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

共 2694 条记录，以下是311-320 订阅

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Transformer-Based Dual-Domain Network for Few-View Dedicated Cardiac SPECT image Reconstructions 26th

Transformer-Based Dual-Domain Network for Few-View Dedicated...

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

作者： Xie, Huidong Zhou, Bo Chen, Xiongchao Guo, Xueqi thorn, Stephanie Liu, Yi-Hwa Wang, Ge Sinusas, Albert Liu, Chi Yale Univ New Haven CT 06511 USA Rensselaer Polytech Inst Troy NY 12180 USA

ISBN: (纸本)9783031439988;9783031439995

Cardiovascular disease (CVD) is the leading cause of death worldwide, and myocardial perfusion imaging using SPECT has been widely used in the diagnosis of CVDs. the GE 530/570c dedicated cardiac SPECT scanners adopt a stationary geometry to simultaneously acquire 19 projections to increase sensitivity and achieve dynamic imaging. However, the limited amount of angular sampling negatively affects image quality. Deep learning methods can be implemented to produce higher-quality images from stationary data. this is essentially a few-view imaging problem. In this work, we propose a novel 3D transformer-based dual-domain network, called TIP-Net, for high-quality 3D cardiac SPECT image reconstructions. Our method aims to first reconstruct 3D cardiac SPECT images directly from projection data without the iterative reconstruction process by proposing a customized projection-to-image domain transformer. then, given its reconstruction output and the original few-view reconstruction, we further refine the reconstruction using an image-domain reconstruction network. Validated by cardiac catheterization images, diagnostic interpretations from nuclear cardiologists, and defect size quantified by an FDA 510(k)-cleared clinical software, our method produced images with higher cardiac defect contrast on human studies compared with previous baseline methods, potentially enabling high-quality defect visualization using stationary few-view dedicated cardiac SPECT scanners.

关键词： Cardiac SPECT Few-view Imaging Transformer

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vox2vec: A Framework for Self-supervised Contrastive Learning of Voxel-Level Representations in medical images 26th

vox2vec: A Framework for Self-supervised Contrastive Learnin...

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

作者： Goncharov, Mikhail Soboleva, Vera Kurmukov, Anvar Pisov, Maxim Belyaev, Mikhail Skolkovo Inst Sci & Technol Moscow Russia Artificial Intelligence Res Inst AIRI Moscow Russia Inst Informat Transmiss Problems Moscow Russia IRA Labs Moscow Russia

ISBN: (纸本)9783031439063;9783031439070

this paper introduces vox2vec - a contrastive method for self-supervised learning (SSL) of voxel-level representations. vox2vec representations are modeled by a Feature Pyramid Network (FPN): a voxel representation is a concatenation of the corresponding feature vectors from different pyramid levels. the FPN is pre-trained to produce similar representations for the same voxel in different augmented contexts and distinctive representations for different voxels. this results in unified multi-scale representations that capture both global semantics (e.g., body part) and local semantics (e.g., different small organs or healthy versus tumor tissue). We use vox2vec to pre-train a FPN on more than 6500 publicly available computed tomography images. We evaluate the pre-trained representations by attaching simple heads on top of them and training the resulting models for 22 segmentation tasks. We show that vox2vec outperforms existing medical imaging SSL techniques in three evaluation setups: linear and non-linear probing and end-to-end fine-tuning. Moreover, a non-linear head trained on top of the frozen vox2vec representations achieves competitive performance with the FPN trained from scratch while having 50 times fewer trainable parameters. the code is available at https://***/mishgon/vox2vec.

关键词： Contrastive Self-Supervised Representation Learning medical image Segmentation

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Gall Bladder Cancer Detection from US images with only image Level Labels 26th

Gall Bladder Cancer Detection from US Images with only Image...

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

作者： Basu, Soumen Papanai, Ashish Gupta, Mayank Gupta, Pankaj Arora, Chetan Indian Inst Technol Delhi India Postgrad Inst Med Educ & Res Chandigarh India

ISBN: (纸本)9783031439063;9783031439070

Automated detection of Gallbladder Cancer (GBC) from Ultrasound (US) images is an important problem, which has drawn increased interest from researchers. However, most of these works use difficult-to-acquire information such as bounding box annotations or additional US videos. In this paper, we focus on GBC detection using only image-level labels. Such annotation is usually available based on the diagnostic report of a patient, and do not require additional annotation effort from the physicians. However, our analysis reveals that it is difficult to train a standard image classification model for GBC detection. this is due to the low inter-class variance (a malignant region usually occupies only a small portion of a US image), high intra-class variance (due to the US sensor capturing a 2D slice of a 3D object leading to large viewpoint variations), and low training data availability. We posit that even when we have only the image level label, still formulating the problem as object detection (with bounding box output) helps a deep neural network (DNN) model focus on the relevant region of interest. Since no bounding box annotations is available for training, we pose the problem as weakly supervised object detection (WSOD). Motivated by the recent success of transformer models in object detection, we train one such model, DETR, using multi-instance-learning (MIL) with self-supervised instance selection to suit the WSOD task. Our proposed method demonstrates an improvement of AP and detection sensitivity over the SOTA transformer-based and CNN-based WSOD methods. Project page is at https://***/wsod-gbc.

关键词： Weakly Supervised Object Detection Ultrasound Gallbladder Cancer

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image2SSM: Reimagining Statistical Shape Models from images with Radial Basis Functions 26th

Image2SSM: Reimagining Statistical Shape Models from Images ...

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

作者： Xu, Hong Elhabian, Shireen Y. Univ Utah Kahlert Sch Comp Sci Comp & Imaging Inst Salt Lake City UT 84112 USA

ISBN: (纸本)9783031439063;9783031439070

Statistical shape modeling (SSM) is an essential tool for analyzing variations in anatomical morphology. In a typical SSM pipeline, 3D anatomical images, gone through segmentation and rigid registration, are represented using lower-dimensional shape features, on which statistical analysis can be performed. Various methods for constructing compact shape representations have been proposed, but they involve laborious and costly steps. We propose image2SSM, a novel deep-learning-based approach for SSM that leverages image-segmentation pairs to learn a radial-basis-function (RBF)-based representation of shapes directly from images. this RBF-based shape representation offers a rich self-supervised signal for the network to estimate a continuous, yet compact representation of the underlying surface that can adapt to complex geometries in a data-driven manner. image2SSM can characterize populations of biological structures of interest by constructing statistical landmark-based shape models of ensembles of anatomical shapes while requiring minimal parameter tuning and no user assistance. Once trained, image2SSM can be used to infer low-dimensional shape representations from new unsegmented images, paving the way toward scalable approaches for SSM, especially when dealing with large cohorts. Experiments on synthetic and real datasets show the efficacy of the proposed method compared to the state-of-art correspondence-based method for SSM.

关键词： Statistical Shape Modeling Deep Learning Radial Basis Function Interpolation Polyharmonic Splines

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CoLa-Diff: Conditional Latent Diffusion Model for Multi-modal MRI Synthesis 26th

CoLa-Diff: Conditional Latent Diffusion Model for Multi-moda...

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

作者： Jiang, Lan Mao, Ye Wang, Xiangfeng Chen, Xi Li, Chao Univ Dundee Sch Sci & Engn Dundee Scotland Univ Cambridge Dept Clin Neurosci Cambridge England East China Normal Univ Sch Comp Sci & Technol Shanghai Peoples R China Univ Bath Dept Comp Sci Bath Avon England Univ Dundee Sch Med Dundee Scotland

ISBN: (纸本)9783031439988;9783031439995

MRI synthesis promises to mitigate the challenge of missing MRI modality in clinical practice. Diffusion model has emerged as an effective technique for image synthesis by modelling complex and variable data distributions. However, most diffusion-based MRI synthesis models are using a single modality. As they operate in the original image domain, they are memory-intensive and less feasible for multi-modal synthesis. Moreover, they often fail to preserve the anatomical structure in MRI. Further, balancing the multiple conditions from multi-modal MRI inputs is crucial for multi-modal synthesis. Here, we propose the first diffusion-based multi-modality MRI synthesis model, namely Conditioned Latent Diffusion Model (CoLa-Diff). To reduce memory consumption, we perform the diffusion process in the latent space. We propose a novel network architecture, e.g., similar cooperative filtering, to solve the possible compression and noise in latent space. To better maintain the anatomical structure, brain region masks are introduced as the priors of density distributions to guide diffusion process. We further present auto-weight adaptation to employ multi-modal information effectively. Our experiments demonstrate that CoLa-Diff outperforms other state-of-the-art MRI synthesis methods, promising to serve as an effective tool for multi-modal MRI synthesis.

关键词： Multi-modal MRI medical image synthesis Latent space Diffusion models Structural guidance

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An Unsupervised Multispectral image Registration Network for Skin Diseases 26th

An Unsupervised Multispectral Image Registration Network for...

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

作者： Diao, Songhui Zhou, Wenxue Qin, Chenchen Liao, Jun Huang, Junzhou Yang, Wenming Yao, Jianhua Chinese Acad Sci Shenzhen Inst Adv Technol Shenzhen Peoples R China Tencent AI Lab Shenzhen Peoples R China Tsinghua Univ Shenzhen Int Grad Sch Shenzhen Peoples R China Univ Texas Arlington Arlington TX 76019 USA

ISBN: (纸本)9783031439988;9783031439995

Multispectral imaging has a broad, promising and advantageous application prospect in the diagnosis of skin diseases. However, there are inherent deviations such as rigid or non-rigid deformation among multi-spectral images (MSI), which makes accurate and robust registration algorithms desirable to extract reliable multispectral features. Existing registration algorithms are susceptible to significant and nonlinear amplitude differences and geometric distortions among MSI, resulting in an unsatisfactory estimation of the registration field (RF). In this study, we propose an end-to-end multispectral image registration (MSIR) network with unsupervised learning for human skin disease diagnosis. First, we propose a basic adjacent-band pair registration (ABPR) model to obtain the corresponding RFs through simultaneously modeling a series of image pairs from adjacent bands. Second, we introduce amultispectral attentionmodule (MAM) for extraction and adaptive weight allocation of the high-level pathological features of multiple MSI pairs. third, we design a registration field refinement module (RFRM) to rectify and reconstruct a general RF solution. Fourth, we propose an unsupervised center-toward registration loss function, combining a similarity loss for features in the frequency domain and a smoothness loss forRF. In addition, we built aMSI dataset of multi-type skin diseases and conducted extensive experiments. the results show that our method not only outperforms state-of-the-art methods on MSI registration task, but also contributes to the subsequent task of benign and malignant disease classification.

关键词： image registration Multispectral image Unsupervised registration Registration field refinement

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Towards Frugal Unsupervised Detection of Subtle Abnormalities in medical Imaging 1

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

作者： Oudoumanessah, Geoffroy Lartizien, Carole Dojat, Michel Forbes, Florence Univ Grenoble Alpes CNRS INRIA Grenoble INPLJK F-38000 Grenoble France Univ Grenoble Alpes Grenoble Inst Neurosci Inserm U1216 CHU Grenoble Alpes F-38000 Grenoble France Univ Lyon CNRS INSERM INSA LyonUCBLCREATISUMR5220U1294 F-69621 Villeurbanne France

ISBN: (数字)9783031438981

ISBN: (纸本)9783031438974;9783031438981

Anomaly detection in medical imaging is a challenging task in contexts where abnormalities are not annotated. this problem can be addressed through unsupervised anomaly detection (UAD) methods, which identify features that do not match with a reference model of normal profiles. Artificial neural networks have been extensively used for UAD but they do not generally achieve an optimal trade-off between accuracy and computational demand. As an alternative, we investigate mixtures of probability distributions whose versatility has been widely recognized for a variety of data and tasks, while not requiring excessive design effort or tuning. their expressivity makes them good candidates to account for complex multivariate reference models. their much smaller number of parameters makes them more amenable to interpretation and efficient learning. However, standard estimation procedures, such as the Expectation-Maximization algorithm, do not scale well to large data volumes as they require high memory usage. To address this issue, we propose to incrementally compute inferential quantities. this online approach is illustrated on the challenging detection of subtle abnormalities in MR brain scans for the follow-up of newly diagnosed Parkinsonian patients. the identified structural abnormalities are consistent with the disease progression, as accounted by the Hoehn and Yahr scale.

关键词： Frugal computing Online EM algorithm Gaussian scale mixture Unsupervised anomaly detection Parkinson's Disease

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CT Kernel Conversion Using Multi-domain image-to-image Translation with Generator-Guided Contrastive Learning 26th

CT Kernel Conversion Using Multi-domain Image-to-Image Trans...

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

作者： Choi, Changyong Jeong, Jiheon Lee, Sangyoon Lee, Sang Min Kim, Namkug Univ Ulsan Coll Med Asan Med Ctr AMISTDept Biomed Engn Seoul South Korea Univ Ulsan Coll Med Asan Med Ctr Dept Convergence Med Seoul South Korea Univ Ulsan Dept Radiol Asan Med Ctr Coll Med Seoul South Korea

ISBN: (纸本)9783031439988;9783031439995

Computed tomography (CT) image can be reconstructed by various types of kernels depending on what anatomical structure is evaluated. Also, even if the same anatomical structure is analyzed, the kernel being used differs depending on whether it is qualitative or quantitative evaluation. thus, CT images reconstructed with different kernels would be necessary for accurate diagnosis. However, once CT image is reconstructed with a specific kernel, the CT raw data, sinogram is usually removed because of its large capacity and limited storage. To solve this problem, many methods have been proposed by using deep learning approach using generative adversarial networks in image-to-image translation for kernel conversion. Nevertheless, it is still challenging task that translated image should maintain the anatomical structure of source image in medical domain. In this study, we propose CT kernel conversion method using multi-domain image-to-image translation with generator-guided contrastive learning. Our proposed method maintains the anatomical structure of the source image accurately and can be easily utilized into other multi-domain image-to-image translation methods with only changing the discriminator architecture and without adding any additional networks. Experimental results showthat our proposed method can translate CT images from sharp into soft kernels and from soft into sharp kernels compared to other image-to-image translation methods. Our code is available at https://git ***/cychoi97/GGCL.

关键词： CT Kernel conversion image-to-image translation Contrastive learning Style transfer

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Explaining Massive-Training Artificial Neural Networks in medical image Analysis Task through Visualizing Functions Within the Models 26th

Explaining Massive-Training Artificial Neural Networks in Me...

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

作者： Jin, Ze Pang, Maolin Yang, Yuqiao Mahdi, Fahad Parvez Qu, Tianyi Sasage, Ren Suzuki, Kenji Tokyo Inst Technol Inst Innovat Res Biomed Artificial Intelligence Res Unit Meguro Kanagawa Japan

ISBN: (纸本)9783031438943;9783031438950

In this study, we proposed a novel explainable artificial intelligence (XAI) technique to explainmassive-training artificial neural networks (MTANNs). Firstly, we optimized the structure of anMTANNto find a compact model that performs equivalently well to the original one. this enables to "condense" functions in a smaller number of hidden units in the network by removing "redundant" units. then, we applied an unsupervised hierarchical clustering algorithm to the function maps in the hidden layers with the single-linkage method. From the clustering and visualization results, we were able to group the hidden units into those with similar functions together and reveal the behaviors and functions of the trained MTANN models. We applied this XAI technique to explain the MTANN model trained to segment liver tumors in CT. the original MTANN model with 80 hidden units (F1 = 0.6894, Dice = 0.7142) was optimized to the one with nine hidden units (F1 = 0.6918, Dice = 0.7005) with almost equivalent performance. the nine hidden units were clustered into three groups, and we found the following three functions: 1) enhancing liver area, 2) suppressing non-tumor area, and 3) suppressing the liver boundary and false enhancement. the results shed light on the "black-box" problem with deep learning (DL) models;and we demonstrated that our proposed XAI technique was able to make MTANN models "transparent".

关键词： Deep Learning Explainable AI (XAI) Visualizing Functions Liver Tumor Segmentation Unsupervised Hierarchical Clustering

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Scaling up 3D Kernels with Bayesian Frequency Re-parameterization for medical image Segmentation 26th

Scaling up 3D Kernels with Bayesian Frequency Re-parameteriz...

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

作者： Lee, Ho Hin Liu, Quan Bao, Shunxing Yang, Qi Yu, Xin Cai, Leon Y. Li, thomas Z. Huo, Yuankai Koutsoukos, Xenofon Landman, Bennett A. Vanderbilt Univ Dept Comp Sci Nashville TN 37212 USA Vanderbilt Univ Dept Elect & Comp Engn Nashville TN 37212 USA Vanderbilt Univ Dept Biomed Engn Nashville TN 37212 USA

ISBN: (纸本)9783031439001;9783031439018

With the inspiration of vision transformers, the concept of depth-wise convolution revisits to provide a large Effective Receptive Field (ERF) using Large Kernel (LK) sizes for medical image segmentation. However, the segmentation performance might be saturated and even degraded as the kernel sizes scaled up (e.g., 21 x 21 x 21) in a Convolutional Neural Network (CNN). We hypothesize that convolution with LK sizes is limited to maintain an optimal convergence for locality learning. While Structural Re-parameterization (SR) enhances the local convergence with small kernels in parallel, optimal small kernel branches may hinder the computational efficiency for training. In this work, we propose RepUX-Net, a pure CNN architecture with a simple large kernel block design, which competes favorably with current network state-of-the-art (SOTA) (e.g., 3D UX-Net, SwinUNETR) using 6 challenging public datasets. We derive an equivalency between kernel reparameterization and the branch-wise variation in kernel convergence. Inspired by the spatial frequency in the human visual system, we extend to vary the kernel convergence into element-wise setting and model the spatial frequency as a Bayesian prior to reparameterize convolutional weights during training. Specifically, a reciprocal function is leveraged to estimate a frequency-weighted value, which rescales the corresponding kernel element for stochastic gradient descent. From the experimental results, RepUX-Net consistently outperforms 3D SOTA benchmarks with internal validation (FLARE: 0.929 to 0.944), external validation (MSD: 0.901 to 0.932, KiTS: 0.815 to 0.847, LiTS: 0.933 to 0.949, TCIA: 0.736 to 0.779) and transfer learning (AMOS: 0.880 to 0.911) scenarios in Dice Score. Both codes and pre-trained models are available at: https://***/MASILab/RepUX-Net.

关键词： Bayesian Frequency Re-parameterization Large Kernel Convolution medical image Segmentation

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