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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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Enhancing Domain Generalization for Cardiac image Segmentation with Probabilistic Perturbation 1st

Enhancing Domain Generalization for Cardiac Image Segmentat...

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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： Lin, Bogen Dong, Jinwei Zheng, Yaqiong Xiang, Yihan Yang, Mingjing College of Physics and Information Engineering Fuzhou University Fuzhou China School of Medical Imaging Fujian Medical University Fuzhou China

ISBN: (纸本)9783031870088

Cardiovascular diseases are a leading cause of mortality worldwide, necessitating accurate segmentation of cardiac structures from 3D CT and MR images for effective diagnosis and treatment planning. Manual segmentation is time-consuming, labor-intensive, and challenging due to the complexity of cardiac anatomy structures. General CNNs, while effective, often struggle to generalize across different imaging domains. To address these issues, we developed a PPM-UNet, which integrates a probabilistic perturbation module (PPM) into the traditional U-Net architecture. Here, the PPM is introduced to control randomness during training, enhancing the model’s robustness and improving its performance across different imaging domains. Furthermore, the PPM is lightweight and easy to implement, making it a practical addition to existing network architectures for domain generalization tasks in medical imaging. Evaluated on the WHS++ dataset, our method demonstrates superior performance in segmenting cardiac structures, particularly in unseen domains, as evidenced by improved Dice Similarity Coefficient, Precision, Sensitivity, and Hausdorff Distance metrics. In addition to its performance benefits, the proposed module requires minimal computational overhead, making it practical for real-world clinical applications. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： image segmentation

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Development of Advanced Noise Filtering Techniques for medical image Enhancement 5

Development of Advanced Noise Filtering Techniques for Medic...

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5th international conference on Intelligent Communication Technologies and Virtual Mobile Networks, ICICV 2024

作者： Kushwaha, Sumit Amuthachenthiru, K. Geetha, K. Narasimharao, Jonnadula Dileep Kumar, M. Gadde, Sai Sudha University Institute of Computing Chandigarh University Department of Computer Applications Punjab 140413 India Ramco Institute of Technology Department of Artificial Intelligence and Data Science Tamil Nadu Rajapalayam626117 India Srm Institute of Science and Technology Department of Computing Technologies Tamil Nadu Kattankulathur603 203 India Cmr Technical Campus Department of Computer Science and Engineering Telangana Hyderabad501401 India Hensard University Faculty of Management Sciences Bayelsa 561101 Nigeria Koneru Lakshmaiah Education Foundation Department of Computer Science and Engineering Telangana Vaddeswaram500075 India

ISBN: (纸本)9798350385649

medical imaging modalities, including computed tomography (CT), magnetic resonance imaging (MRI), X-rays, and ultrasound, are extensively employed in the healthcare industry for diagnostic purposes. Noise, on the other hand, can disturb these approaches and lead to inaccurate diagnosis. Due to this problem, the significance of noise removal technologies has grown in the field of medical imaging, specifically for the examination and understanding of medical images and anatomical structures. To tackle these challenges, a variety of denoising techniques have been developed, such as the Weiner filter (WF), Gaussian filter, and median filter. this work employs a Deep Learning (DL) method known as Convolutional Autoencoder (CAE) to remove noise from medical images. We gather chest X-ray images for analysis and focus on common noise types that affect medical images, such as Poisson and Gaussian noise. We evaluate the effectiveness of the suggested denoising method CAE by comparing it to the commonly employed WF methodology. the Structural Similarity Index (SSIM) and Peak Signal-to-Noise Ratio (PSNR) are employed to assess and compare the performance of the WF and CAE in reducing Poisson and Gaussian noise. the study's findings indicate that the suggested denoising method based on CAE shows promising results in terms of improving quality. © 2024 IEEE.

关键词： medical imaging

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How Does Pruning Impact Long-Tailed Multi-label medical image Classifiers? 26th

How Does Pruning Impact Long-Tailed Multi-label Medical Imag...

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

作者： Holste, Gregory Jiang, Ziyu Jaiswal, Ajay Hanna, Maria Minkowitz, Shlomo Legasto, Alan C. Escalon, Joanna G. Steinberger, Sharon Bittman, Mark Shen, thomas C. Ding, Ying Summers, Ronald M. Shih, George Peng, Yifan Wang, Zhangyang Univ Texas Austin Austin TX 78712 USA Texas A&M Univ College Stn TX 77834 USA Weill Cornell Med New York NY USA NIH Ctr Clin Bldg 10 Bethesda MD 20892 USA

ISBN: (纸本)9783031439032;9783031439049

Pruning has emerged as a powerful technique for compressing deep neural networks, reducing memory usage and inference time without significantly affecting overall performance. However, the nuanced ways in which pruning impacts model behavior are not well understood, particularly for long-tailed, multi-label datasets commonly found in clinical settings. this knowledge gap could have dangerous implications when deploying a pruned model for diagnosis, where unexpected model behavior could impact patient well-being. To fill this gap, we perform the first analysis of pruning's effect on neural networks trained to diagnose thorax diseases from chest X-rays (CXRs). On two large CXR datasets, we examine which diseases are most affected by pruning and characterize class "forgettability" based on disease frequency and co-occurrence behavior. Further, we identify individual CXRs where uncompressed and heavily pruned models disagree, known as pruning-identified exemplars (PIEs), and conduct a human reader study to evaluate their unifying qualities. We find that radiologists perceive PIEs as having more label noise, lower image quality, and higher diagnosis difficulty. this work represents a first step toward understanding the impact of pruning on model behavior in deep long-tailed, multi-label medical image classification. All code, model weights, and data access instructions can be found at https://***/VITA-Group/PruneCXR.

关键词： Pruning Chest X-Ray Imbalance Long-Tailed Learning

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Label-Free Nuclei Segmentation Using Intra-image Self Similarity 26th

Label-Free Nuclei Segmentation Using Intra-Image Self Simila...

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

作者： Chen, Long Li, Han Zhou, S. Kevin Chinese Acad Sci Inst Comp Technol Key Lab Intelligent Informat Proc Beijing 100190 Peoples R China Univ Sci & Technol China Sch Biomed Engn Div Life Sci & Med Hefei 230026 Anhui Peoples R China Univ Sci & Technol China Suzhou Inst Adv Res Ctr Med Imaging Robot Analyt Comp & Learning MIRACLE Suzhou 215123 Jiangsu Peoples R China

ISBN: (纸本)9783031439865;9783031439872

In computational pathology, nuclei segmentation from histology images is a fundamental task. While deep learning based nuclei segmentation methods yield excellent results, they rely on a large amount of annotated images;however, annotating nuclei from histology images is tedious and time-consuming. To get rid of labeling burden completely, we propose a label-free approach for nuclei segmentation, motivated from one pronounced yet omitted property that characterizes histology images and nuclei: intra-image self similarity (IISS), that is, within an image, nuclei are similar in their shapes and appearances. First, we leverage traditional machine learning and image processing techniques to generate a pseudo segmentation map, whose connected components form candidate nuclei, both positive or negative. In particular, it is common that adjacent nuclei are merged into one candidate due to imperfect staining and imaging conditions, which violate the IISS property. then, we filter the candidates based on a custom-designed index that roughly measures if a candidate contains multiple nuclei. the remaining candidates are used as pseudo labels, which we use to train a U-Net to discover the hierarchical features distinguish nuclei pixels from background. Finally, we apply the learned U-Net to produce final nuclei segmentation. We validate the proposed method on the public dataset MoNuSeg. Experimental results demonstrate the effectiveness of our design and, to the best of our knowledge, it achieves the state-of-the-art performances of label-free segmentation on the benchmark MoNuSeg dataset with a mean Dice score of 79.2%.

关键词： Label-free Nuclei segmentation Pseudo Label

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Electron Microscope image Registration Using Laplacian Sharpening Transformer U-Net 25th

Electron Microscope Image Registration Using Laplacian Sharp...

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

作者： Xie, Kunzi Yang, Yixing Pagnucco, Maurice Song, Yang Univ New South Wales Sch Comp Sci & Engn Sydney Australia

ISBN: (纸本)9783031164460;9783031164453

image registration is an essential task in electron microscope (EM) image analysis, which aims to accurately warp the moving image to align with the fixed image, to reduce the spatial deformations across serial slices resulted during image acquisition. Existing learning-based registration approaches are primarily based on Convolution Neural Networks (CNNs). However, for the requirements of EM image registration, CNN-based methods lack the capability of learning global and long-term semantic information. In this work, we propose a new framework, Cascaded LST-UNet, which integrates a sharpening skip-connection layer with the Swin Transformer based U-Net structure in a cascaded manner for unsupervised EM image registration. Our experimental results on a public dataset show that our method consistently outperforms the baseline approaches.

关键词： image registration Swin transformer Deep learning Electron microscopy

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A Left Atrial Automatic Segmentation Based on ResCAUNet 1st

A Left Atrial Automatic Segmentation Based on ResCAUNet

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作者： Li, Xinru Gao, Ruikun Zheng, Yuxin Zheng, Shaohua Chen, Weisheng College of Physics and Information Engineering Fuzhou University Fuzhou China Department of Thoracic Surgery Clinical Oncology School of Fujian Medical University Fujian Cancer Hospital FJ Fuzhou350108 China

ISBN: (纸本)9783031870088

Atrial fibrillation (AF) is a prevalent heart rate arrhythmia and its incidence is increasing with the aging population. the late gadoliniumenhanced magnetic resonance imaging (LGE-MRI) provides pathologic changes in the left atrium, allowing for a detailed assessment of the left atrial anatomy, which is critical for diagnosis and treatment decisions in AF. the segmentation performance of current left atrial segmentation methods is significantly degraded when applied to multicenter data. In this work, we propose ResCAUNet, a deep learning method based on residual neural networks. Specifically, a pre-trained model driven residual segmentation network is first designed to alleviate the problem of gradient disappearance and help to extract more complex image features. Secondly, an adaptive scale weight loss function was introduced to solve the problem of discontinuous segmentation boundary, so as to ensure higher accuracy of object segmentation. Furthermore, the coordinate attention(CA) mechanism is introduced for adaptive weight allocation, focusing on the key parts of the image to improve the accuracy of left atrial reconstruction. Our method is evaluated on the LAScarQS2024 validation set and achieves an average Dice of 0.853. the evaluation results show that the proposed method is effective in left atrium segmentation of LGE-MRI. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： image enhancement

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Global k-Space Interpolation for Dynamic MRI Reconstruction Using Masked image Modeling 26th

Global k-Space Interpolation for Dynamic MRI Reconstruction ...

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

作者： Pan, Jiazhen Shit, Suprosanna Turgut, Oezguen Huang, Wenqi Li, Hongwei Bran Stolt-Anso, Nil Kuestner, thomas Hammernik, Kerstin Rueckert, Daniel Tech Univ Munich Klinikum Rechts Isar Sch Med Munich Germany Univ Zurich Dept Quantitat Biomed Zurich Switzerland Tech Univ Munich Sch Comp Informat & Technol Munich Germany Univ Tubingen Hosp Med Image & Data Anal Tubingen Germany Imperial Coll London Dept Comp London England

ISBN: (纸本)9783031439988;9783031439995

In dynamic Magnetic Resonance Imaging (MRI), k-space is typically undersampled due to limited scan time, resulting in aliasing artifacts in the image domain. Hence, dynamic MR reconstruction requires not only modeling spatial frequency components in the x and y directions of k-space but also considering temporal redundancy. Most previous works rely on image-domain regularizers (priors) to conduct MR reconstruction. In contrast, we focus on interpolating the undersampled k-space before obtaining images with Fourier transform. In this work, we connect masked image modeling with k-space interpolation and propose a novel Transformer-based k-space Global Interpolation Network, termed k-GIN. Our k-GIN learns global dependencies among low- and high-frequency components of 2D+t k-space and uses it to interpolate unsampled data. Further, we propose a novel k-space Iterative Refinement Module (k-IRM) to enhance the high-frequency components learning. We evaluate our approach on 92 in-house 2D+t cardiac MR subjects and compare it to MR reconstruction methods with image-domain regularizers. Experiments show that our proposed k-space interpolation method quantitatively and qualitatively outperforms baseline methods. Importantly, the proposed approach achieves substantially higher robustness and generalizability in cases of highly-undersampled MR data.

关键词： Cardiac MR Imaging Reconstruction k-space Interpolation Masked image Modeling Masked Autoencoders Transformers

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Chat-Bot assisted Recommendation Platform for computers and Repair Services 5

Chat-Bot Assisted Recommendation Platform for Computers and ...

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5th international conference on Advancements in computing, ICAC 2023

作者： thirimanne, Shanila Rathnaweera, Wishwa Unais, Amanullah Senadeera, Minindu Abeywardhana, Lakmini Sri Lanka Institute of Information Technology Department of Data Science Malabe Sri Lanka

ISBN: (纸本)9798350358131

In the age of computers, individuals used to look for computers through every avenue or ask for such assistance to find the ideal equipment based on the necessity. It is therefore important to have all the information under one roof. Hence, this novel approach have proposed a chat-bot recommendation system for computers, accessories and repair centers which provides those information at ease. this particular method uses Large Language Models to understand the user's preferences and requirements through a user friendly Chat - Bot interface. Furthermore, this platform utilized a state of the art ASR model to gather audio inputs. this study tends to give the best solutions towards users by collecting reviews of devices and repair centers by web-scraping certain websites to gather relevant data. In addition, image processing has been introduced in order to identify computer accessories without any difficulties. All these features will be available through the proposed chat bot system such that any individual with varying knowledge of electronic devices can get a solution without any hesitation. © 2023 IEEE.

关键词： image processing

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Deep Reinforcement Learning Based System for Intraoperative Hyperspectral Video Autofocusing 26th

Deep Reinforcement Learning Based System for Intraoperative ...

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

作者： Budd, Charlie Qiu, Jianrong MacCormac, Oscar Huber, Martin Mower, Christopher Janatka, Mirek Trotouin, theo Shapey, Jonathan Bergholt, Mads S. Vercauteren, Tom Kings Coll London Biomed Engn & Imaging Sci London England Kings Coll London Sch Craniofacial & Regenerat Biol London England Kings Coll Hosp London Dept Neurosurg Denmark Hill London England Hypervis Surg Ltd 1st Floor 85 Great Portland St London England

ISBN: (纸本)9783031439957;9783031439964

Hyperspectral imaging (HSI) captures a greater level of spectral detail than traditional optical imaging, making it a potentially valuable intraoperative tool when precise tissue differentiation is essential. Hardware limitations of current optical systems used for handheld real-time video HSI result in a limited focal depth, thereby posing usability issues for integration of the technology into the operating room. this work integrates a focus-tunable liquid lens into a video HSI exoscope, and proposes novel video autofocusing methods based on deep reinforcement learning. A first-of-its-kind robotic focal-time scan was performed to create a realistic and reproducible testing dataset. We benchmarked our proposed autofocus algorithm against traditional policies, and found our novel approach to perform significantly (p < 0.05) better than traditional techniques (0.070 +/- .098 mean absolute focal error compared to 0.146 +/- .148). In addition, we performed a blinded usability trial by having two neurosurgeons compare the system with different autofocus policies, and found our novel approach to be the most favourable, making our system a desirable addition for intraoperative HSI.

关键词： Autofocus Deep Reinforcement Learning Hyperspectral Imaging computer assisted intervention

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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...

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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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