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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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Global Multi-modal 2D/3D Registration via Local Descriptors Learning 25th

Global Multi-modal 2D/3D Registration via Local Descriptors ...

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

作者： Markova, Viktoria Ronchetti, Matteo Wein, Wolfgang Zettinig, Oliver Prevost, Raphael ImFus GmbH Munich Germany Tech Univ Munich Munich Germany

ISBN: (纸本)9783031164460;9783031164453

Multi-modal registration is a required step for many image-guided procedures, especially ultrasound-guided interventions that require anatomical context. While a number of such registration algorithms are already available, they all require a good initialization to succeed due to the challenging appearance of ultrasound images and the arbitrary coordinate system they are acquired in. In this paper, we present a novel approach to solve the problem of registration of an ultrasound sweep to a pre-operative image. We learn dense keypoint descriptors from which we then estimate the registration. We show that our method overcomes the challenges inherent to registration tasks with freehand ultrasound sweeps, namely, the multi-modality and multi-dimensionality of the data in addition to lack of precise ground truth and low amounts of training examples. We derive a registration method that is fast, generic, fully automatic, does not require any initialization and can naturally generate visualizations aiding interpretability and explainability. Our approach is evaluated on a clinical dataset of paired MR volumes and ultrasound sequences.

关键词： Ultrasound Magnetic resonance image registration Feature learning Deep learning

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UWAT-GAN: Fundus Fluorescein Angiography Synthesis via Ultra-Wide-Angle Transformation Multi-scale GAN 26th

UWAT-GAN: Fundus Fluorescein Angiography Synthesis via Ultra...

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

作者： Fang, Zhaojie Chen, Zhanghao Wei, Pengxue Li, Wangting Zhang, Shaochong Elazab, Ahmed Jia, Gangyong Ge, Ruiquan Wang, Changmiao Hangzhou Dianzi Univ HDU ITMO Joint Inst Hangzhou 310018 Peoples R China Hangzhou Dianzi Univ Sch Comp Sci & Technol Hangzhou 310018 Peoples R China Jinan Univ Shenzhen Eye Hosp Shenzhen 518040 Peoples R China Shenzhen Univ Sch Biomed Engn Shenzhen 518060 Peoples R China Shenzhen Res Inst Big Data Med Big Data Lab Shenzhen 518172 Peoples R China

ISBN: (纸本)9783031439896;9783031439902

Fundus photography is an essential examination for clinical and differential diagnosis of fundus diseases. Recently, Ultra-Wide-angle Fundus (UWF) techniques, UWF Fluorescein Angiography (UWF-FA) and UWF Scanning Laser Ophthalmoscopy (UWF-SLO) have been gradually put into use. However, Fluorescein Angiography (FA) and UWF-FA require injecting sodium fluorescein which may have detrimental influences. To avoid negative impacts, cross-modality medical image generation algorithms have been proposed. Nevertheless, current methods in fundus imaging could not produce high-resolution images and are unable to capture tiny vascular lesion areas. this paper proposes a novel conditional generative adversarial network (UWAT-GAN) to synthesize UWF-FA from UWF-SLO. Using multi-scale generators and a fusion module patch to better extract global and local information, our model can generate high-resolution images. Moreover, an attention transmit module is proposed to help the decoder learn effectively. Besides, a supervised approach is used to train the network using multiple new weighted losses on different scales of data. Experiments on an in-house UWF image dataset demonstrate the superiority of the UWAT-GAN over the state-of-the-art methods. the source code is available at: https://***/Tinysqua/UWAT-GAN.

关键词： Fluorescein Angiography Cross-modality image Generation Ultra-Wide-angle Fundus Imaging Conditional Generative Adversarial Network

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Addressing Class Imbalance in Semi-supervised image Segmentation: A Study on Cardiac MRI 25th

Addressing Class Imbalance in Semi-supervised Image Segmenta...

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

作者： Basak, Hritam Ghosal, Sagnik Sarkar, Ram Jadavpur Univ Dept Elect Engn Kolkata India Jadavpur Univ Dept Comp Sci & Engn Kolkata India

ISBN: (纸本)9783031164521;9783031164514

Due to the imbalanced and limited data, semi-supervised medical image segmentation methods often fail to produce superior performance for some specific tailed classes. Inadequate training for those particular classes could introduce more noise to the generated pseudo labels, affecting overall learning. To alleviate this shortcoming and identify the under-performing classes, we propose maintaining a confidence array that records class-wise performance during training. A fuzzy fusion of these confidence scores is proposed to adaptively prioritize individual confidence metrics in every sample rather than traditional ensemble approaches, where a set of predefined fixed weights are assigned for all the test cases. Further, we introduce a robust class-wise sampling method and dynamic stabilization for a better training strategy. Our proposed method considers all the under-performing classes with dynamic weighting and tries to remove most of the noises during training Upon evaluation on two cardiac MRI datasets, ACDC and MMWHS, our proposed method shows effectiveness and generalizability and outperforms several state-of-the-art methods found in the literature.

关键词： Class imbalance Fuzzy fusion Semi-supervised learning Cardiac MRI image segmentation

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Inflated 3D Convolution-Transformer for Weakly-Supervised Carotid Stenosis Grading with Ultrasound Videos 26th

Inflated 3D Convolution-Transformer for Weakly-Supervised Ca...

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

作者： Zhou, Xinrui Huang, Yuhao Xue, Wufeng Yang, Xin Zou, Yuxin Ying, Qilong Zhang, Yuanji Liu, Jia Ren, Jie Ni, Dong Shenzhen Univ Sch Biomed Engn Hlth Sci Ctr 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 Shenzhen Ray Shape Med Technol Co Ltd Shenzhen Peoples R China Sun Yat Sen Univ Affiliated Hosp 3 Guangzhou Peoples R China

ISBN: (纸本)9783031438943;9783031438950

Localization of the narrowest position of the vessel and corresponding vessel and remnant vessel delineation in carotid ultrasound (US) are essential for carotid stenosis grading (CSG) in clinical practice. However, the pipeline is time-consuming and tough due to the ambiguous boundaries of plaque and temporal variation. To automatize this procedure, a large number of manual delineations are usually required, which is not only laborious but also not reliable given the annotation difficulty. In this study, we present the first video classification framework for automatic CSG. Our contribution is three-fold. First, to avoid the requirement of laborious and unreliable annotation, we propose a novel and effective video classification network for weakly-supervised CSG. Second, to ease the model training, we adopt an inflation strategy for the network, where pre-trained 2D convolution weights can be adapted into the 3D counterpart in our network for an effective warm start. third, to enhance the feature discrimination of the video, we propose a novel attention-guided multi-dimension fusion (AMDF) transformer encoder to model and integrate global dependencies within and across spatial and temporal dimensions, where two lightweight cross-dimensional attention mechanisms are designed. Our approach is extensively validated on a large clinically collected carotid US video dataset, demonstrating state-of-the-art performance compared with strong competitors.

关键词： Ultrasound video Carotid stenosis grading Classification

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Revealing Anatomical Structures in PET to Generate CT for Attenuation Correction 26th

Revealing Anatomical Structures in PET to Generate CT for At...

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

作者： Pan, Yongsheng Liu, Feihong Jiang, Caiwen Huang, Jiawei Xia, Yong Shen, Dinggang ShanghaiTech Univ Sch Biomed Engn Shanghai Peoples R China Northwest Univ Sch Informat Sci & Technol Xian Peoples R China Northwestern Polytech Univ Sch Comp Sci Xian Peoples R China Shanghai United Imaging Intelligence Co Ltd Shanghai Peoples R China

ISBN: (纸本)9783031439988;9783031439995

Positron emission tomography (PET) is a molecular imaging technique relying on a step, namely attenuation correction (AC), to correct radionuclide distribution based on pre-determined attenuation coefficients. Conventional AC techniques require additionally-acquired computed tomography (CT) or magnetic resonance (MR) images to calculate attenuation coefficients, which increases imaging expenses, time costs, or radiation hazards to patients, especially for whole-body scanners. In this paper, considering technological advances in acquiring more anatomical information in raw PET images, we propose to conduct attenuation correction to PET by itself. To achieve this, we design a deep learning based framework, namely anatomical skeleton-enhanced generation (ASEG), to generate pseudo CT images from non-attenuation corrected PET images for attenuation correction. Specifically, ASEG contains two sequential modules, i.e., a skeleton prediction module and a tissue rendering module. the former module first delineates anatomical skeleton and the latter module then renders tissue details. Both modules are trained collaboratively with specific anatomical-consistency constraint to guarantee tissue generation fidelity. Experiments on four public PET/CT datasets demonstrate that our ASEG outperforms existing methods by achieving better consistency of anatomical structures in generated CT images, which are further employed to conduct PET attenuation correction with better similarity to real ones. this work verifies the feasibility of generating pseudo CT from raw PET for attenuation correction without acquising additional images. the associated implementation is available at https://***/YongshengPan/ASEG-for-PET2CT.

关键词： PET Attenuation correction CT image generation

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cOOpD: Reformulating COPD Classification on Chest CT Scans as Anomaly Detection Using Contrastive Representations 26th

cOOpD: Reformulating COPD Classification on Chest CT Scans a...

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

作者： Almeida, Silvia D. Lueth, Carsten T. Norajitra, Tobias Wald, Tassilo Nolden, Marco Jaeger, Paul F. Heussel, Claus P. Biederer, Juergen Weinheimer, Oliver Maier-Hein, Klaus H. German Canc Res Ctr Div Med Image Comp Heidelberg Germany Heidelberg Univ Med Fac Heidelberg Germany German Ctr Lung Res DZL Translat Lung Res Ctr Heidelberg TLRC Heidelberg Germany German Canc Res Ctr Interact Machine Learning Grp Heidelberg Germany German Canc Res Ctr Helmholtz Imaging Heidelberg Germany Thoraxklin Univ Hosp Diagnost & Intervent Radiol Nucl Med Heidelberg Germany Univ Hosp Diagnost & Intervent Radiol Heidelberg Germany

ISBN: (纸本)9783031439032;9783031439049

Classification of heterogeneous diseases is challenging due to their complexity, variability of symptoms and imaging findings. Chronic Obstructive Pulmonary Disease (COPD) is a prime example, being underdiagnosed despite being the third leading cause of death. Its sparse, diffuse and heterogeneous appearance on computed tomography challenges supervised binary classification. We reformulate COPD binary classification as an anomaly detection task, proposing cOOpD: heterogeneous pathological regions are detected as Out-of-Distribution (OOD) from normal homogeneous lung regions. To this end, we learn representations of unlabeled lung regions employing a self-supervised contrastive pretext model, potentially capturing specific characteristics of diseased and healthy unlabeled regions. A generative model then learns the distribution of healthy representations and identifies abnormalities (stemming from COPD) as deviations. Patient-level scores are obtained by aggregating region OOD scores. We show that cOOpD achieves the best performance on two public datasets, with an increase of 8.2% and 7.7% in terms of AUROC compared to the previous supervised state-of-the-art. Additionally, cOOpD yields well-interpretable spatial anomaly maps and patient-level scores which we show to be of additional value in identifying individuals in the early stage of progression. Experiments in artificially designed real-world prevalence settings further support that anomaly detection is a powerful way of tackling COPD classification. Code is at https://***/MIC-DKFZ/cOOpD.

关键词： COPD classification Anomaly detection Contrastive learning

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Test-Time Adaptation with Shape Moments for image Segmentation 1

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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: (数字)9783031164408

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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Iteratively Coupled Multiple Instance Learning from Instance to Bag Classifier for Whole Slide image Classification 26th

Iteratively Coupled Multiple Instance Learning from Instance...

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

作者： Wang, Hongyi Luo, Luyang Wang, Fang Tong, Ruofeng Chen, Yen-Wei Hu, Hongjie Lin, Lanfen Chen, Hao Zhejiang Univ Coll Comp Sci & Technol Hangzhou Peoples R China Hong Kong Univ Sci & Technol Dept Comp Sci & Engn Hong Kong Peoples R China Sir Run Run Shaw Hosp Dept Radiol Hangzhou Peoples R China Zhejiang Lab Res Ctr Healthcare Data Sci Hangzhou Peoples R China Ritsumeikan Univ Coll Informat Sci & Engn Kusatsu Japan Hong Kong Univ Sci & Technol Dept Chem & Biol Engn Hong Kong Peoples R China

ISBN: (纸本)9783031439865;9783031439872

Whole Slide image (WSI) classification remains a challenge due to their extremely high resolution and the absence of fine-grained labels. Presently, WSI classification is usually regarded as a Multiple Instance Learning (MIL) problem when only slide-level labels are available. MIL methods involve a patch embedding module and a bag-level classification module, but they are prohibitively expensive to be trained in an end-to-end manner. therefore, existing methods usually train them separately, or directly skip the training of the embedder. Such schemes hinder the patch embedder's access to slide-level semantic labels, resulting in inconsistency within the entireMIL pipeline. To overcome this issue, we propose a novel framework called Iteratively Coupled MIL (ICMIL), which bridges the loss back-propagation process from the bag-level classifier to the patch embedder. In ICMIL, we use category information in the bag-level classifier to guide the patch-level fine-tuning of the patch feature extractor. the refined embedder then generates better instance representations for achieving a more accurate bag-level classifier. By coupling the patch embedder and bag classifier at a low cost, our proposed framework enables information exchange between the two modules, benefiting the entire MIL classification model. We tested our framework on two datasets using three different backbones, and our experimental results demonstrate consistent performance improvements over state-of-the-art MIL methods. the code is available at: https://***/Dootmaan/ICMIL.

关键词： Multiple Instance Learning Whole Slide image Deep Learning

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Data-Driven Multi-modal Partial medical image Preregistration by Template Space Patch Mapping 25th

Data-Driven Multi-modal Partial Medical Image Preregistratio...

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

作者： Xia, Ding Yang, Xi van Kaick, Oliver Kin, Taichi Igarashi, Takeo Univ Tokyo 7 Chome 3-1 Hong Bunkyo City Tokyo 1138654 Japan Jilin Univ 2699 Qianjin St Changchun 130012 Jilin Peoples R China Carleton Univ 1125 Colonel Ottawa ON K1S 5B6 Canada

ISBN: (纸本)9783031164460;9783031164453

image registration is an essential part of medical image Analysis. Traditional local search methods (e.g., Mean Square Errors (MSE) and Normalized Mutual Information (NMI)) achieve accurate registration but require good initialization. However, finding a good initialization is difficult in partial image matching. Recent deep learning methods such as images-to-transformation directly solve the registration problem but need images of mostly same sizes and already roughly aligned. this work presents a learning-based method to provide good initialization for partial image registration. A light and efficient network learns the mapping from a small patch of an image to a position in the template space for each modality. After computing such mapping for a set of patches, we compute a rigid transformation matrix that maps the patches to the corresponding target positions. We tested our method to register a 3DRA image of a partial brain to a CT image of a whole brain. the result shows that MSE registration with our initialization significantly outperformed baselines including naive initialization and recent deep learning methods without template. You can access our source code in https://***/ApisXia/PartialMedPreregistration.

关键词： Multi-modal Partial image Preregistration Patch mapping

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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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