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检索条件"任意字段=5th International Conference on Medical Image Computing and Computer-Assisted Intervention"

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Flexible Unfolding of Circular Structures for Rendering Textbook-Style Cerebrovascular Maps 26th

Flexible Unfolding of Circular Structures for Rendering Text...

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

作者： Rist, Leonhard Taubmann, Oliver Ditt, Hendrik Suhling, Michael Maier, Andreas Friedrich Alexander Univ Erlangen Nurnberg Erlangen Germany Siemens Healthineers CT R&D Image Analyt Forchheim Germany

ISBN: (纸本)9783031439032;9783031439049

Comprehensive, contiguous visualizations of the main cerebral arteries and the surrounding parenchyma offer considerable potential for improving diagnostic workflows in cerebrovascular disease, e.g., for fast assessment of vascular topology and lumen in stroke patients. Unfolding the brain vasculature into a 2D overview is, however, infeasible using common Curved Planar Reformation (CPR) due to the circular structure of the Circle of Willis (CoW) and the spatial configuration of the vessels typically rendering them unsuitable for mapping onto simple geometric primitives. We propose CeVasMap, extending the As-Rigid-As-Possible (ARAP) deformation by a smart initialization of the required mesh to map the CoW as well as a merging of neighboring vessels depending on the resulting degree of distortion. Otherwise, vessels are unfolded and attached individually, creating a textbook-style overview image. We provide an extensive distortion analysis, comparing the vector fields of individual and merged unfoldings of each vessel to their CPR results. In addition to enabling unfolding of circular structures, our method is on par in terms of incurred distortions to optimally oriented CPRs for individual vessels and comparable to unfavorable CPR orientations when merging the complete CoW with a median distortion of 65 mu m/mm.

关键词： Vessel unfolding Distortion Cerebrovascular disease

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Correlation-Aware Mutual Learning for Semi-supervised medical image Segmentation 26th

Correlation-Aware Mutual Learning for Semi-supervised Medica...

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

作者： Gao, Shengbo Zhang, Ziji Ma, Jiechao Li, Zihao Zhang, Shu Deepwise AI Lab Beijing Peoples R China Beijing Univ Posts & Telecommun Sch Artificial Intelligence Beijing Peoples R China

ISBN: (纸本)9783031439063;9783031439070

Semi-supervised learning has become increasingly popular in medical image segmentation due to its ability to leverage large amounts of unlabeled data to extract additional information. However, most existing semi-supervised segmentation methods only focus on extracting information from unlabeled data, disregarding the potential of labeled data to further improve the performance of the model. In this paper, we propose a novel Correlation Aware Mutual Learning (CAML) framework that leverages labeled data to guide the extraction of information from unlabeled data. Our approach is based on a mutual learning strategy that incorporates two modules: the Cross-sample Mutual Attention Module (CMA) and the Omni-Correlation Consistency Module (OCC). the CMA module establishes dense cross-sample correlations among a group of samples, enabling the transfer of label prior knowledge to unlabeled data. the OCC module constructs omni-correlations between the unlabeled and labeled datasets and regularizes dual models by constraining the omni-correlation matrix of each sub-model to be consistent. Experiments on the Atrial Segmentation Challenge dataset demonstrate that our proposed approach outperforms state-of-the-art methods, highlighting the effectiveness of our framework in medical image segmentation tasks. the codes, pre-trained weights, and data are publicly available.

关键词： Semi-supervised learning medical image Segmentation Mutual learning Cross-sample correlation

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COLosSAL: A Benchmark for Cold-Start Active Learning for 3D medical image Segmentation 26th

COLosSAL: A Benchmark for Cold-Start Active Learning for 3D ...

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

作者： Liu, Han Li, Hao Yao, Xing Fan, Yubo Hu, Dewei Dawant, Benoit M. Nath, Vishwesh Xu, Zhoubing Oguz, Ipek Vanderbilt Univ 221 Kirkland Hall Nashville TN 37235 USA NVIDIA Nashville TN USA Siemens Healthineers Princeton NJ USA

ISBN: (纸本)9783031438943;9783031438950

medical image segmentation is a critical task in medical image analysis. In recent years, deep learning based approaches have shown exceptional performance when trained on a fully-annotated dataset. However, data annotation is often a significant bottleneck, especially for 3D medical images. Active learning (AL) is a promising solution for efficient annotation but requires an initial set of labeled samples to start active selection. When the entire data pool is unlabeled, how do we select the samples to annotate as our initial set? this is also known as the cold-start AL, which permits only one chance to request annotations from experts without access to previously annotated data. Cold-start AL is highly relevant in many practical scenarios but has been under-explored, especially for 3D medical segmentation tasks requiring substantial annotation effort. In this paper, we present a benchmark named COLosSAL by evaluating six cold-start AL strategies on five 3D medical image segmentation tasks from the public medical Segmentation Decathlon collection. We perform a thorough performance analysis and explore important open questions for cold-start AL, such as the impact of budget on different strategies. Our results show that cold-start AL is still an unsolved problem for 3D segmentation tasks but some important trends have been observed. the code repository, data partitions, and baseline results for the complete benchmark are publicly available at https://***/MedICL-VU/COLosSAL.

关键词： Efficient Annotation Active Learning Cold Start image Segmentation

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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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Recruiting the Best Teacher Modality: A Customized Knowledge Distillation Method for if Based Nephropathy Diagnosis 26th

Recruiting the Best Teacher Modality: A Customized Knowledge...

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

作者： Dai, Ning Jiang, Lai Fu, Yibing Pan, Sai Xu, Mai Deng, Xin Chen, Pu Chen, Xiangmei Beihang Univ Sch Elect & Informat Engn Beijing Peoples R China Chinese Peoples Liberat Army Gen Hosp Med Sch Chinese PLA Natl Clin Res Ctr Kidney DisDept Nephrol State Key Lab Kidney DisInst Nephrol Chinese PLA Beijing Peoples R China Beihang Univ Sch Cyber Sci & Technol Beijing Peoples R China

ISBN: (纸本)9783031439032;9783031439049

the joint use of multiple imaging modalities for medical image has been widely studied in recent years. the fusion of information from different modalities has demonstrated the performance improvement for some medical tasks. For nephropathy diagnosis, immunofluorescence (IF) is one of the most widely-used medical image due to its ease of acquisition with low cost, which is also an advanced multi-modality technique. However, the existing methods mainly integrate information from diverse sources by averaging or combining them, failing to exploit multi-modality knowledge in details. In this paper, we observe that the 7 modalities of IF images have different impact on different nephropathy categories. Accordingly, we propose a knowledge distillation framework to transfer knowledge from the trained single-modality teacher networks to a multi-modality student network. On top of this, given a input IF sequence, a recruitment module is developed to dynamically assign weights to teacher models and optimize the performance of student model. By applying on several different architectures, the extensive experimental results verify the effectiveness of our method for nephropathy diagnosis.

关键词： Nephropathy diagnosis IF image Knowledge distillation

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Transformer-Based Annotation Bias-Aware medical image Segmentation 26th

Transformer-Based Annotation Bias-Aware Medical Image Segmen...

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

作者： Liao, Zehui Hu, Shishuai Xie, Yutong Xia, Yong Northwestern Polytech Univ Sch Comp Sci & Engn Natl Engn Lab Integrated Aerospace Ground Ocean B Xian 710072 Peoples R China Univ Adelaide Australian Inst Machine Learning Adelaide SA Australia

ISBN: (纸本)9783031439001;9783031439018

Manual medical image segmentation is subjective and suffers from annotator-related bias, which can be mimicked or amplified by deep learning methods. Recently, researchers have suggested that such bias is the combination of the annotator preference and stochastic error, which are modeled by convolution blocks located after decoder and pixelwise independent Gaussian distribution, respectively. It is unlikely that convolution blocks can effectively model the varying degrees of preference at the full resolution level. Additionally, the independent pixel-wise Gaussian distribution disregards pixel correlations, leading to a discontinuous boundary. this paper proposes a Transformer-based Annotation Bias-aware (TAB) medical image segmentation model, which tackles the annotator-related bias via modeling annotator preference and stochastic errors. TAB employs the Transformer with learnable queries to extract the different preference-focused features. this enables TAB to produce segmentation with various preferences simultaneously using a single segmentation head. Moreover, TAB takes the multivariant normal distribution assumption that models pixel correlations, and learns the annotation distribution to disentangle the stochastic error. We evaluated our TAB on an OD/OC segmentation benchmark annotated by six annotators. Our results suggest that TAB outperforms existing medical image segmentation models which take into account the annotator-related bias. the code is available at https://***/Merrical/TAB.

关键词： medical image segmentation Multiple annotators Transformer Multivariate normal distribution

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OpenAL: An Efficient Deep Active Learning Framework for Open-Set Pathology image Classification 26th

OpenAL: An Efficient Deep Active Learning Framework for Open...

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

作者： Qu, Linhao Ma, Yingfan Yang, Zhiwei Wang, Manning Song, Zhijian Fudan Univ Sch Basic Med Sci Digital Med Res Ctr Shanghai 200032 Peoples R China Shanghai Key Lab Med Image Comp & Comp Assisted I Shanghai 200032 Peoples R China Fudan Univ Acad Engn & Technol Shanghai 200433 Peoples R China

ISBN: (纸本)9783031438943;9783031438950

Active learning (AL) is an effective approach to select the most informative samples to label so as to reduce the annotation cost. Existing AL methods typically work under the closed-set assumption, i.e., all classes existing in the unlabeled sample pool need to be classified by the target model. However, in some practical clinical tasks, the unlabeled pool may contain not only the target classes that need to be fine-grainedly classified, but also non-target classes that are irrelevant to the clinical tasks. Existing AL methods cannot work well in this scenario because they tend to select a large number of non-target samples. In this paper, we formulate this scenario as an open-set AL problem and propose an efficient framework, OpenAL, to address the challenge of querying samples from an unlabeled pool with both target class and non-target class samples. Experiments on fine-grained classification of pathology images show that OpenAL can significantly improve the query quality of target class samples and achieve higher performance than current state-of-the-art AL methods. Code is available at https:// ***/miccaiif/OpenAL.

关键词： Active learning Openset Pathology image classification

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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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MedGen3D: A Deep Generative Framework for Paired 3D image and Mask Generation 26th

MedGen3D: A Deep Generative Framework for Paired 3D Image an...

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

作者： Han, Kun Xiong, Yifeng You, Chenyu Khosravi, Pooya Sun, Shanlin Yan, Xiangyi Duncan, James S. Xie, Xiaohui Univ Calif Irvine Irvine CA 92697 USA Yale Univ New Haven CT USA

ISBN: (纸本)9783031439063;9783031439070

Acquiring and annotating sufficient labeled data is crucial in developing accurate and robust learning-based models, but obtaining such data can be challenging in many medical image segmentation tasks. One promising solution is to synthesize realistic data with ground-truth mask annotations. However, no prior studies have explored generating complete 3D volumetric images with masks. In this paper, we present MedGen3D, a deep generative framework that can generate paired 3D medical images and masks. First, we represent the 3D medical data as 2D sequences and propose the Multi-Condition Diffusion Probabilistic Model (MC-DPM) to generate multi-label mask sequences adhering to anatomical geometry. then, we use an image sequence generator and semantic diffusion refiner conditioned on the generated mask sequences to produce realistic 3D medical images that align with the generated masks. Our proposed framework guarantees accurate alignment between synthetic images and segmentation maps. Experiments on 3D thoracic CT and brain MRI datasets show that our synthetic data is both diverse and faithful to the original data, and demonstrate the benefits for downstream segmentation tasks. We anticipate that MedGen3D's ability to synthesize paired 3D medical images and masks will prove valuable in training deep learning models for medical imaging tasks.

关键词： Deep Generative Framework 3D Volumetric images with Masks Fidelity and Diversity Segmentation

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