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4th international MICCAI Brainlesion Workshop, BrainLes 2018 held in conjunction with the medical image computing for computer assisted intervention conference, MICCAI 2018

4th International MICCAI Brainlesion Workshop, BrainLes 2018...

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4th international MICCAI Brainlesion Workshop, BrainLes 2018 held in conjunction with the medical image computing for computer assisted intervention conference, MICCAI 2018

ISBN: (纸本)9783030117221

the proceedings contain 92 papers. the special focus in this conference is on MICCAI Brainlesion. the topics include: Segmentation of post-operative glioblastoma in MRI by U-net with patient-specific interactive refinement;Characterizing peritumoral tissue using DTI-based free water elimination;Deep 2D encoder-decoder convolutional neural network for multiple sclerosis lesion segmentation in brain MRI;shallow vs deep learning architectures for white matter lesion segmentation in the early stages of multiple sclerosis;detection of midline brain abnormalities using convolutional neural networks;Deep autoencoding models for unsupervised anomaly segmentation in brain MR images;Brain tumor detection and classification from multi-sequence MRI: study using convnets;Voxel-wise comparison with a-contrario analysis for automated segmentation of multiple sclerosis lesions from multimodal MRI;a graph based similarity measure for assessing altered connectivity in traumatic brain injury;CT brain perfusion: A clinical perspective;multi-scale convolutional-stack aggregation for robust white matter hyperintensities segmentation;holistic brain tumor screening and classification based on densenet and recurrent neural network;3D texture feature learning for noninvasive estimation of gliomas pathological subtype;pathology segmentation using distributional differences to images of healthy origin;multi-stage association analysis of glioblastoma gene expressions with texture and spatial patterns;Stroke lesion segmentation with 2D novel CNN pipeline and novel loss function;Contra-lateral information CNN for core lesion segmentation based on native CTP in acute stroke;dense multi-path u-net for ischemic stroke lesion segmentation in multiple image modalities;Multi-scale deep convolutional neural network for stroke lesions segmentation on CT images;ischemic stroke lesion segmentation using adversarial learning.

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4th international Workshop on Patch-Based Techniques in medical Imaging, Patch-MI 2018 Held in Conjunction with 21st international conference on medical image computing and computer-assisted intervention, MICCAI 2018

4th International Workshop on Patch-Based Techniques in Medi...

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4th international Workshop on Patch-Based Techniques in medical Imaging, Patch-MI 2018 Held in Conjunction with 21st international conference on medical image computing and computer-assisted intervention, MICCAI 2018

ISBN: (纸本)9783030004996

the proceedings contain 15 papers. the special focus in this conference is on Patch-Based Techniques in medical Imaging. the topics include: LesionBrain: An online tool for white matter lesion segmentation;multi-atlas parcellation in the presence of lesions: Application to multiple sclerosis;a patch-based segmentation approach with high level representation of the data for cortical sulci recognition;tumor delineation for brain radiosurgery by a ConvNet and non-uniform patch generation;iterative deep retinal topology extraction;MRI denoising using deep learning;MRI denoising and artefact removal using self-organizing maps for fast global block-matching;a Monte Carlo framework for denoising and missing wedge reconstruction in cryo-electron tomography;robust supervoxel matching combining mid-level spectral and context-rich features;stereo matching for wireless capsule endoscopy using direct attenuation model;liver tissue classification using an auto-context-based deep neural network with a multi-phase training framework;using 1D patch-based signatures for efficient cascaded classification of lung nodules;predicting future bone infiltration patterns in multiple myeloma.

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5th Workshop on Computational Methods and Clinical Applications in Musculoskeletal Imaging, MSKI 2017 Held in Conjunction with 20th international conference on medical image computing and computer-assisted intervention, MICCAI 2017

5th Workshop on Computational Methods and Clinical Applicati...

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5th Workshop on Computational Methods and Clinical Applications in Musculoskeletal Imaging, MSKI 2017 Held in Conjunction with 20th international conference on medical image computing and computer-assisted intervention, MICCAI 2017

ISBN: (纸本)9783319741123

the proceedings contain 13 papers. the special focus in this conference is on Computational Methods and Clinical Applications in Musculoskeletal Imaging. the topics include: Automatic full femur segmentation from computed tomography datasets using an atlas-based approach;classification of osteoporotic vertebral fractures using shape and appearance modelling;DSMS-FCN: A deeply supervised multi-scale fully convolutional network for automatic segmentation of intervertebral disc in 3D MR images;shape-aware deep convolutional neural network for vertebrae segmentation;Automated characterization of body composition and frailty with clinically acquired CT;unfolded cylindrical projection for rib fracture diagnosis;3D cobb angle measurements from scoliotic mesh models with varying face-vertex density;Automatic localization of the lumbar vertebral landmarks in CT images with context features;Joint multimodal segmentation of clinical CT and MR from hip arthroplasty patients;reconstruction of 3D muscle fiber structure using high resolution cryosectioned volume;Segmentation of pathological spines in CT images using a two-way CNN and a collision-based model.

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9th international Workshop on Machine Learning in medical Imaging, MLMI 2018 held in conjunction with the 21st international conference on medical image computing and computer-assisted intervention, MICCAI 2018

9th International Workshop on Machine Learning in Medical Im...

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9th international Workshop on Machine Learning in medical Imaging, MLMI 2018 held in conjunction with the 21st international conference on medical image computing and computer-assisted intervention, MICCAI 2018

ISBN: (纸本)9783030009182

the proceedings contain 46 papers. the special focus in this conference is on Machine Learning in medical Imaging. the topics include: Synthesizing dynamic MRI using long-term recurrent convolutional networks;Automatically designing CNN architectures for medical image segmentation;rotation invariance and directional sensitivity: Spherical harmonics versus radiomics features;can dilated convolutions capture ultrasound video dynamics?;topological correction of infant cortical surfaces using anatomically constrained U-net;Self-taught learning with residual sparse autoencoders for HEp-2 cell staining pattern recognition;semantic-aware generative adversarial nets for unsupervised domain adaptation in chest X-ray segmentation;brain status prediction with non-negative projective dictionary learning;classification of pancreatic cystic neoplasms based on multimodality images;robust contextual bandit via the capped- l2 Norm for Mobile Health intervention;retinal blood vessel segmentation using a fully convolutional network – transfer learning from patch- to image-level;combining deep learning and active contours opens the way to robust, automated analysis of brain cytoarchitectonics;Latent3DU-net: Multi-level latent shape space constrained 3D U-net for automatic segmentation of the proximal femur from radial MRI of the hip;Adversarial image registration with application for MR and TRUS image fusion;Reproducible white matter tract segmentation using 3D U-net on a large-scale DTI dataset;competition vs. concatenation in skip connections of fully convolutional networks;Ensemble of multi-sized FCNs to improve white matter lesion segmentation;automatic accurate infant cerebellar tissue segmentation with densely connected convolutional network;nuclei detection using mixture density networks;attention-guided curriculum learning for weakly supervised classification and localization of thoracic diseases on chest radiographs.

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Segmentation Style Discovery: Application to Skin Lesion images 9th

Segmentation Style Discovery: Application to Skin Lesion Ima...

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27th international conference on medical image computing and computer assisted intervention (MICCAI)

作者： Abhishek, Kumar Kawahara, Jeremy Hamarneh, Ghassan Simon Fraser Univ Sch Comp Sci Burnaby BC Canada AIP Labs Budapest Hungary

ISBN: (纸本)9783031776090;9783031776106

Variability in medical image segmentation, arising from annotator preferences, expertise, and their choice of tools, has been well documented. While the majority of multi-annotator segmentation approaches focus on modeling annotator-specific preferences, they require annotator-segmentation correspondence. In this work, we introduce the problem of segmentation style discovery, and propose StyleSeg, a segmentation method that learns plausible, diverse, and semantically consistent segmentation styles from a corpus of image-mask pairs without any knowledge of annotator correspondence. StyleSeg consistently outperforms competing methods on four publicly available skin lesion segmentation (SLS) datasets. We also curate ISIC-MultiAnnot, the largest multi-annotator SLS dataset with annotator correspondence, and our results show a strong alignment, using our newly proposed measure AS(2), between the predicted styles and annotator preferences. the code and the dataset are available at https://***/sfu-mial/StyleSeg.

关键词： inter-rater variability image segmentation dermatology

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FedGS: Federated Gradient Scaling for Heterogeneous medical image Segmentation 9th

FedGS: Federated Gradient Scaling for Heterogeneous Medical ...

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27th international conference on medical image computing and computer assisted intervention (MICCAI)

作者： Schutte, Philip Corbetta, Valentina Beets-Tan, Regina Silva, Wilson Univ Amsterdam Amsterdam Netherlands Netherlands Canc Inst Dept Radiol Amsterdam Netherlands Univ Utrecht Dept Informat & Comp Sci Dept Biol AI Technol Life Utrecht Netherlands Maastricht Univ GROW Sch Oncol & Dev Biol Med Ctr Maastricht Netherlands

ISBN: (纸本)9783031776090;9783031776106

Federated Learning (FL) in Deep Learning (DL)-automated medical image segmentation helps preserving privacy by enabling collaborative model training without sharing patient data. However, FL faces challenges with data heterogeneity among institutions, leading to suboptimal global models. Integrating Disentangled Representation Learning (DRL) in FL can enhance robustness by separating data into distinct representations. Existing DRL methods assume heterogeneity lies solely in style features, overlooking content-based variability like lesion size and shape. We propose FedGS, a novel FL aggregation method, to improve segmentation performance on small, under-represented targets while maintaining overall efficacy. FedGS demonstrates superior performance over FedAvg, particularly for small lesions, across PolypGen and LiTS datasets. the code and pre-trained checkpoints are available at the following link: https://***/Trustworthy-AI-UU-NKI/Federated-Learning-Disentanglement.

关键词： Federated learning

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Semantic image Synthesis for Abdominal CT 1

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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： Zhuang, Yan Hou, Benjamin Mathai, Tejas Sudharshan Mukherjee, Pritam Kim, Boah Summers, Ronald M. NIH Imaging Biomarkers & Comp Aided Diag Lab Dept Radiol & Imaging Sci Ctr Clin Bethesda MD 20892 USA

ISBN: (数字)9783031537677

ISBN: (纸本)9783031537660;9783031537677

As a new emerging and promising type of generative models, diffusion models have proven to outperform Generative Adversarial Networks (GANs) in multiple tasks, including image synthesis. In this work, we explore semantic image synthesis for abdominal CT using conditional diffusion models, which can be used for downstream applications such as data augmentation. We systematically evaluated the performance of three diffusion models, as well as to other state-of-the-art GAN-based approaches, and studied the different conditioning scenarios for the semantic mask. Experimental results demonstrated that diffusion models were able to synthesize abdominal CT images with better quality. Additionally, encoding the mask and the input separately is more effective than naive concatenating.

关键词： CT Abdomen Diffusion model Semantic image Synthesis

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MIM-OOD: Generative Masked image Modelling for Out-of-Distribution Detection in medical images 1

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作者： Marimont, Sergio Naval Siomos, Vasilis Tarroni, Giacomo City Univ London CitAI Res Ctr London England Imperial Coll London BioMedIA London England

ISBN: (数字)9783031537677

ISBN: (纸本)9783031537660;9783031537677

Unsupervised Out-of-Distribution (OOD) detection consists in identifying anomalous regions in images leveraging only models trained on images of healthy anatomy. An established approach is to tokenize images and model the distribution of tokens with Auto-Regressive (AR) models. AR models are used to 1) identify anomalous tokens and 2) inpaint anomalous representations with in-distribution tokens. However, AR models are slow at inference time and prone to error accumulation issues which negatively affect OOD detection performance. Our novel method, MIM-OOD, overcomes both speed and error accumulation issues by replacing the AR model with two task-specific networks: 1) a transformer optimized to identify anomalous tokens and 2) a transformer optimized to in-paint anomalous tokens using masked image modelling (MIM). Our experiments with brain MRI anomalies show that MIM-OOD substantially outperforms AR models (DICE 0.458 vs 0.301) while achieving a nearly 25x speedup (9.5 s vs 244 s).

关键词： out-of-distribution detection unsupervised learning masked image modelling

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A Review on medical image Generation Generative Adversarial Networks (GANs) 6

A Review on Medical Image Generation Generative Adversarial ...

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6th international conference on Mobile computing and Sustainable Informatics, ICMCSI 2025

作者： Patel, Shrina Makwana, Ashwin U & P U Patel Department of Computer Engineering C S Patel Institute of Technology Charotar University of Science and Technology Gujarat Changa India

ISBN: (纸本)9798331522667

this review focuses on the way GANs can be applied in the medical image generation and how they can be helpful in diagnostics, training or data augmentation. through GANs, it is possible to create artificial images for synthetic medical image generation because the current data collected in the healthcare field lacks sufficient large amount of data amount and diverse image quality, however, there are apparent problems in the production of high quality and diverse, and strongly interpretable synthetic images. the work examines several structures based on GANs and compares them, pointing out the challenges that need to be addressed when generating realistic images of severe medical conditions. Some of these are to increase GAN dimensions to improve model complexity and expand the training data to include therapeutic relevance. thus, this review contributes to the identification of the state of AI-based medical image synthesis. © 2025 IEEE.

关键词： Generative adversarial networks

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Ultrasound image Reconstruction with Denoising Diffusion Restoration Models 1

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作者： Zhang, Yuxin Huneau, Clement Idier, Jerome Mateus, Diana Nantes Univ Ecole Cent Nantes LS2N CNRSUMR 6004 F-44000 Nantes France

ISBN: (数字)9783031537677

ISBN: (纸本)9783031537660;9783031537677

Ultrasound image reconstruction can be approximately cast as a linear inverse problem that has traditionally been solved with penalized optimization using the l(1) or l(2) norm, or wavelet-based terms. However, such regularization functions often struggle to balance the sparsity and the smoothness. A promising alternative is using learned priors to make the prior knowledge closer to reality. In this paper, we rely on learned priors under the framework of Denoising Diffusion Restoration Models (DDRM), initially conceived for restoration tasks with natural images. We propose and test two adaptions of DDRM to ultrasound inverse problem models, DRUS and WDRUS. Our experiments on synthetic and PICMUS data show that from a single plane wave our method can achieve image quality comparable to or better than DAS and state-of-the-art methods. the code is available at https://***/Yuxin-Zhang-Jasmine/DRUS-v1/.

关键词： Ultrasound imaging Inverse Problems Diffusion models

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