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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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Classification-Aided High-Quality PET image Synthesis via Bidirectional Contrastive GAN with Shared Information Maximization 25th

Classification-Aided High-Quality PET Image Synthesis via Bi...

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

作者： Fei, Yuchen Zu, Chen Jiao, Zhengyang Wu, Xi Zhou, Jiliu Shen, Dinggang Wang, Yan Sichuan Univ Sch Comp Sci Chengdu Peoples R China JD COM Dept Risk Controlling Res Chengdu Peoples R China Chengdu Univ Informat Technol Sch Comp Sci Chengdu Peoples R China ShanghaiTech Univ Sch Biomed Engn Chengdu Peoples R China Shanghai United Imaging Intelligence Co Ltd Dept Res & Dev Shanghai Peoples R China

ISBN: (纸本)9783031164460;9783031164453

Positron emission tomography (PET) is a pervasively adopted nuclear imaging technique, however, its inherent tracer radiation inevitably causes potential health hazards to patients. To obtain high-quality PET image while reducing radiation exposure, this paper proposes an algorithm for high-quality standard-dose PET (SPET) synthesis from low-dose PET (LPET) image. Specifically, considering that LPET images and SPET images come from the same subjects, we argue that there is abundant shared content and structural information between LPET and SPET domains, which is helpful for improving synthesis performance. To this end, we innovatively propose a bi-directional contrastive generative adversarial network (BiC-GAN), containing a master network and an auxiliary network. Both networks implement intra-domain reconstruction and inter-domain synthesis tasks, aiming to extract shared information from LPET and SPET domains, respectively. Meanwhile, the contrastive learning strategy is also introduced to two networks for enhancing feature representation capability and acquiring more domain-independent information. To maximize the shared information extracted from two domains, we further design a domain alignment module to constrain the consistency of the shared information extracted from the two domains. On the other hand, since synthesized PET images can be used to assist disease diagnosis, such as mild cognitive impairment (MCI) identification, the MCI classification task is incorporated into PET image synthesis to further improve clinical applicability of the synthesized PET image through direct feedback from the classification task. Evaluated on a Real Human Brain dataset, our proposed method is demonstrated to achieve state-of-the-art performance quantitatively and qualitatively.

关键词： Positron emission tomography (PET) Contrastive learning Shared information Generative adversarial network (GAN) image synthesis

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ACT: Semi-supervised Domain-Adaptive medical image Segmentation with Asymmetric Co-training 25th

ACT: Semi-supervised Domain-Adaptive Medical Image Segmentat...

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

作者： Liu, Xiaofeng Xing, Fangxu Shusharina, Nadya Lim, Ruth Kuo, C. -C. Jay El Fakhri, Georges Woo, Jonghye Massachusetts Gen Hosp Dept Radiol Gordon Ctr Med Imaging Boston MA 02114 USA Harvard Med Sch Boston MA 02114 USA Massachusetts Gen Hosp Div Radiat Biophys Dept Radiat Oncol Boston MA 02114 USA Univ Southern Calif Dept Elect & Comp Engn Los Angeles CA 90007 USA

ISBN: (纸本)9783031164439;9783031164422

Unsupervised domain adaptation (UDA) has been vastly explored to alleviate domain shifts between source and target domains, by applying a well-performed model in an unlabeled target domain via supervision of a labeled source domain. Recent literature, however, has indicated that the performance is still far from satisfactory in the presence of significant domain shifts. Nonetheless, delineating a few target samples is usually manageable and particularly worthwhile, due to the substantial performance gain. Inspired by this, we aim to develop semisupervised domain adaptation (SSDA) for medical image segmentation, which is largely underexplored. We, thus, propose to exploit both labeled source and target domain data, in addition to unlabeled target data in a unified manner. Specifically, we present a novel asymmetric co-training (ACT) framework to integrate these subsets and avoid the domination of the source domain data. Following a divide-and-conquer strategy, we explicitly decouple the label supervisions in SSDA into two asymmetric sub-tasks, including semi-supervised learning (SSL) and UDA, and leverage different knowledge from two segmentors to take into account the distinction between the source and target label supervisions. the knowledge learned in the two modules is then adaptively integrated with ACT, by iteratively teaching each other, based on the confidence-aware pseudolabel. In addition, pseudo label noise is well-controlled with an exponential MixUp decay scheme for smooth propagation. Experiments on cross-modality brain tumor MRI segmentation tasks using the BraTS18 database showed, even with limited labeled target samples, ACT yielded marked improvements over UDA and state-of-the-art SSDA methods and approached an "upper bound" of supervised joint training.

关键词： Iterative methods

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DSP-Net: Deeply-Supervised Pseudo-Siamese Network for Dynamic Angiographic image Matching 25th

DSP-Net: Deeply-Supervised Pseudo-Siamese Network for Dynami...

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

作者： Ma, Xi-Yao Liu, Shi-Qi Xie, Xiao-Liang Zhou, Xiao-Hu Hou, Zeng-Guang Zhou, Yan-Jie Song, Meng Zhang, Lin-Sen Wang, Chao-Nan Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Univ Chinese Acad Sci Sch Artificial Intelligence Beijing 100049 Peoples R China Chinese Acad Sci Ctr Excellence Brain Sci & Intelligence Technol Beijing 100190 Peoples R China Univ Sci & Technol Beijing Beijing 100083 Peoples R China Peking Union Med Coll Hosp Beijing 100730 Peoples R China Chinese Acad Med Sci Beijing 100730 Peoples R China

ISBN: (纸本)9783031164491;9783031164484

During percutaneous coronary intervention (PCI), severe elastic deformation of coronary arteries caused by cardiac movement is a serious disturbance to physicians. It increases the difficulty of estimating the relative position between interventional instruments and vessels, leading to inaccurate operation and higher intraoperative mortality. Providing doctors with dynamic angiographic images can be helpful. However, it often faces the challenges of indistinguishable features between consecutive frames and multiple modalities caused by individual differences. In this paper a novel deeply-supervised pseudo-siamese network (DSP-Net) is developed to solve the problem. A pseudo siamese attention dense (PSAD) block is designed to extract salient features from X-ray images with noisy background, and the deep supervision architecture is integrated to accelerate convergence. Evaluations are conducted on the CVM X-ray Database built by us, which consists of 51 sequences, showing that the proposed network can not only achieve state-of-the-art matching performance of 3.48 Hausdorff distance and 84.09% guidewire recall rate, but also demonstrate the great generality to images with different heart structures or fluoroscopic angles. Exhaustive experiment results indicate that our DSP-Net has the potential to assist doctors to overcome the visual misjudgment caused by the elastic deformation of the arteries and achieve safer procedure.

关键词： Dynamic angiography Pseudo-siamese network Deep supervision

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6th Joint international Workshops on computing and Visualization for Intravascular Imaging and computer assisted Stenting, CVII-STENT 2017 and 2nd international Workshop on Large-Scale Annotation of Biomedical Data and Expert Label Synthesis, LABELS 2017 held in Conjunction with 20th international conference on medical image computing and computer assisted intervention, MICCAI 2017

6th Joint International Workshops on Computing and Visualiza...

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6th Joint international Workshops on computing and Visualization for Intravascular Imaging and computer assisted Stenting, CVII-STENT 2017 and 2nd international Workshop on Large-Scale Annotation of Biomedical Data and Expert Label Synthesis, LABELS 2017 held in Conjunction with 20th international conference on medical image computing and computer assisted intervention, MICCAI 2017

ISBN: (纸本)9783319675336

the proceedings contain 17 papers. the special focus in this conference is on computing and Visualization for Intravascular Imaging and computer assisted Stenting. the topics include: Robust detection of circles in the vessel contours and application to local probability density estimation;intra-coronary stent localization in intravascular ultrasound sequences, a preliminary study;robust automatic graph-based skeletonization of hepatic vascular trees;DCNN-based automatic segmentation and quantification of aortic thrombus volume: influence of the training approach;vascular segmentation in TOF MRA images of the brain using a deep convolutional neural network;automatic vessel-of-intervention dynamic detection in PCI procedures;exploring the similarity of medical imaging classification problems;real data augmentation for medical image classification;detecting and classifying nuclei on a budget;towards an efficient way of building annotated medical image collections for big data studies;expected exponential loss for gaze-based video and volume ground truth annotation;crowdsourced emphysema assessment;a web-based platform for distributed annotation of computerized tomography scans;training deep convolutional neural networks with active learning for exudate classification in eye fundus images and uncertainty driven multi-loss fully convolutional networks for histopathology.

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Point Beyond Class: A Benchmark for Weakly Semi-supervised Abnormality Localization in Chest X-Rays 1

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

作者： Ji, Haoqin Liu, Haozhe Li, Yuexiang Xie, Jinheng He, Nanjun Huang, Yawen Wei, Dong Chen, Xinrong Shen, Linlin Zheng, Yefeng Shenzhen Univ Comp Vision Inst Coll Comp Sci & Software Engn Shenzhen Peoples R China Shenzhen Univ Res Ctr Med Image Anal & Diag Shenzhen Peoples R China Tencent Jarvis Lab Shenzhen Peoples R China Fudan Univ Acad Engn & Technol Shanghai Peoples R China

ISBN: (数字)9783031164378

ISBN: (纸本)9783031164378;9783031164361

Accurate abnormality localization in chest X-rays (CXR) can benefit the clinical diagnosis of various thoracic diseases. However, the lesion-level annotation can only be performed by experienced radiologists, and it is tedious and time-consuming, thus difficult to acquire. Such a situation results in a difficulty to develop a fully-supervised abnormality localization system for CXR. In this regard, we propose to train the CXR abnormality localization framework via a weakly semi-supervised strategy, termed Point Beyond Class (PBC), which utilizes a small number of fully annotated CXRs with lesion-level bounding boxes and extensive weakly annotated samples by points. Such a point annotation setting can provide weakly instance-level information for abnormality localization with a marginal annotation cost. Particularly, the core idea behind our PBC is to learn a robust and accurate mapping from the point annotations to the bounding boxes against the variance of annotated points. To achieve that, a regularization term, namely multi-point consistency, is proposed, which drives the model to generate the consistent bounding box from different point annotations inside the same abnormality. Furthermore, a self-supervision, termed symmetric consistency, is also proposed to deeply exploit the useful information from the weakly annotated data for abnormality localization. Experimental results on RSNA and VinDr-CXR datasets justify the effectiveness of the proposed method. When <= 20% box-level labels are used for training, an improvement of similar to 5% in mAP can be achieved by our PBC, compared to the current state-of-the-art method (i.e., Point DETR).

关键词： Weakly supervised learning Semi-supervised learning Regularization consistency

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All Sizes Matter: Improving Volumetric Brain Segmentation on Small Lesions 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： Erdur, Ayhan Can Scholz, Daniel Buchner, Josef A. Combs, Stephanie E. Rueckert, Daniel Peeken, Jan C. Department of Radiation Oncology TUM School of Medicine and Health Klinikum rechts der Isar Technical University of Munich Munich Germany and TUM University Hospital Munich Germany Department of Neuroradiology TUM School of Medicine and Health Klinikum rechts der Isar Technical University of Munich Munich Germany Partner Site Munich Munich Germany Helmholtz Zentrum Munich Munich Germany Department of Computing Imperial College London London United Kingdom

ISBN: (数字)9783031761638

ISBN: (纸本)9783031761621

Brain metastases (BMs) are the most frequently occurring brain tumors. the treatment of patients having multiple BMs with stereotactic radiosurgery necessitates accurate localization of the metastases. Neural networks can assist in this time-consuming and costly task that is typically performed by human experts. Particularly challenging is the detection of small lesions since they are often underrepresented in existing approaches. Yet, lesion detection is equally important for all sizes. In this work, we develop an ensemble of neural networks explicitly focused on detecting and segmenting small BMs. To accomplish this task, we trained several neural networks focusing on individual aspects of the BM segmentation problem: We use blob loss that specifically addresses the imbalance of lesion instances in terms of size and texture and is, therefore, not biased towards larger lesions. In addition, a model using a subtraction sequence between the T1 and T1 contrast-enhanced sequence focuses on low-contrast lesions. Furthermore, we train additional models only on small lesions. Our experiments demonstrate the utility of the additional blob loss and the subtraction sequence. However, including the specialized small lesion models in the ensemble deteriorates segmentation results. We also find domain-knowledge-inspired postprocessing steps to drastically increase our performance in most experiments. Our approach enables us to submit a challenge entry to the ASNR-MICCAI BraTS Brain Metastasis Challenge 2023. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Electroencephalography

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Masked Video Modeling with Correlation-Aware Contrastive Learning for Breast Cancer Diagnosis in Ultrasound 1

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1st international Workshop on Resource-Efficient medical image Analysis (REMIA) / 25th international conference on medical image computing and computer-assisted intervention (MICCAI)

作者： Lin, Zehui Huang, Ruobing Ni, Dong Wu, Jiayi Luo, Baoming Shenzhen Univ Hlth Sci Ctr 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 Sun Yat Sen Univ Sun Yat Sen Mem Hosp Dept Ultrasound Guangzhou Peoples R China

ISBN: (数字)9783031168765

ISBN: (纸本)9783031168765;9783031168758

Breast cancer is one of the leading causes of cancer deaths in women. As the primary output of breast screening, breast ultrasound (US) video contains exclusive dynamic information for cancer diagnosis. However, training models for video analysis is non-trivial as it requires a voluminous dataset which is also expensive to annotate. Furthermore, the diagnosis of breast lesion faces unique challenges such as inter-class similarity and intra-class variation. In this paper, we propose a pioneering approach that directly utilizes US videos in computer-aided breast cancer diagnosis. It leverages masked video modeling as pretraning to reduce reliance on dataset size and detailed annotations. Moreover, a correlation-aware contrastive loss is developed to facilitate the identifying of the internal and external relationship between benign and malignant lesions. Experimental results show that our proposed approach achieved promising classification performance and can outperform other state-of-the-art methods.

关键词： Ultrasonics

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Spatio-Temporal Motion Correction and Iterative Reconstruction of In-Utero Fetal fMRI 25th

Spatio-Temporal Motion Correction and Iterative Reconstructi...

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

作者： Taymourtash, Athena Kebiri, Hamza Schwartz, Ernst Nenning, Karl-Heinz Tourbier, Sebastien Kasprian, Gregor Prayer, Daniela Cuadra, Meritxell Bach Langs, Georg Med Univ Vienna Dept Biomed Imaging & Image Guided Therapy Computat Imaging Res Lab Vienna Austria Lausanne Univ Hosp Dept Radiol Med Image Anal Lab Lausanne Switzerland Univ Lausanne Lausanne Switzerland CIBM Ctr Biomed Imaging Vaud Switzerland Nathan S Kline Inst Psychiat Res Ctr Biomed Imaging & Neuromodulat Orangeburg NY USA Med Univ Vienna Div Neuroradiol & Muskulo Skeletal Radiol Dept Biomed Imaging & Image Guided Therapy Vienna Austria

ISBN: (纸本)9783031164460;9783031164453

Resting-state functional Magnetic Resonance Imaging (fMRI) is a powerful imaging technique for studying functional development of the brain in utero. However, unpredictable and excessive movement of fetuses have limited its clinical applicability. Previous studies have focused primarily on the accurate estimation of the motion parameters employing a single step 3D interpolation at each individual time frame to recover a motion-free 4D fMRI image. Using only information from a 3D spatial neighborhood neglects the temporal structure of fMRI and useful information from neighboring timepoints. Here, we propose a novel technique based on four dimensional iterative reconstruction of the motion scattered fMRI slices. Quantitative evaluation of the proposed method on a cohort of real clinical fetal fMRI data indicates improvement of reconstruction quality compared to the conventional 3D interpolation approaches.

关键词： Fetal fMRI image reconstruction Motion-compensated recovery Regularization

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RobNODDI: Robust NODDI Parameter Estimation with Adaptive Sampling Under Continuous Representation 15th

RobNODDI: Robust NODDI Parameter Estimation with Adaptive 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： Xiao, Taohui Cheng, Jian Fan, Wenxin Yang, Jing Li, Cheng Dong, Enqing Wang, Shanshan School of Mechanical Electrical and Information Engineering Shandong University Weihai264209 China State Key Laboratory of Software Development Environment Beihang University Beijing China Key Laboratory of Data Science and Intelligent Computing Institute of International Innovation Beihang University Zhejiang Hangzhou China Paul C. Lauterbur Research Center for Biomedical Imaging Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Guangdong Shenzhen China Peng Cheng Laboratory Guangdong Shenzhen China

ISBN: (纸本)9783031869198

Neurite Orientation Dispersion and Density Imaging (NODDI) is an important imaging technology used to evaluate the microstructure of brain tissue, which is of great significance for the discovery and treatment of various neurological diseases. Current deep learning-based methods perform parameter estimation through diffusion magnetic resonance imaging (dMRI) with a small number of diffusion gradients. these methods speed up parameter estimation and improve accuracy. However, the diffusion directions used by most existing deep learning models during testing needs to be strictly consistent with the diffusion directions during training. this results in poor generalization and robustness of deep learning models in dMRI parameter estimation. In this work, we first verify that the parameter estimation performance of current mainstream methods will significantly decrease when the testing diffusion directions and the training diffusion directions are inconsistent. A robust NODDI parameter estimation method with adaptive sampling under continuous representation (RobNODDI) is proposed. Furthermore, long short-term memory (LSTM) units and fully connected layers are selected to learn continuous representation signals. To this end, we use a total of 100 subjects to conduct experiments based on the Human Connectome Project (HCP) dataset, of which 60 are used for training, 20 are used for validation, and 20 are used for testing. the test results indicate that RobNODDI improves the generalization performance and robustness of the deep learning model, enhancing the stability and flexibility of deep learning NODDI parameter estimatimation applications. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Deep learning

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Gigapixel Whole-Slide images Classification Using Locally Supervised Learning 25th

Gigapixel Whole-Slide Images Classification Using Locally Su...

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

作者： Zhang, Jingwei Zhang, Xin Ma, Ke Gupta, Rajarsi Saltz, Joel Vakalopoulou, Maria Samaras, Dimitris SUNY Stony Brook New York NY USA Snap Inc Santa Monica CA USA Univ Paris Saclay Cent Supelec Gif Sur Yvette France

ISBN: (纸本)9783031164347;9783031164330

Histopathology whole slide images (WSIs) play a very important role in clinical studies and serve as the gold standard for many cancer diagnoses. However, generating automatic tools for processing WSIs is challenging due to their enormous sizes. Currently, to deal with this issue, conventional methods rely on a multiple instance learning (MIL) strategy to process a WSI at patch level. Although effective, such methods are computationally expensive, because tiling a WSI into patches takes time and does not explore the spatial relations between these tiles. To tackle these limitations, we propose a locally supervised learning framework which processes the entire slide by exploring the entire local and global information that it contains. this framework divides a pre-trained network into several modules and optimizes each module locally using an auxiliary model. We also introduce a random feature reconstruction unit (RFR) to preserve distinguishing features during training and improve the performance of our method by 1% to 3%. Extensive experiments on three publicly available WSI datasets: TCGA-NSCLC, TCGA-RCC and LKS, highlight the superiority of our method on different classification tasks. Our method outperforms the state-of-the-art MIL methods by 2% to 5% in accuracy, while being 7 to 10 times faster. Additionally, when dividing it into eight modules, our method requires as little as 20% of the total gpu memory required by end-to-end training. Our code is available at https://***/cvlab-stonybrook/local_learning_wsi.

关键词： Locally supervised learning Whole slide image Multiple instance learning Classification

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