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检索条件"任意字段=3rd International Conference on Medical Image Computing and Computer-Assisted Intervention"

共 4110 条记录，以下是111-120 订阅

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SAM-U: Multi-box Prompts Triggered Uncertainty Estimation for Reliable SAM in medical image 26th

SAM-U: Multi-box Prompts Triggered Uncertainty Estimation fo...

引用

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

作者： Deng, Guoyao Zou, Ke Ren, Kai Wang, Meng Yuan, Xuedong Ying, Sancong Fu, Huazhu Sichuan Univ Natl Key Lab Fundamental Sci Synthet Vis Chengdu Sichuan Peoples R China Sichuan Univ Coll Comp Sci Chengdu Sichuan Peoples R China Agcy Sci Technol & Res Inst High Performance Comp Singapore Singapore

ISBN: (纸本)9783031474248;9783031474255

Recently, Segmenting Anything Model has taken a significant step towards general artificial intelligence. Simultaneously, its reliability and fairness have garnered significant attention, particularly in the field of healthcare. In this study, we propose a multi-box prompt-triggered uncertainty estimation for SAM cues to demonstrate the reliability of segmented lesions or tissues. We estimate the distribution of SAM predictions using Monte Carlo with prior distribution parameters, employing different prompts as a formulation of test-time augmentation. Our experimental results demonstrate that multi-box prompts augmentation enhances SAM performance and provides uncertainty for each pixel. This presents a groundbreaking paradigm for a reliable SAM.

关键词： Segmenting Anything reliability uncertainty estimation prompt learning

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Detection and Classification of Monkeypox Disease with Chatbot 3

Detection and Classification of Monkeypox Disease with Chatb...

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3rd international conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Chandu, P. Siddardha, P. Priyadharshini, K. Department of Computing Technologies SRM Institute of Science and Technology Kattankulathur India

ISBN: (纸本)9798331527549

Monkeypox detection is essential for effective public health management and controlling its spread. Timely detection enables early intervention in outbreaks, reducing transmission risk. This project presents an innovative framework that enhances monkeypox detection and classification using deep learning and an interactive chatbot. It employs the VGG16 convolutional neural network (CNN) for accurate classification of monkeypox from medical images, utilizing performance metrics like precision, recall, and F1 score for reliability. Additionally, the system includes a neural network-based chatbot that provides interactive assistance, answers user questions, offers symptom information, and guides users through the diagnostic process. This chatbot improves user engagement and accessibility, linking clinical diagnostics with patient interaction. Built on the Flask framework, the system ensures a smooth, user-friendly interface for both image classification and chatbot functionalities. © 2025 IEEE.

关键词： Convolutional neural networks

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Hybrid Watermarking Techniques in medical Imaging: A Comprehensive Analysis and Performance Evaluation 3

Hybrid Watermarking Techniques in Medical Imaging: A Compreh...

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3rd international conference on Communication, Control, and Intelligent Systems, CCIS 2024

作者： Rai, Ankur Singh, Siddharth Kushwaha, Sumit Gla University Computer Engineering and Applications U.P. Mathura India University Institute of Computing Chandigarh University Department of Computer Applications 140413 India

ISBN: (纸本)9798331528201

In tele-health services, the security of patient information becomes an essential consideration during the exchange of information. In this digital age, digital image watermarking becomes a crucial tool for digital authentication and security. Techniques based on digital image watermarking can enhance the security of information during copyright protection, open channel transmission, content management and identification, file archiving, and broadcast monitoring. This paper discusses the benefits and drawbacks of spatial and frequency domain-based watermarking techniques. The goal of this proposed work is to present a review of various available embedding techniques in the transform domain. The hybrid watermarking model carried out in this paper with MRI, CT scan, and ultrasound images for SSIM values of sizes (16×16, 32×32, 64×64) of the watermark image and the average imperceptibility 0.958, 0.922, 0.903 of the model has been calculated. The results indicate that the higher the dimensions of the watermark image, the lower the imperceptibility of the model. © 2024 IEEE.

关键词： Frequency domain analysis

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Asymmetric Contour Uncertainty Estimation for medical image Segmentation 1

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

作者： Judge, Thierry Bernard, Olivier Kim, Woo-Jin Cho Gomez, Alberto Chartsias, Agisilaos Jodoin, Pierre-Marc Univ Sherbrooke Dept Comp Sci Sherbrooke PQ Canada Ultr Ltd Oxford OX4 2SU England Univ Lyon 1 Univ Lyon CNRS CREATISInserm U1294UMR5220 Villeurbanne France

ISBN: (数字)9783031438981

ISBN: (纸本)9783031438974;9783031438981

Aleatoric uncertainty estimation is a critical step in medical image segmentation. Most techniques for estimating aleatoric uncertainty for segmentation purposes assume a Gaussian distribution over the neural network's logit value modeling the uncertainty in the predicted class. However, in many cases, such as image segmentation, there is no uncertainty about the presence of a specific structure, but rather about the precise outline of that structure. For this reason, we explicitly model the location uncertainty by redefining the conventional per-pixel segmentation task as a contour regression problem. This allows for modeling the uncertainty of contour points using a more appropriate multivariate distribution. Additionally, as contour uncertainty may be asymmetric, we use a multivariate skewed Gaussian distribution. In addition to being directly interpretable, our uncertainty estimation method outperforms previous methods on three datasets using two different image modalities. Code is available at: https://***/ThierryJudge/contouring-uncertainty.

关键词： Uncertainty estimation image segmentation

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Explainable image Classification with Improved Trustworthiness for Tissue Characterisation 26th

Explainable Image Classification with Improved Trustworthine...

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

作者： Roddan, Alfie Xu, Chi Ajlouni, Serine Kakaletri, Irini Charalampaki, Patra Giannarou, Stamatia Imperial Coll London Dept Surg & Canc Hamlyn Ctr Robot Surg London England Univ Witten Herdecke Fac Med Witten Germany Rhein Friedrich Wilhelms Univ Bonn Fac Med Bonn Germany Cologne Med Ctr Dept Neurosurg Cologne Germany

ISBN: (纸本)9783031438943;9783031438950

The deployment of Machine Learning models intraoperatively for tissue characterisation can assist decision making and guide safe tumour resections. For the surgeon to trust the model, explainability of the generated predictions needs to be provided. For image classification models, pixel attribution (PA) and risk estimation are popular methods to infer explainability. However, the former method lacks trustworthiness while the latter can not provide visual explanation of the model's attention. In this paper, we propose the first approach which incorporates risk estimation into a PA method for improved and more trustworthy image classification explainability. The proposed method iteratively applies a classification model with a PA method to create a volume of PA maps. We introduce a method to generate an enhanced PA map by estimating the expectation values of the pixel-wise distributions. In addition, the coefficient of variation (CV) is used to estimate pixel-wise risk of this enhanced PA map. Hence, the proposed method not only provides an improved PA map but also produces an estimation of risk on the output PA values. Performance evaluation on probe-based Confocal Laser Endomicroscopy (pCLE) data verifies that our improved explainability method outperforms the state-of-the-art.

关键词： Explainability Uncertainty MC Dropout

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1st Challenge on Segmentation of the Aorta, SEG.A. 2023 which was held in conjunction with 26th international conference on medical image computing and computer-assisted intervention, MICCAI 2023

1st Challenge on Segmentation of the Aorta, SEG.A. 2023 whic...

引用

1st Challenge on Segmentation of the Aorta, SEG.A. 2023 which was held in conjunction with 26th international conference on medical image computing and computer-assisted intervention, MICCAI 2023

ISBN: (纸本)9783031532405

The proceedings contain 11 papers. The special focus in this conference is on Challenge on Segmentation of the Aorta. The topics include: M3F: Multi-Field-of-View Feature Fusion Network for Aortic Vessel Tree Segmentation in CT Angiography;Aorta Segmentation from 3D CT in MICCAI SEG.A. 2023 Challenge;A Data-Centric Approach for Segmenting the Aortic Vessel Tree: A Solution to SEG.A. Challenge 2023 Segmentation Task;Automatic Aorta Segmentation with Heavily Augmented, High-Resolution 3-D ResUNet: Contribution to the SEG.A Challenge;position-Encoded Pixel-to-Prototype Contrastive Learning for Aortic Vessel Tree Segmentation;misclassification Loss for Segmentation of the Aortic Vessel Tree;Deep Learning-Based Segmentation and Mesh Reconstruction of the Aortic Vessel Tree from CTA images;RASNet: U-Net-Based Robust Aortic Segmentation Network for Multicenter Datasets;optimizing Aortic Segmentation with an Innovative Quality Assessment: The Role of Global Sensitivity Analysis.

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Exploring CNN and Transformer-Based Architectures to Improve image Segmentation for Chronic Wound Measurement 3rd

Exploring CNN and Transformer-Based Architectures to Impro...

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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： Carvalho, Rafaela Morgado, Ana C. Sampaio, Ana Filipa Vasconcelos, Maria J. M. Fraunhofer Portugal AICOS Porto Portugal

ISBN: (纸本)9783031820069

The high prevalence of chronic wounds and their consequences for people’s quality of life makes wound treatment a highly relevant topic in the context of healthcare. One vital aspect of monitoring concerns tracking wound size evolution, which guides healthcare professionals during diagnosis and serves as a key predictor of treatment efficacy. This work proposes an automatic image segmentation and measurement framework for chronic wounds using deep learning and computer vision techniques. The wound segmentation task involved exploring three prominent segmentation models: a popular convolutional neural network (DeepLabV3+), a cutting-edge transformer approach (SegFormer) and a visual foundation model (MedSAM). Traditional computer vision techniques were further applied to infer the open wound’s width, length and area in real-world units during the wound measurement task. Separate studies were performed to assess each task’s performance and a final assessment of the complete framework that couples a wound and reference marker detection model with the developed segmentation and measurement approach. For the automatic wound segmentation, MedSAM achieved the best performance with Dice scores of 88.14% and 92.25%, applied on public AZH FU and private datasets, respectively. In the wound measurement task, the area estimation achieved a mean relative error of 5.36% for the private dataset. Concerning the overall pipeline results, MedSAM experienced a decline in performance and SegFormer emerged as the best segmentation model, achieving a Dice score of 91.55% and a 16.7% mean relative error for the area estimation (which surpasses the literature results) in the private dataset, demonstrating its applicability in clinical practice. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Convolutional neural networks

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Intraoperative CT Augmentation for Needle-Based Liver interventions 26th

Intraoperative CT Augmentation for Needle-Based Liver Interv...

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

作者： El Hadramy, Sidaty Verde, Juan Padoy, Nicolas Cotin, Stephane INRIA Strasbourg France Univ Strasbourg ICube CNRS Strasbourg France IHU Strasbourg Strasbourg France

ISBN: (纸本)9783031439957;9783031439964

This paper addresses the need for improved CT-guidance during needle-based liver procedures (i.e., tumor ablation), while reduces the need for contrast agent injection during such interventions. To achieve this objective, we augment the intraoperative CT with the preoperative vascular network deformed to match the current acquisition. First, a neural network learns local image features in a non-contrasted CT image by leveraging the known preoperative vessel tree geometry and topology extracted from a matching contrasted CT image. Then, the augmented CT is generated by fusing the labeled vascular tree and the non-contrasted intraoperative CT. Our method is trained and validated on porcine data, achieving an average dice score of 0.81 on the predicted vessel tree instead of 0.51 when a medical expert segments the non-contrasted CT. In addition, vascular labels can also be transferred to provide additional information. Source code of this work is publicly available at https://***/Sidaty1/Intraoperative CT augmentation.

关键词： Liver tumor ablation Needle-based procedures Patient-specific interventions CT-guidance medical image augmentation

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X2Vision: 3D CT Reconstruction from Biplanar X-Rays with Deep Structure Prior 26th

X2Vision: 3D CT Reconstruction from Biplanar X-Rays with Dee...

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

作者： Cafaro, Alexandre Spinat, Quentin Leroy, Amaury Maury, Pauline Munoz, Alexandre Beldjoudi, Guillaume Robert, Charlotte Deutsch, Eric Gregoire, Vincent Lepetit, Vincent Paragios, Nikos TheraPanacea Paris France Paris Saclay Univ Gustave Roussy Inserm 1030 Villejuif France Ctr Leon Berard Dept Radiat Oncol Lyon France Univ Gustave Eiffel CNRS LIGM Ecole Ponts Paris France

ISBN: (纸本)9783031439988;9783031439995

We propose an unsupervised deep learning method to reconstruct a 3D tomographic image from biplanar X-rays, to reduce the number of required projections, the patient dose, and the acquisition time. To address this ill-posed problem, we introduce prior knowledge of anatomic structures by training a generative model on 3D CTs of head and neck. We optimize the latent vectors of the generative model to recover a volume that both integrates this prior knowledge and ensures consistency between the reconstructed image and input projections. Our method outperforms recent methods in terms of reconstruction error while being faster and less radiating than current clinical workflow. We evaluate our method in a clinical configuration for radiotherapy.

关键词： image reconstruction Inverse problem Sparse sampling Deep generative model CT

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Boundary Difference over Union Loss for medical image Segmentation 26th

Boundary Difference over Union Loss for Medical Image Segmen...

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

作者： Sun, Fan Luo, Zhiming Li, Shaozi Xiamen Univ Dept Artificial Intelligence Xiamen Fujian Peoples R China

ISBN: (纸本)9783031439001;9783031439018

medical image segmentation is crucial for clinical diagnosis. However, current losses for medical image segmentation mainly focus on overall segmentation results, with fewer losses proposed to guide boundary segmentation. Those that do exist often need to be used in combination with other losses and produce ineffective results. To address this issue, we have developed a simple and effective loss called the Boundary Difference over Union Loss (Boundary DoU Loss) to guide boundary region segmentation. It is obtained by calculating the ratio of the difference set of prediction and ground truth to the union of the difference set and the partial intersection set. Our loss only relies on region calculation, making it easy to implement and training stable without needing any additional losses. Additionally, we use the target size to adaptively adjust attention applied to the boundary regions. Experimental results using UNet, TransUNet, and Swin-UNet on two datasets (ACDC and Synapse) demonstrate the effectiveness of our proposed loss function. Code is available at https://***/sunfan-bvb/BoundaryDoULoss.

关键词： medical image segmentation Boundary loss

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