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Swin-VoxelMorph: A Symmetric Unsupervised Learning Model for Deformable medical image Registration Using Swin Transformer 25th

Swin-VoxelMorph: A Symmetric Unsupervised Learning Model for...

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

作者： Zhu, Yongpei Lu, Shi Tsinghua Univ Grad Sch Shenzhen Shenzhen 518055 Peoples R China

ISBN: (纸本)9783031164460;9783031164453

Deformable medical image registration is widely used in medical image processing with the invertible and one-to-one mapping between images. While state-of-the-art image registration methods are based on convolutional neural networks, few attempts have been made with Transformers which show impressive performance on computer vision tasks. Existing models neglect to employ attention mechanisms to handle the long-range cross-image relevance in embedding learning, limiting such approaches to identify the semantically meaningful correspondence of anatomical structures. these methods also ignore the topology preservation and invertibility of the transformation although they achieve fast image registration. In this paper, we propose a novel, symmetric unsupervised learning network Swin-VoxelMorph based on the Swin Transformer which minimizes the dissimilarity between images and estimates both forward and inverse transformations simultaneously. Specifically, we propose 3D Swin-UNet, which applies hierarchical Swin Transformer with shifted windows as the encoder to extract context features. And a symmetric Swin Transformer-based decoder with patch expanding layer is designed to perform the up-sampling operation to estimate the registration fields. Besides, our objective loss functions can guarantee substantial diffeomorphic properties of the predicted transformations. We verify our method on two datasets including ADNI and PPMI, and it achieves state-of-the-art registration accuracy while maintaining desirable diffeomorphic properties.

关键词： medical image registration Swin transformer Swin-VoxelMorph Diffeomorphic registration fields

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RadimageGAN – A Multi-modal Dataset-Scale Generative AI for medical Imaging 3rd

RadImageGAN – A Multi-modal Dataset-Scale Generative AI for...

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3rd international Workshop on Applications of medical Artificial Intelligence, AMAI 2024 held in conjunction with the 27th international conference on medical image computing and computer assisted intervention, MICCAI 2024

作者： Liu, Zelong Smith, Peyton Lautin, Alexander Zhou, Jieshen Yoo, Maxwell Sullivan, Mikey Li, Haorun Deyer, Louisa Zhou, Alexander Yang, Arnold Yimaz, Alara Zhang, Catherine Grant, James Li, Daiqing Fayad, Zahi A. Huver, Sean Deyer, Timothy Mei, Xueyan BioMedical Engineering and Imaging Institute Icahn School of Medicine at Mount Sinai New YorkNY United States NVIDIA Santa ClaraCA United States East River Medical Imaging New YorkNY United States Department of Radiology Cornell Medicine New YorkNY United States Windreich Department of Artificial Intelligence and Human Health Icahn School of Medicine at Mount Sinai New YorkNY United States

ISBN: (纸本)9783031820069

Deep learning in medical imaging often requires large-scale, high-quality data, or initiation with suitably pre-trained weights. However, medical datasets are limited by data availability, domain-specific knowledge, and privacy concerns, and the creation of large and diverse radiologic databases like RadimageNet is highly resource-intensive. To address these limitations, we introduce RadimageGAN, a multi-modal medical image generator developed by training StyleGAN-XL on the RadimageNet dataset of CT and MRI images and the HyperKvasir dataset of gastrointestinal images. RadimageGAN can generate high-resolution synthetic medical imaging datasets across 12 anatomical regions and 130 pathological classes in 3 modalities. Furthermore, we developed the RadimageGAN-Labeler, which can generate multi-class pixel-wise annotated paired synthetic images and masks for diverse downstream segmentation tasks with minimal manual annotation. Using synthetic auto-labeled data from RadimageGAN can significantly improve performance on four diverse downstream segmentation datasets by augmenting real training data. We find that RadimageGAN can improve model performance and address data scarcity while reducing the resources needed for annotations for segmentation tasks. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： image segmentation

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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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Few-shot medical image Segmentation Regularized with Self-reference and Contrastive Learning 1

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

作者： Wang, Runze Zhou, Qin Zheng, Guoyan Shanghai Jiao Tong Univ Sch Biomed Engn Inst Med Robot 800 Dongchuan Rd Shanghai 200240 Peoples R China

ISBN: (数字)9783031164408

ISBN: (纸本)9783031164408;9783031164392

Despite the great progress made by deep convolutional neural networks (CNN) in medical image segmentation, they typically require a large amount of expert-level accurate, densely-annotated images for training and are difficult to generalize to unseen object categories. Few-shot learning has thus been proposed to address the challenges by learning to transfer knowledge from a few annotated support examples. In this paper, we propose a new prototype-based few-shot segmentation method. Unlike previous works, where query features are compared with the learned support prototypes to generate segmentation over the query images, we propose a self-reference regularization where we further compare support features with the learned support prototypes to generate segmentation over the support images. By this, we argue for that the learned support prototypes should be representative for each semantic class and meanwhile discriminative for different classes, not only for query images but also for support images. We additionally introduce contrastive learning to impose intra-class cohesion and inter-class separation between support and query features. Results from experiments conducted on two publicly available datasets demonstrated the superior performance of the proposed method over the state-of-the-art (SOTA).

关键词： Few-shot medical image segmentation Self-reference Contrastive learning

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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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Enhancing Lung Disease Identification with Multimodal Data Fusion and Deep Learning CNN Approach 5

Enhancing Lung Disease Identification with Multimodal Data F...

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5th international conference on Smart Electronics and Communication, ICOSEC 2024

作者： Grace Shalini, T. Shiny, G. Susan Saranya, R. Suresh Babu, P. Kavitha, R. Atheeswaran, Athiraja Srm Institute of Science and Technology School of Computing Department of Computational Intelligence Chengalpattu Kattankulathur India Sethu Institute of Technology Department of information technology Kariapatti India Velammal college of engineering and technology Department of Eee Madurai India Kalasalingam Academy of Research and Education Department of Computer Science and Engineering Krishnankoil India Bannari Amman Institute Of Technology Department Of Computer Science And Engineering Erode Sathyamangalam India Hyderabad India

ISBN: (纸本)9798331504403

Lung diseases, encompassing a wide spectrum from chronic conditions to acute infections, present a significant burden on global healthcare systems, necessitating precise and timely diagnostic methodologies. In this study, we propose a pioneering approach leveraging multimodal data fusion and deep learning Convolutional Neural Network (CNN) algorithms for enhanced identification of lung diseases. Our methodology integrates diverse data sources including medical images, clinical reports, and patient demographics to capture a comprehensive representation of lung pathology. We employ a state-of-the-art CNN architecture for medical image analysis, trained on a large-scale dataset encompassing various lung diseases and healthy lung tissue. through extensive experimentation and validation, the approach achieved remarkable performance, with an accuracy of 94%, sensitivity of 92%, specificity of 93%, and an F1-score of 93% in accurately identifying and classifying lung abnormalities. Furthermore, we demonstrate the clinical utility of our method in providing valuable insights to healthcare professionals, aiding in timely diagnosis, prognosis, and treatment planning. Our findings highlight the potential of multimodal data fusion coupled with deep learning CNN algorithms in advancing the field of lung disease identification, offering a promising avenue for improving healthcare practices and patient outcomes in respiratory medicine. © 2024 IEEE.

关键词： Lung cancer

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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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A Comprehensive Study of Convolutional Neural Networks for medical image Segmentation 5th

A Comprehensive Study of Convolutional Neural Networks for M...

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5th international conference on Data Science, Machine Learning and Applications, ICDSMLA 2023

作者： Nautiyal, Kapish Augustine, Jacob Savita Rengarajan, A. Department of Electronic & Communication Engineering School of Engineering and Computing Dev Bhoomi Uttarakhand University Navgaon Manduwala Dehradun India School of Computer Science and Engineering Presidency University Karnataka Bengaluru India Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh Lucknow India Karnataka Bangalore India

ISBN: (纸本)9789819780426

It affords an overview latest, the modern-day inside the field of modern-day clinical picture segmentation, after which highlights the gain of trendy CNNs in it. the paper then describes today's CNNs’ fundamentals, architectures, and education techniques. It similarly affords one-of-a-kind CNN fashions used for clinical image segmentation. It presents a comprehensive analysis of new research addressing modern-day CNNs’ utility for this assignment. In the end, the paper affords steering for destiny research guidelines in scientific photo segmentation, the use of CNNs, and an outlook for the ability of today’s this era. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Convolutional neural networks

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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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