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

共 2692 条记录，以下是471-480 订阅

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QID2: An image-Conditioned Diffusion Model for Q-Space Up-Sampling of DWI Data 15th

QID2: An Image-Conditioned Diffusion Model for Q-Space Up-S...

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15th international Workshop on Computational Diffusion MRI, CDMRI 2024, held in conjunction with 27th international conference on medical image computing and computer-assisted intervention, MICCAI 2024

作者： Chen, Zijian Wang, Jueqi Venkataraman, Archana Department of Electrical and Computer Engineering Boston University Boston United States

ISBN: (纸本)9783031869198

We propose an image-conditioned diffusion model to estimate high angular resolution diffusion weighted imaging (DWI) from a low angular resolution acquisition. Our model, which we call QID2, takes as input a set of low angular resolution DWI data and uses this information to estimate the DWI data associated with a target gradient direction. We leverage a U-Net architecture with cross-attention to preserve the positional information of the reference images, further guiding the target image generation. We train and evaluate QID2 on single-shell DWI samples curated from the Human Connectome Project (HCP) dataset. Specifically, we sub-sample the HCP gradient directions to produce low angular resolution DWI data and train QID2 to reconstruct the missing high angular resolution samples. We compare QID2 with two state-of-the-art GAN models. Our results demonstrate that QID2 not only achieves higher-quality generated images, but it consistently outperforms state-of-the-art baseline methods in downstream tensor estimation across multiple metrics and in generalizing to downsampling scenario during testing. Taken together, this study highlights the potential of diffusion models, and QID2 in particular, for q-space up-sampling, thus offering a promising toolkit for clinical and research applications. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Deep Learning Diffusion Models Diffusion Weighted Imaging Q-Space Up-sampling Tensor Reconstruction

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image Quality Transfer of Diffusion MRI Guided By High-Resolution Structural MRI 15th

Image Quality Transfer of Diffusion MRI Guided By High-Reso...

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作者： Cicimen, Alp G. Tregidgo, Henry F. J. Figini, Matteo Messaritaki, Eirini B. McNabb, Carolyn Palombo, Marco Evans, C. John Cercignani, Mara Jones, Derek K. C. Alexander, Daniel Centre for Medical Image Computing University College London London United Kingdom Cardiff University Brain Research Imaging Centre Cardiff University Cardiff United Kingdom

ISBN: (纸本)9783031869198

Prior work on the image Quality Transfer on Diffusion MRI (dMRI) has shown significant improvement over traditional interpolation methods. However, the difficulty in obtaining ultra-high resolution Diffusion MRI scans poses a problem in training neural networks to obtain high-resolution dMRI scans. Here we hypothesise that the inclusion of structural MRI images, which can be acquired at much higher resolutions, can be used as a guide to obtaining a more accurate high-resolution dMRI output. To test our hypothesis, we have constructed a novel framework that incorporates structural MRI scans together with dMRI to obtain high-resolution dMRI scans. We set up tests which evaluate the validity of our claim through various configurations and compare the performance of our approach against a unimodal approach. Our results show that the inclusion of structural MRI scans do lead to an improvement in high-resolution image prediction when T1w data is incorporated into the model input. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： image coding

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Test-Time Adaptation with Shape Moments for image Segmentation 25th

Test-Time Adaptation with Shape Moments for Image Segmentati...

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

作者： Bateson, Mathilde Lombaert, Herve Ben Ayed, Ismail ETS Montreal Montreal PQ Canada

ISBN: (纸本)9783031164408;9783031164392

Supervised learning is well-known to fail at generalization under distribution shifts. In typical clinical settings, the source data is inaccessible and the target distribution is represented with a handful of samples: adaptation can only happen at test time on a few (or even a single) subject(s). We investigate test-time single-subject adaptation for segmentation, and propose a shape-guided entropy minimization objective for tackling this task. During inference for a single testing subject, our loss is minimized with respect to the batch normalization's scale and bias parameters. We show the potential of integrating various shape priors to guide adaptation to plausible solutions, and validate our method in two challenging scenarios: MRI-to-CT adaptation of cardiac segmentation and cross-site adaptation of prostate segmentation. Our approach exhibits substantially better performances than the existing test-time adaptation methods. Even more surprisingly, it fares better than state-of-the-art domain adaptation methods, although it forgoes training on additional target data during adaptation. Our results question the usefulness of training on target data in segmentation adaptation, and points to the substantial effect of shape priors on test-time inference. Our framework can be readily used for integrating various priors and for adapting any segmentation network. the code is publicly available (https://*** mathilde-b/TTA).

关键词： Test-time adaptation Segmentation Shape moments Deep networks Entropy minimization

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TransFusion: Multi-view Divergent Fusion for medical image Segmentation with Transformers 25th

TransFusion: Multi-view Divergent Fusion for Medical Image S...

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

作者： Liu, Di Gao, Yunhe Zhangli, Qilong Han, Ligong He, Xiaoxiao Xia, Zhaoyang Wen, Song Chang, Qi Yan, Zhennan Zhou, Mu Metaxas, Dimitris Rutgers State Univ Dept Comp Sci New Brunswick NJ 08901 USA SenseBrain Res San Jose CA USA

ISBN: (纸本)9783031164439;9783031164422

Combining information from multi-view images is crucial to improve the performance and robustness of automated methods for disease diagnosis. However, due to the non-alignment characteristics of multi-view images, building correlation and data fusion across views largely remain an open problem. In this study, we present TransFusion, a Transformer-based architecture to merge divergent multi-view imaging information using convolutional layers and powerful attention mechanisms. In particular, the Divergent Fusion Attention (DiFA) module is proposed for rich cross-view context modeling and semantic dependency mining, addressing the critical issue of capturing long-range correlations between unaligned data from different image views. We further propose the Multi-Scale Attention (MSA) to collect global correspondence of multi-scale feature representations. We evaluate TransFusion on the Multi-Disease, Multi-View & Multi-Center Right Ventricular Segmentation in Cardiac MRI (M&Ms-2) challenge cohort. TransFusion demonstrates leading performance against the state-of-the-art methods and opens up new perspectives for multi-view imaging integration towards robust medical image segmentation.

关键词： Cardiac MRI Segmentation Deep learning

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You Don't Have to Be Perfect to Be Amazing: Unveil the Utility of Synthetic images 26th

You Don't Have to Be Perfect to Be Amazing: Unveil the Utili...

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

作者： Xing, Xiaodan Felder, Federico Nan, Yang Papanastasiou, Giorgos Walsh, Simon Yang, Guang Imperial Coll London Natl Heart & Lung Inst London England Royal Brompton Hosp London England Imperial Coll London Bioengn Dept London W12 7SL England Imperial Coll London Imperial X London W12 7SL England Univ Essex Colchester Essex England Imperial Coll London Natl Heart & Lung Inst London SW7 2AZ England Royal Brompton Hosp Cardiovasc Res Ctr London SW3 6NP England Kings Coll London Sch Biomed Engn & Imaging Sci London WC2R 2LS England

ISBN: (纸本)9783031439032;9783031439049

Synthetic images generated from deep generative models have the potential to address data scarcity and data privacy issues. the selection of synthesis models is mostly based on image quality measurements, and most researchers favor synthetic images that produce realistic images, i.e., images with good fidelity scores, such as low Frechet Inception Distance (FID) and high Peak Signal-To-Noise Ratio (PSNR). However, the quality of synthetic images is not limited to fidelity, and a wide spectrum of metrics should be evaluated to comprehensively measure the quality of synthetic images. In addition, quality metrics are not truthful predictors of the utility of synthetic images, and the relations between these evaluation metrics are not yet clear. In this work, we have established a comprehensive set of evaluators for synthetic images, including fidelity, variety, privacy, and utility. By analyzing more than 100k chest X-ray images and their synthetic copies, we have demonstrated that there is an inevitable trade-off between synthetic image fidelity, variety, and privacy. In addition, we have empirically demonstrated that the utility score does not require images with both high fidelity and high variety. For intra- and cross-task data augmentation, mode-collapsed images and low-fidelity images can still demonstrate high utility. Finally, our experiments have also showed that it is possible to produce images with both high utility and privacy, which can provide a strong rationale for the use of deep generative models in privacy-preserving applications. Our study can shore up comprehensive guidance for the evaluation of synthetic images and elicit further developments for utility-aware deep generative models in medical image synthesis.

关键词： Synthetic Data Augmentation medical image Synthesis

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Scribble-Supervised medical image Segmentation via Dual-Branch Network and Dynamically Mixed Pseudo Labels Supervision 25th

Scribble-Supervised Medical Image Segmentation via Dual-Bran...

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

作者： Luo, Xiangde Hu, Minhao Liao, Wenjun Zhai, Shuwei Song, Tao Wang, Guotai Zhang, Shaoting Univ Elect Sci & Technol China Chengdu Peoples R China Shanghai Lab Shanghai Peoples R China SenseTime Res Shanghai Peoples R China

ISBN: (纸本)9783031164316;9783031164309

medical image segmentation plays an irreplaceable role in computer-assisted diagnosis, treatment planning and following-up. Collecting and annotating a large-scale dataset is crucial to training a powerful segmentation model, but producing high-quality segmentation masks is an expensive and time-consuming procedure. Recently, weakly-supervised learning that uses sparse annotations (points, scribbles, bounding boxes) for network training has achieved encouraging performance and shown the potential for annotation cost reduction. However, due to the limited supervision signal of sparse annotations, it is still challenging to employ them for networks training directly. In this work, we propose a simple yet efficient scribble-supervised image segmentation method and apply it to cardiac MRI segmentation. Specifically, we employ a dual-branch network with one encoder and two slightly different decoders for image segmentation and dynamically mix the two decoders' predictions to generate pseudo labels for auxiliary supervision. By combining the scribble supervision and auxiliary pseudo labels supervision, the dual-branch network can efficiently learn from scribble annotations end-to-end. Experiments on the public ACDC dataset show that our method performs better than current scribble-supervised segmentation methods and also outperforms several semi-supervised segmentation methods. Code is available: https://***/HiLab-git/WSL4MIS.

关键词： Weakly-supervised learning Scribble annotation Pseudo labels

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Evaluating STU-Net for Brain Tumor Segmentation 1

引用

Challenge on Brain Tumor Segmentation, BraTS 2023, international Challenge on Cross-Modality Domain Adaptation for medical image Segmentation, CrossMoDA 2023, held in conjunction with the medical image computing for computer assisted intervention conference, MICCAI 2023

作者： Huang, Ziyan Ye, Jin Wang, Haoyu Deng, Zhongying Su, Yanzhou Li, Tianbin Cheng, Junlong Chen, Jianpin Guo, Sizheng Shen, Yiqing He, Junjun Shanghai AI Laboratory Shanghai China Institute of Medical Robotics Shanghai Jiao Tong University Shanghai China University of Cambridge Cambridge United Kingdom

ISBN: (数字)9783031761638

ISBN: (纸本)9783031761621

Brain tumor segmentation is vital in addressing the tumor’s high heterogeneity, enhancing accurate diagnosis, guiding effective treatment, and improving prognosis predictions. In recent years, state-of-the-art methods in this domain have primarily evolved from U-Net-based architectures, demonstrating notable advancements in glioma radiographic segmentation. While large-scale models pre-trained on extensive datasets have significantly propelled deep learning progress, most current medical image segmentation models remain small-scale, encompassing only tens of millions of parameters. As the BraTS competition amasses more data and with the advancements of large-scale models in the past year, there emerges a compelling need to investigate the potential benefits of larger network architectures for adult glioma segmentation. In this context, we utilize the Scalable and Transferable U-Net (STU-Net) and its pre-trained variants on the RSNA-ASNR-MICCAI Brain Tumor Segmentation (BraTS) 2023 dataset. Being one of the most extensive medical image segmentation models, STU-Net’s sizes range between 14 million to 1.4 billion parameters. Our research seeks to evaluate STU-Net’s efficacy and transferability on the BraTS23 dataset, encapsulating diverse MRI scans of brain tumor patients. the code and pre-trained models are available at https://***/uni-medical/STU-Net. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： image segmentation

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Uncertainty Aware Sampling Framework of Weak-Label Learning for Histology image Classification 25th

Uncertainty Aware Sampling Framework of Weak-Label Learning ...

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

作者： Aljuhani, Asmaa Casukhela, Ishya Chan, Jany Liebner, David Machiraju, Raghu Ohio State Univ Dept Comp Sci & Engn Columbus OH USA Ohio State Univ Dept Biomed Informat Coll Med Columbus OH USA Ohio State Univ Dept Pathol Coll Med Columbus OH USA Ohio State Univ Div Med Oncol Dept Internal Med Columbus OH USA

ISBN: (纸本)9783031164347;9783031164330

Advances in digital pathology and deep learning have enabled robust disease classification, better diagnosis, and prognosis. In real-world settings, readily available and inexpensive image-level labels from pathology reports are weak, which seriously degrades the performance of deep learning models. Weak image-level labels do not represent the complexity and heterogeneity of the analyzed WSIs. this work presents an importance-based sampling framework for robust histopathology image analysis, Uncertainty-Aware Sampling Framework (UASF). Our experiments demonstrate the effectiveness of UASF when used to grade a highly heterogeneous subtype of soft tissue sarcomas. Furthermore, our proposed model achieves better accuracy when compared to the baseline models by sampling the most relevant tiles.

关键词： Histopathology image analysis Uncertainty estimation Weak label learning Noisy label detection

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Online Easy Example Mining for Weakly-Supervised Gland Segmentation from Histology images 25th

Online Easy Example Mining for Weakly-Supervised Gland Segme...

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

作者： Li, Yi Yu, Yiduo Zou, Yiwen Xiang, Tianqi Li, Xiaomeng Hong Kong Univ Sci & Technol Dept Elect & Comp Engn Hong Kong Peoples R China HKUST Shenzhen Res Inst Shenzhen Peoples R China

ISBN: (纸本)9783031164408;9783031164392

Developing an AI-assisted gland segmentation method from histology images is critical for automatic cancer diagnosis and prognosis;however, the high cost of pixel-level annotations hinders its applications to broader diseases. Existing weakly-supervised semantic segmentation methods in computer vision achieve degenerative results for gland segmentation, since the characteristics and problems of glandular datasets are different from general object datasets. We observe that, unlike natural images, the key problem with histology images is the confusion of classes owning to morphological homogeneity and low color contrast among different tissues. To this end, we propose a novel method Online Easy Example Mining (OEEM) that encourages the network to focus on credible supervision signals rather than noisy signals, therefore mitigating the influence of inevitable false predictions in pseudo-masks. According to the characteristics of glandular datasets, we design a strong framework for gland segmentation. Our results exceed many fully-supervised methods and weakly-supervised methods for gland segmentation over 4.6% and 6.04% at mIoU, respectively. Code is available at https://github. com/xmed-lab/OEEM.

关键词： Online Easy Example Mining Histology image Gland segmentation Wealy-supervised semantic segmentation

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FUSSNet: Fusing Two Sources of Uncertainty for Semi-supervised medical image Segmentation 25th

FUSSNet: Fusing Two Sources of Uncertainty for Semi-supervis...

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

作者： Xiang, Jinyi Qiu, Peng Yang, Yang Shanghai Jiao Tong Univ Dept Comp Sci & Engn Shanghai Peoples R China Shanghai Jiao Tong Univ Shanghai Peoples Hosp 9 Dept Vasc Surg Shanghai Peoples R China

ISBN: (纸本)9783031164521;9783031164514

In recent years, various semi-supervised learning (SSL) methods have been developed to deal with the scarcity of labeled data in medical image segmentation. Especially, many of them focus on the uncertainty caused by a lack of knowledge (about the best model), i.e. epistemic uncertainty (EU). Besides EU, another type of uncertainty, aleatoric uncertainty (AU), originated from irreducible errors or noise, also commonly exists in medical imaging data. While previous SSL approaches focus on only one of them (mostly EU), this study shows that SSL segmentation models can benefit more by considering both sources of uncertainty. the proposed FUSSNet framework is featured by a joint learning scheme, which combines the EU-guided unsupervised learning and AU-guided supervised learning. We assess the method on two benchmark datasets for the segmentation of left atrium (LA) and pancreas, respectively. the experimental results show that FUSSNet outperforms the state-of-the-art semi-supervised segmentation methods by over 2% on Dice score for pancreas data and almost reaches the accuracy obtained in fully supervised setting for LA data.

关键词： Semi-supervised learning Uncertainty-guided training medical image segmentation

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