Pinaya, Walter Hugo Lopez Borges, Pedro Graham, Mark S. Vercauteren, Tom Cardoso, M. Jorge Kings Coll London London WC2R 2LS England

ISBN: (纸本)9783031537660;9783031537677

Generative modelling and synthetic data can be a surrogate for real medical imaging datasets, whose scarcity and difficulty to share can be a nuisance when delivering accurate deep learning models for healthcare applications. In recent years, there has been an increased interest in using these models for data augmentation and synthetic data sharing, using architectures such as generative adversarial networks (GANs) or diffusion models (DMs). Nonetheless, the application of synthetic data to tasks such as 3D magnetic resonance imaging (MRI) segmentation remains limited due to the lack of labels associated with the generated images. Moreover, many of the proposed generative MRI models lack the ability to generate arbitrary modalities due to the absence of explicit contrast conditioning. These limitations prevent the user from adjusting the contrast and content of the images and obtaining more generalisable data for training task-specific models. In this work, we propose brainSPADE3D, a 3D generative model for brain MRI and associated segmentations, where the user can condition on specific pathological phenotypes and contrasts. The proposed joint imaging-segmentation generative model is shown to generate high-fidelity synthetic images and associated segmentations, with the ability to combine pathologies. We demonstrate how the model can alleviate issues with segmentation model performance when unexpected pathologies are present in the data.

关键词： Magnetic resonance imaging

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3rd international Workshop on Predictive Intelligence in Medicine, PRIME 2020, held in conjunction with the medical image computing and computer assisted intervention, MICCAI 2020

3rd International Workshop on Predictive Intelligence in Med...

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3rd international Workshop on Predictive Intelligence in Medicine, PRIME 2020, held in conjunction with the medical image computing and computer assisted intervention, MICCAI 2020

ISBN: (纸本)9783030593537

The proceedings contain 19 papers. The special focus in this conference is on Predictive Intelligence in Medicin. The topics include: Learned Deep Radiomics for Survival Analysis with Attention;robustification of Segmentation Models Against Adversarial Perturbations in medical Imaging;Joint Clinical Data and CT image Based Prognosis: A Case Study on Postoperative Pulmonary Venous Obstruction Prediction;Low-Dose CT Denoising Using Octave Convolution with High and Low Frequency Bands;conditional Generative Adversarial Network for Predicting 3D medical images Affected by Alzheimers Diseases;Inpainting Cropped Diffusion MRI Using Deep Generative Models;preface;Context-Aware Synergetic Multiplex Network for Multi-organ Segmentation of Cervical Cancer MRI;multi-view Brain HyperConnectome AutoEncoder for Brain State Classification;foreseeing Brain Graph Evolution over Time Using Deep Adversarial Network Normalizer;longitudinal Prediction of Radiation-Induced Anatomical Changes of Parotid Glands During Radiotherapy Using Deep Learning;deep Parametric Mixtures for Modeling the Functional Connectome;deep EvoGraphNet Architecture for Time-Dependent Brain Graph Data Synthesis from a Single Timepoint;Uniformizing Techniques to Process CT Scans with 3D CNNs for Tuberculosis Prediction;mr$$^2$$ NST: Multi-resolution and Multi-reference Neural Style Transfer for Mammography;template-Oriented Multi-task Sparse Low-Rank Learning for Parkinsons Diseases Diagnosis;multimodal Prediction of Breast Cancer Relapse Prior to Neoadjuvant Chemotherapy Treatment.

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Shape-Guided Conditional Latent Diffusion Models for Synthesising Brain Vasculature 3rd

Shape-Guided Conditional Latent Diffusion Models for Synthes...

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作者： Deo, Yash Dou, Haoran Ravikumar, Nishant Frangi, Alejandro F. Lassila, Toni Univ Leeds Ctr Computat Imaging & Simulat Technol Biomed CIS Sch Comp Leeds England Univ Leeds Sch Med Leeds England NIHR Leeds Biomed Res Ctr BRC Leeds England Alan Turing Inst London England Katholieke Univ Leuven Med Imaging Res Ctr MIRC Elect Engn Dept Leuven Belgium Katholieke Univ Leuven Med Imaging Res Ctr MIRC Cardiovasc Sci Dept Leuven Belgium Univ Manchester Div Informat Imaging & Data Sci Sch Comp Sci Manchester England Univ Manchester Div Informat Imaging & Data Sci Sch Hlth Sci Manchester England

ISBN: (纸本)9783031537660;9783031537677

The Circle ofWillis (CoW) is the part of cerebral vasculature responsible for delivering blood to the brain. Understanding the diverse anatomical variations and configurations of the CoW is paramount to advance research on cerebrovascular diseases and refine clinical interventions. However, comprehensive investigation of less prevalent CoW variations remains challenging because of the dominance of a few commonly occurring configurations. We propose a novel generative approach utilising a conditional latent diffusion model with shape and anatomical guidance to generate realistic 3D CoW segmentations, including different phenotypical variations. Our conditional latent diffusion model incorporates shape guidance to better preserve vessel continuity and demonstrates superior performance when compared to alternative generative models, including conditional variants of 3D GAN and 3D VAE. We observed that our model generated CoW variants that are more realistic and demonstrate higher visual fidelity than competing approaches with an FID score 53% better than the best-performing GAN-based model.

关键词： image Synthesis Deep Learning Brain Vasculature Vessel Synthesis Diffusion Latent Diffusion

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Reference-Free Isotropic 3D EM Reconstruction Using Diffusion Models 3rd

Reference-Free Isotropic 3D EM Reconstruction Using Diffusio...

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作者： Lee, Kyungryun Jeong, Won-Ki Korea Univ Dept Comp Sci & Engn Coll Informat Seoul South Korea

ISBN: (纸本)9783031537660;9783031537677

Electron microscopy (EM) images exhibit anisotropic axial resolution due to the characteristics inherent to the imaging modality, presenting challenges in analysis and downstream tasks. Recently proposed deep-learning-based isotropic reconstruction methods have addressed this issue;however, training the deep neural networks require either isotropic ground truth volumes, prior knowledge of the degradation process, or point spread function (PSF). Moreover, these methods struggle to generate realistic volumes when confronted with high scaling factors (e.g. x8, x10). In this paper, we propose a diffusion-model-based framework that overcomes the limitations of requiring reference data or prior knowledge about the degradation process. Our approach utilizes 2D diffusion models to consistently reconstruct 3D volumes and is well-suited for highly downsampled data. Extensive experiments conducted on two public datasets demonstrate the robustness and superiority of leveraging the generative prior compared to supervised learning methods. Additionally, we demonstrate our method's feasibility for self-supervised reconstruction, which can restore a single anisotropic volume without any training data. The source code is available on GitHub: https://***/hvcl/diffusion-em-recon.

关键词： Diffusion models Isotropic EM reconstruction Super-Resolution

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