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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411-420 订阅

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Learning Site-Specific Styles for Multi-institutional Unsupervised Cross-Modality Domain Adaptation 1

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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： Liu, Han Fan, Yubo Xu, Zhoubing Dawant, Benoit M. Oguz, Ipek Vanderbilt University NashvilleTN United States Siemens Healthineers PrincetonNJ United States

ISBN: (数字)9783031761638

ISBN: (纸本)9783031761621

Unsupervised cross-modality domain adaptation is a challenging task in medical image analysis, and it becomes more challenging when source and target domain data are collected from multiple institutions. In this paper, we present our solution to tackle the multi-institutional unsupervised domain adaptation for the crossMoDA 2023 challenge. First, we perform unpaired image translation to translate the source domain images to the target domain, where we design a dynamic network to generate synthetic target domain images with controllable, site-specific styles. Afterwards, we train a segmentation model using the synthetic images and further reduce the domain gap by self-training. Our solution achieved the 1st place during both the validation and testing phases of the challenge. the code repository is publicly available at https://***/MedICL-VU/crossmoda2023. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： image segmentation

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Shape-Based Pose Estimation for Automatic Standard Views of the Knee 26th

Shape-Based Pose Estimation for Automatic Standard Views of ...

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

作者： Kausch, Lisa thomas, Sarina Kunze, Holger El Barbari, Jan Siad Maier-Hein, Klaus H. German Canc Res Ctr Div Med Image Comp Heidelberg Germany Natl Ctr Tumor Dis NCT Heidelberg Germany Heidelberg Univ Hosp Dept Radiat Oncol Pattern Anal & Learning Grp Heidelberg Germany Univ Oslo Dept Informat Oslo Norway Siemens Healthineers Adv Therapy Syst Div Erlangen Germany MINTOS Res Grp Trauma Surg Clin Ludwigshafen Heidelberg Germany

ISBN: (纸本)9783031439896;9783031439902

Surgical treatment of complicated knee fractures is guided by real-time imaging using a mobile C-arm. Immediate and continuous control is achieved via 2D anatomy-specific standard views that correspond to a specific C-arm pose relative to the patient positioning, which is currently determined manually, following a trial-and-error approach at the cost of time and radiation dose. the characteristics of the standard views of the knee suggests that the shape information of individual bones could guide an automatic positioning procedure, reducing time and the amount of unnecessary radiation during C-arm positioning. To fully automate the C-arm positioning task during knee surgeries, we propose a complete framework that enables (1) automatic laterality and standard view classification and (2) automatic shape-based pose regression toward the desired standard view based on a single initial X-ray. A suitable shape representation is proposed to incorporate semantic information into the pose regression pipeline. the pipeline is designed to handle two distinct standard views with one architecture. Experiments were conducted to assess the performance of the proposed system on 3528 synthetic and 1386 real X-rays for the a.-p. and lateral standard. the view/laterality classificator resulted in an accuracy of 100%/98% on the simulated and 99%/98% on the real X-rays. the pose regression performance was d theta(a.- p) = 5.8 +/- 3.3 degrees, d theta(lateral) = 3.7 +/- 2.0 degrees on the simulated data and d theta(a.- p) = 7.4 +/- 5.0 degrees, d theta(lateral) = 8.4 +/- 5.4 degrees on the real data outperforming intensity-based pose regression.

关键词： Shape-based pose estimation Standard projections Knee

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Modeling Alzheimers' Disease Progression from Multi-task and Self-supervised Learning Perspective with Brain Networks 26th

Modeling Alzheimers' Disease Progression from Multi-task and...

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

作者： Liang, Wei Zhang, Kai Cao, Peng Zhao, Pengfei Liu, Xiaoli Yang, Jinzhu Zaiane, Osmar R. Northeastern Univ Comp Sci & Engn Shenyang Peoples R China Northeastern Univ Key Lab Intelligent Comp Med Image Minist Educ Shenyang Peoples R China Natl Frontiers Sci Ctr Ind Intelligence & Syst Op Shenyang 110819 Peoples R China Nanjing Med Univ Affiliated Brain Hosp Nanjing Peoples R China DAMO Acad Alibaba Grp Hangzhou Peoples R China Univ Alberta Alberta Machine Intelligence Inst Edmonton AB Canada

ISBN: (纸本)9783031439063;9783031439070

Alzheimer's disease (AD) is a common irreversible neurodegenerative disease among elderlies. Establishing relationships between brain networks and cognitive scores plays a vital role in identifying the progression of AD. However, most of the previous works focus on a single time point, without modeling the disease progression with longitudinal brain networks data. Besides, the longitudinal data is insufficient for sufficiently modeling the predictive models. To address these issues, we propose a Self-supervised Multi-Task learning Progression model SMP-Net for modeling the relationship between longitudinal brain networks and cognitive scores. Specifically, the proposed model is trained in a self-supervised way by designing a masked graph auto-encoder and a temporal contrastive learning that simultaneously learn the structural and evolutional features from the longitudinal brain networks. Furthermore, we propose a temporal multi-task learning paradigm to model the relationship among multiple cognitive scores prediction tasks. Experiments on the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset show the effectiveness of our method and achieve consistent improvements over state-of-the-art methods in terms of Mean Absolute Error (MAE), Pearson Correlation Coefficient (PCC) and Concordance Correlation Coefficient (CCC). Our code is available at https://***/IntelliDAL/Graph/tree/main/SMP-Net.

关键词： Self-supervised learning Multi-task learning Cognitive scores Brain networks Longitudinal prediction

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Efficient Population Based Hyperparameter Scheduling for medical image Segmentation 25th

Efficient Population Based Hyperparameter Scheduling for Med...

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

作者： He, Yufan Yang, Dong Myronenko, Andriy Xu, Daguang NVidia Santa Clara CA 95051 USA

ISBN: (纸本)9783031164439;9783031164422

the training hyperparameters (learning rate, augmentation policies, e.t.c) are key factors affecting the performance of deep networks for medical image segmentation. Manual or automatic hyperparameter optimization (HPO) is used to improve the performance. However, manual tuning is infeasible for a large number of parameters, and existing automatic HPO methods like Bayesian optimization are extremely time consuming. Moreover, they can only find a fixed set of hyperparameters. Population based training (PBT) has shown its ability to find dynamic hyperparameters and has fast search speed by using parallel training processes. However, it is still expensive for large 3D medical image datasets with limited GPUs, and the performance lower bound is unknown. In this paper, we focus on improving the network performance using hyperparameter scheduling via PBT with limited computation cost. the core idea is to train the network with a default setting from prior knowledge, and finetune using PBT based hyperparameter scheduling. Our method can achieve 1%-3% performance improvements over default setting while only taking 3%-10% computation cost of training from scratch using PBT.

关键词： Hyperparameter optimization Population based training Bayesian optimization medical image segmentation

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MUVF-YOLOX: A Multi-modal Ultrasound Video Fusion Network for Renal Tumor Diagnosis 26th

MUVF-YOLOX: A Multi-modal Ultrasound Video Fusion Network fo...

引用

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

作者： Li, Junyu Huang, Han Ni, Dong Xue, Wufeng Zhu, Dongmei Cheng, Jun Shenzhen Univ Sch Med Sch Biomed Engn 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 North Sichuan Med Coll Nanchong Cent Hosp Dept Ultrasound Nanchong Peoples R China

ISBN: (纸本)9783031439032;9783031439049

Early diagnosis of renal cancer can greatly improve the survival rate of patients. Contrast-enhanced ultrasound (CEUS) is a cost-effective and non-invasive imaging technique and has become more and more frequently used for renal tumor diagnosis. However, the classification of benign and malignant renal tumors can still be very challenging due to the highly heterogeneous appearance of cancer and imaging artifacts. Our aim is to detect and classify renal tumors by integrating B-mode and CEUS-mode ultrasound videos. To this end, we propose a novel multi-modal ultrasound video fusion network that can effectively perform multi-modal feature fusion and video classification for renal tumor diagnosis. the attention-based multi-modal fusion module uses cross-attention and self-attention to extract modality-invariant features and modality-specific features in parallel. In addition, we design an object-level temporal aggregation (OTA) module that can automatically filter low-quality features and efficiently integrate temporal information from multiple frames to improve the accuracy of tumor diagnosis. Experimental results on a multicenter dataset show that the proposed framework outperforms the single-modal models and the competing methods. Furthermore, our OTA module achieves higher classification accuracy than the frame-level predictions. Our code is available at https://***/JeunyuLi/MUAF.

关键词： Multi-modal Fusion Ultrasound Video Object Detection Renal Tumor

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Clustering Disease Trajectories in Contrastive Feature Space for Biomarker Proposal in Age-Related Macular Degeneration 26th

Clustering Disease Trajectories in Contrastive Feature Space...

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

作者： Holland, Robbie Leingang, Oliver Holmes, Christopher Anders, Philipp Kaye, Rebecca Riedl, Sophie Paetzold, Johannes C. Ezhov, Ivan Bogunovic, Hrvoje Schmidt-Erfurth, Ursula Scholl, Hendrik P. N. Sivaprasad, Sobha Lotery, Andrew J. Rueckert, Daniel Menten, Martin J. Imperial Coll London BioMedIA London England Med Univ Vienna Lab Ophthalm Image Anal Vienna Austria Moorfields Eye Hosp NHS Fdn Trust London England Inst Mol & Clin Ophthalmol Basel Basel Switzerland Univ Basel Dept Ophthalmol Basel Switzerland Univ Southampton Clin & Expt Sci Southampton Hants England Tech Univ Munich Munich Germany

ISBN: (纸本)9783031439896;9783031439902

Age-related macular degeneration (AMD) is the leading cause of blindness in the elderly. Current grading systems based on imaging biomarkers only coarsely group disease stages into broad categories that lack prognostic value for future disease progression. It is widely believed that this is due to their focus on a single point in time, disregarding the dynamic nature of the disease. In this work, we present the first method to automatically propose biomarkers that capture temporal dynamics of disease progression. Our method represents patient time series as trajectories in a latent feature space built with contrastive learning. then, individual trajectories are partitioned into atomic sub-sequences that encode transitions between disease states. these are clustered using a newly introduced distance metric. In quantitative experiments we found our method yields temporal biomarkers that are predictive of conversion to late AMD. Furthermore, these clusters were highly interpretable to ophthalmologists who confirmed that many of the clusters represent dynamics that have previously been linked to the progression of AMD, even though they are currently not included in any clinical grading system.

关键词： Contrastive learning Biomarker discovery Clustering Disease trajectories Age-related macular degeneration

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Multi-IMU with Online Self-consistency for Freehand 3D Ultrasound Reconstruction 26th

Multi-IMU with Online Self-consistency for Freehand 3D Ultra...

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

作者： Luo, Mingyuan Yang, Xin Yan, Zhongnuo Li, Junyu Zhang, Yuanji Chen, Jiongquan Hu, Xindi Qian, Jikuan Cheng, Jun 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 RayShape Med Technol Inc Shenzhen Peoples R China

ISBN: (纸本)9783031439063;9783031439070

Ultrasound (US) imaging is a popular tool in clinical diagnosis, offering safety, repeatability, and real-time capabilities. Freehand 3D US is a technique that provides a deeper understanding of scanned regions without increasing complexity. However, estimating elevation displacement and accumulation error remains challenging, making it difficult to infer the relative position using images alone. the addition of external lightweight sensors has been proposed to enhance reconstruction performance without adding complexity, which has been shown to be beneficial. We propose a novel online self-consistency network (OSCNet) using multiple inertial measurement units (IMUs) to improve reconstruction performance. OSCNet utilizes a modal-level self-supervised strategy to fuse multiple IMU information and reduce differences between reconstruction results obtained from each IMU data. Additionally, a sequence-level self-consistency strategy is proposed to improve the hierarchical consistency of prediction results among the scanning sequence and its sub-sequences. Experiments on large-scale arm and carotid datasets with multiple scanning tactics demonstrate that our OSCNet outperforms previous methods, achieving state-of-the-art reconstruction performance.

关键词： Multiple IMU Online Learning Freehand 3D Ultrasound

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Multi-model Ensemble and Region Specific Normalization for Myocardial Pathology Segmentation with Partial Modalities 1st

Multi-model Ensemble and Region Specific Normalization for...

引用

1st MICCAI Challenge Comprehensive Analysis and computing of Real-World medical images, CARE 2024 Held in Conjunction with 27th international conference on medical image computing and computer-assisted intervention, MICCAI 2024

作者： Luo, Zihao Wang, Guotai School of Mechanical and Electrical Engineering University of Electronic Science and Technology of China Chengdu China Shanghai AI Lab Shanghai China

ISBN: (纸本)9783031870088

Myocardial pathology segmentation in cardiac magnetic resonance (CMR) images is crucial for diagnosing and managing cardiovascular diseases. However, the challenge of partial modality availability in multi-sequence datasets complicates this task. In this paper, we propose a simple but efficient approach that combines a two-stage prediction strategy with a multi-model ensemble and Region Specific Normalization (RSN) to improve segmentation accuracy. Our method leverages the strengths of models trained on different combinations of CMR sequences, resulting in significant improvements in the Dice scores for myocardial scars and edema. RSN enhances contrast within regions of interest, reducing the impact of artifacts and intensity variability, thereby refining segmentation outcomes. We evaluate our approach using the CARE 2024 MyoPS++ challenge dataset, which includes data from multiple centers with diverse modality availability. the results demonstrate that our method outperforms full-sequence models, both quantitatively and qualitatively, providing a robust and effective solution for myocardial pathology segmentation in clinical settings. Our method achieved second place in the CARE 2024 MyoPS++ challenge, highlighting its efficacy and competitiveness. Code and model are available: https://***/Zihaoluoh/CARE-2024_MyoPS-. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： image segmentation

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Joint Modeling of image and Label Statistics for Enhancing Model Generalizability of medical image Segmentation 25th

Joint Modeling of Image and Label Statistics for Enhancing M...

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

作者： Gao, Shangqi Zhou, Hangqi Gao, Yibo Zhuang, Xiahai Fudan Univ Sch Data Sci Shanghai 200433 Peoples R China

ISBN: (纸本)9783031164439;9783031164422

Although supervised deep-learning has achieved promising performance in medical image segmentation, many methods cannot generalize well on unseen data, limiting their real-world applicability. To address this problem, we propose a deep learning-based Bayesian framework, which jointly models image and label statistics, utilizing the domain-irrelevant contour of a medical image for segmentation. Specifically, we first decompose an image into components of contour and basis. then, we model the expected label as a variable only related to the contour. Finally, we develop a variational Bayesian framework to infer the posterior distributions of these variables, including the contour, the basis, and the label. the framework is implemented with neural networks, thus is referred to as deep Bayesian segmentation. Results on the task of cross-sequence cardiac MRI segmentation show that our method set a new state of the art for model generalizability. Particularly, the BayeSeg model trained with LGE MRI generalized well on T2 images and outperformed other models with great margins, i.e., over 0.47 in terms of average Dice. Our code is available at https: //***/***.

关键词： Bayesian segmentation image decomposition Model generalizability Deep learning

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UNeXt: MLP-Based Rapid medical image Segmentation Network 25th

UNeXt: MLP-Based Rapid Medical Image Segmentation Network

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

作者： Valanarasu, Jeya Maria Jose Patel, Vishal M. Johns Hopkins Univ Baltimore MD 21218 USA

ISBN: (纸本)9783031164439;9783031164422

UNet and its latest extensions like TransUNet have been the leading medical image segmentation methods in recent years. However, these networks cannot be effectively adopted for rapid image segmentation in point-of-care applications as they are parameter-heavy, computationally complex and slow to use. To this end, we propose UNeXt which is a Convolutional multilayer perceptron (MLP) based network for image segmentation. We design UNeXt in an effective way with an early convolutional stage and a MLP stage in the latent stage. We propose a tokenized MLP block where we efficiently tokenize and project the convolutional features and use MLPs to model the representation. To further boost the performance, we propose shifting the channels of the inputs while feeding in to MLPs so as to focus on learning local dependencies. Using tokenized MLPs in latent space reduces the number of parameters and computational complexity while being able to result in a better representation to help segmentation. the network also consists of skip connections between various levels of encoder and decoder. We test UNeXt on multiple medical image segmentation datasets and show that we reduce the number of parameters by 72x, decrease the computational complexity by 68x, and improve the inference speed by 10x while also obtaining better segmentation performance over the state-of-the-art medical image segmentation architectures.

关键词： medical image segmentation MLP Point-of-care

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