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PLD-AL: Pseudo-label Divergence-Based Active Learning in Carotid Intima-Media Segmentation for Ultrasound images 26th

PLD-AL: Pseudo-label Divergence-Based Active Learning in Car...

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

作者： Tang, Yucheng Hu, Yipeng Li, Jing Lin, Hu Xu, Xiang Huang, Ke Lin, Hongxiang Zhejiang Univ Sch Math Sci Hangzhou Peoples R China Zhejiang Lab Hangzhou Peoples R China UCL Ctr Med Image Comp & Wellcome EPSRC Ctr Intervent & Surg Sci London England Zhejiang Univ Childrens Hosp Dept Endocrinol Sch Med Hangzhou Peoples R China

ISBN: (纸本)9783031438943;9783031438950

Segmentation of the carotid intima-media (CIM) offers more precise morphological evidence for obesity and atherosclerotic disease compared to the method that measures its thickness and roughness during routine ultrasound scans. Although advanced deep learning technology has shown promise in enabling automatic and accurate medical image segmentation, the lack of a large quantity of high-quality CIM labels may hinder the model training process. Active learning (AL) tackles this issue by iteratively annotating the subset whose labels contribute the most to the training performance at each iteration. However, this approach substantially relies on the expert's experience, particularly when addressing ambiguous CIM boundaries that may be present in real-world ultrasound images. Our proposed approach, called pseudo-label divergence-based active learning (PLD-AL), aims to train segmentation models using a gradually enlarged and refined labeled pool. the approach has an outer and an inner loops: the outer loop calculates the Kullback-Leibler (KL) divergence of predictive pseudo-labels related to two consecutive AL iterations. It determines which portion of the unlabeled pool should be annotated by an expert. the inner loop trains two networks: the student network is fully trained on the current labeled pool, while the teacher network is weighted upon itself and the student one, ultimately refining the labeled pool. We evaluated our approach using both the Carotid Ultrasound Boundary Study dataset and an in-house dataset from Children's Hospital, Zhejiang University School of Medicine. Our results demonstrate that our approach outperforms state-of-the-art AL approaches. Furthermore, the visualization results show that our approach less overestimates the CIM area than the rest methods, especially for severely ambiguous ultrasound images at the thickness direction.

关键词： Carotid intima-media complex active learning image segmentation

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Unsupervised Deformable image Registration with Absent Correspondences in Pre-operative and Post-recurrence Brain Tumor MRI Scans 25th

Unsupervised Deformable Image Registration with Absent Corre...

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

作者： Mok, Tony C. W. Chung, Albert C. S. Hong Kong Univ Sci & Technol Dept Comp Sci & Engn Kowloon Hong Kong Peoples R China

ISBN: (纸本)9783031164460;9783031164453

Registration of pre-operative and post-recurrence brain images is often needed to evaluate the effectiveness of brain gliomas treatment. While recent deep learning-based deformable registration methods have achieved remarkable success with healthy brain images, most of them would be unable to accurately align images with pathologies due to the absent correspondences in the reference image. In this paper, we propose a deep learning-based deformable registration method that jointly estimates regions with absent correspondence and bidirectional deformation fields. A forward-backward consistency constraint is used to aid in the localization of the resection and recurrence region from voxels with absence correspondences in the two images. Results on 3D clinical data from the BraTS-Reg challenge demonstrate our method can improve image alignment compared to traditional and deep learning-based registration approaches with or without cost function masking strategy. the source code is available at https://***/cwmok/DIRAC.

关键词： Absent correspondences Patient-specific registration Deformable registration

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A Novel Deep Learning System for Breast Lesion Risk Stratification in Ultrasound images 1

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

作者： Liu, Ting An, Xing Liu, Yanbo Liu, Yuxi Lin, Bin Jiang, Runzhou Xu, Wenlong Cong, Longfei Zhu, Lei Shenzhen Mindray BioMed Elect Co Ltd Shenzhen Peoples R China

ISBN: (数字)9783031164378

ISBN: (纸本)9783031164378;9783031164361

this paper presents a novel deep learning system to classify breast lesions in ultrasound images into benign and malignant and into Breast Imaging Reporting and Data System (BI-RADS) six categories simultaneously. A multitask soft label generating architecture is proposed to improve the classification performance, in which task-correlated labels are obtained from a dual-task teacher network and utilized to guide the training of a student model. In student model, a consistency supervision mechanism is embedded to constrain that a prediction of BI-RADS is consistent with the predicted pathology result. Moreover, a cross-class loss function that penalizes different degrees of misclassified items with different weights is introduced to make the prediction of BI-RADS closer to the annotation. Experiments on our private and two public datasets show that the proposed system outperforms current state-of-the-art methods, demonstrating the great potential of our method in clinical diagnosis.

关键词： Classification Breast ultrasound image Multitask soft label Consistency supervision Cross-class loss

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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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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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Video Foundation Model for medical 3D Segmentation 2nd

Video Foundation Model for Medical 3D Segmentation

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2nd international ToothFairy2: Multi-Structure Segmentation in CBCT Volumes, ToothFairy 2024, 3D Teeth Landmarks Detection Challenge, 3DTeethLand 2024, Semi-supervised Teeth Segmentation, STS 2024 held in conjunction with the 27th international conference on medical image computing and computer-assisted intervention, MICCAI 2024

作者： Ma, Qi Sun, Guolei Tombak, Güney Ik Jain, Shipra Huber, Niko Benjamin Gool, Luc Van Konukoglu, Ender Computer Vision Laboratory ETH Zurich Zurich Switzerland INSAIT Sofia University Sofia Bulgaria Align Technology San Jose United States

ISBN: (纸本)9783031889769

Video foundation models are gaining significant attention due to their ability to deliver highly accurate and efficient performance when pretrained on large-scale natural video datasets, especially compared to models trained from scratch. these pretrained models offer a substantial advantage in extracting rich features, enabling faster fine-tuning and more accurate downstream tasks. In the medical field, growing research has shown that treating 3D volumes as video sequences is effective. However, the models trained on limited 3D medical data extract unsatisfactory features due to insufficient training, leading to performance loss. therefore, we adapt video foundation models pretrained on large-scale RGB videos for segmenting 3D CT volumes, to make use of their ability of extracting high-quality features. Our method is evaluated on the ToothFairy2 and AMOS datasets, where it outperforms other transformer-based methods, bridging the gap between video foundation models and 3D medical segmentation. Our results demonstrate that knowledge from video foundation models trained on large-scale RGB datasets can be effectively transferred to medical segmentation tasks, achieving impressive performance even without training on any medical dataset. Additionally, we conduct extensive experiments exploring various design choices for the encoder, decoder, and domain adaptation mechanisms, offering comprehensive insights into adapting video foundation models for 3D medical segmentation. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： 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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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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