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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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NETrack: A Lightweight Attention-Based Network for Real-Time Pose Tracking of Nasal Endoscope Based on Endoscopic image 21st

NETrack: A Lightweight Attention-Based Network for Real-Tim...

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21st Pacific Rim international conference on Artificial Intelligence, PRICAI 2024

作者： Shao, Liangjing Chen, Benshuang Chen, Xinrong Academy for Engineering and Technology Fudan University Shanghai200438 China Shanghai Key Laboratory for Medical Imaging Computing and Computer Assisted Intervention Fudan University Shanghai200032 China

ISBN: (纸本)9789819601271

Vision-based real-time pose tracking of endoscope is significant for navigation and automation of endoscopy. In this work, a deep learning-based framework is proposed for vision-based real-time pose tracking of nasal endoscope based on endoscopic images. Specifically, an attention-based module is introduced into the feature encoder to extract and integrate multi-dimensional and multi-scale information. Furthermore, a novel pose encoder is proposed to extract more complicated representation from the feature map for pose vector prediction. the proposed method is compared with state-of-the-art methods on the dataset, which consists of endoscopic videos and endoscope poses obtaining by an optical tracking system. the experimental results demonstrate that the proposed network achieves better performance than state-of-the-art works for real-time nasal endoscope pose estimation. the average tracking error of our method is 5.78 mm, while the relative translation error and rotation error are respectively 0.48 mm and 0.22 degree on average. In addition, the average inference speed of our network is 46 fps, which achieves the real-time requirement. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Endoscopy

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A Novel Approach for Repairing Unsegmented Liver Vascular images based on Centerline 8

A Novel Approach for Repairing Unsegmented Liver Vascular Im...

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8th international conference on Biomedical Imaging, Signal Processing, ICBSP 2023

作者： Zhao, Lisha Wang, Zixuan Dai, Kun Qi, Quan College of Information Science and Technology Shihezi University Shihezi China Qingdao Hospital University of Health and Rehabilitation Sciences Qingdao Municipal Hospital Qingdao China Aimi Medical Technology Co. Ltd Qingdao China

ISBN: (纸本)9798400716584

In liver vascular intervention surgery, the absence or narrowing vascular images may impact the understanding of patients' vascular structure and morphology. Repairing and analyzing vascular images is a crucial task in computer-aided diagnosis and surgery for minimally invasive vascular diseases. Previous research has addressed the problem of repairing small areas with regular shapes or minor curvature changes in the holes, often leading to modifications to the existing points and encounter noise and geometric loss. Unlike existing point cloud completion algorithms, this paper aims to address the issue of missing vessels in liver vascular images that have not been segmented. It employs axis-aligned bounding boxes to extract missing vascular images. Based on the geometric characteristics of the liver vascular centerline, two methods of cross-section and spherical wave propagation are proposed for repair process. these methods take localized missing vascular images as input and preserve the complete topological structure of the vessels. the repair results are evaluated using the Chamfer distance and Dice similarity coefficient. Experimental results demonstrate that the proposed approach effectively repairs large-scale, non-closed missing vascular images in liver vascular images and generates complete vessel geometric models with accurate liver vascular structure and morphology. the validity of the vessel models is confirmed through hemodynamic analysis, providing robust support for medical image analysis and diagnosis. © 2023 Owner/Author.

关键词： image reconstruction

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Model Ensemble for Brain Tumor Segmentation in Magnetic Resonance Imaging 1

引用

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： Capellán-Martín, Daniel Jiang, Zhifan Parida, Abhijeet Liu, Xinyang Lam, Van Nisar, Hareem Tapp, Austin Elsharkawi, Sarah Ledesma-Carbayo, María J. Anwar, Syed Muhammad Linguraru, Marius George Sheikh Zayed Institute for Pediatric Surgical Innovation Children’s National Hospital WashingtonDC20010 United States Biomedical Image Technologies ETSI Telecomunicación Universidad Politécnica de Madrid Madrid 28040 Spain and CIBER-BBN ISCIII Madrid Spain Princeton University PrincetonNJ08544 United States School of Medicine and Health Sciences George Washington University WashingtonDC20052 United States

ISBN: (数字)9783031761638

ISBN: (纸本)9783031761621

Segmenting brain tumors in multi-parametric magnetic resonance imaging enables performing quantitative analysis in support of clinical trials and personalized patient care. this analysis provides the potential to impact clinical decision-making processes, including diagnosis and prognosis. In 2023, the well-established Brain Tumor Segmentation (BraTS) challenge presented a substantial expansion with eight tasks and 4,500 brain tumor cases. In this paper, we present a deep learning-based ensemble strategy that is evaluated for newly included tumor cases in three tasks: pediatric brain tumors (PED), intracranial meningioma (MEN), and brain metastases (MET). In particular, we ensemble outputs from state-of-the-art nnU-Net and Swin UNETR models on a region-wise basis. Furthermore, we implemented a targeted post-processing strategy based on a cross-validated threshold search to improve the segmentation results for tumor sub-regions. the evaluation of our proposed method on unseen test cases for the three tasks resulted in lesion-wise Dice scores for PED: 0.653, 0.809, 0.826;MEN: 0.876, 0.867, 0.849;and MET: 0.555, 0.6, 0.58;for the enhancing tumor, tumor core, and whole tumor, respectively. Our method was ranked first for PED, third for MEN, and fourth for MET, respectively. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Magnetic resonance imaging

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Deep Self-supervised Learning for Ultra-widefield Fundus image Quality Assessment 1st

Deep Self-supervised Learning for Ultra-widefield Fundus Im...

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1st MICCAI Challenge on Ultra-Widefield Fundus Imaging for Diabetic Retinopathy, UWF4DR 2024, Held in Conjunction with 27th international conference on medical image computing and computer assisted intervention, MICCAI 2024

作者： Okran, Ammar M. Musluh, Saif Khalid Abdulwahab, Saddam Puig, Domenec Rashwan, Hatem A. Universitat Rovira i Virgili Tarragona43007 Spain University of Al-Qadisiyah Al Diwaniyah Iraq

ISBN: (纸本)9783031893872

Ultra-widefield fundus imaging (UWF) enhances retinal diagnostics by offering comprehensive retinal views, crucial for detecting and managing diabetic retinopathy (DR). this study introduces a deep learning method utilizing self-supervised learning through an Autoencoder network to assess the quality of UWF images. the classification task distinguishes between gradable and ungradable images, a critical component for effective DR screening. the model was evaluated using the MICCAI UWF4DR 2024 dataset, which included 434 training images (with 201 images shared with Task 2) and 61 validation images. the system demonstrated excellent performance during the validation phase, achieving an AUROC of 0.8863, sensitivity of 0.9730, and specificity of 0.7500. In the test phase, the model achieved an AUROC of 0.8979, AUPRC of 0.9371, sensitivity of 0.7627, and specificity of 0.9000. these results highlight the model’s effectiveness in accurately classifying images as gradable or ungradable, demonstrating robust performance in UWF image quality assessment. this approach shows great potential for integration into large-scale DR screening programs, improving diagnostic efficiency and consistency in teleophthalmology. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Supervised learning

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A Comprehensive Approach to Graph Analysis and Equation Decoding

A Comprehensive Approach to Graph Analysis and Equation Deco...

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international conference on computing Communication Control and Automation (ICCUBEA)

作者： Aniket Patil Shreyas Patil Yuvraj Patil Tushar Jadhav Milind Patil Department of E&TC Vishwakarma Institute of Information Technology Pune India

ISBN: (数字)9798350391770

ISBN: (纸本)9798350391787

In the realm of graph analysis, different methods are employed to derive curve equations, each showcasing distinctive traits. Manual graph plotting involves human intervention to meticulously plot data points and derive the curve equation, relying on mathematical techniques. In contrast, employing YOLO (You Only Look Once) algorithm for curve detection offers an automated, machine learning-based approach. YOLO, a computer vision model, autonomously identifies curves within graph images, bypassing human intervention. the manual method relies on subjective interpretation and mathematical rigor, whereas YOLO presents an automated, potentially more objective, and efficient means for curve detection through image analysis and machine learning algorithms, revolutionizing graph analysis paradigms.

关键词： YOLO computer vision Machine learning algorithms image analysis Automation Computational modeling Manuals Mathematical models Decoding

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A comprehensive study of applying object detection methods for medical image analysis 8

A comprehensive study of applying object detection methods f...

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8th international conference on computing for Sustainable Global Development, INDIACom 2021

作者： Ganatra, Nilay Faculty of Computer Sceicne and Applications Changa India

ISBN: (纸本)9789380544434

medical imaging is a widely accepted technique for the early detection and diagnosis of disease within digital health. It includes different techniques such as Magnetic resonance imaging (MRI), X-ray, positron emission tomography (PET) scan. Human experts mostly perform the analysis of these images. However, recent advancement in the field of computer-assisted interventions shows the promising results for medical image analysis. With the availability of enormous data, sophisticated algorithms, and high computation power, deep neural networks are highly effective for image analysis and interpretation tasks. medical image analysis can be performed using the object detection method, where a convolutional neural network (CNN) eliminates the need for manual feature extraction. Object detection using CNN able to extract features directly from images and provides good accuracy. this paper exhibits a detailed survey on applications of different object detection methods available for medical image analysis. this paper discusses the different techniques, state-of-the-art datasets, tools, techniques available, and performance metrics. It also presents the work carried out by various researchers for applying object detection methods for medical image analysis. © 2021 Bharati Vidyapeeth, New Delhi. Copy Right in Bulk will be transferred to IEEE by Bharati Vidyapeeth.

关键词： Object detection

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SVoRT: Iterative Transformer for Slice-to-Volume Registration in Fetal Brain MRI 25th

SVoRT: Iterative Transformer for Slice-to-Volume Registratio...

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

作者： Xu, Junshen Moyer, Daniel Grant, P. Ellen Golland, Polina Iglesias, Juan Eugenio Adalsteinsson, Elfar MIT Dept Elect Engn & Comp Sci Cambridge MA 02139 USA MIT Comp Sci & Artificial Intelligence Lab Cambridge MA USA Boston Childrens Hosp Fetal Neonatal Neuroimaging & Dev Sci Ctr Boston MA USA Harvard Med Sch Boston MA USA UCL Dept Med Phys & Biomed Engn Ctr Med Image Comp London England Massachusetts Gen Hosp Athinoula A Martinos Ctr Biomed Imaging Boston MA USA MIT Inst Med Engn & Sci Cambridge MA USA

ISBN: (纸本)9783031164460;9783031164453

Volumetric reconstruction of fetal brains from multiple stacks of MR slices, acquired in the presence of almost unpredictable and often severe subject motion, is a challenging task that is highly sensitive to the initialization of slice-to-volume transformations. We propose a novel slice-to-volume registration method using Transformers trained on synthetically transformed data, which model multiple stacks of MR slices as a sequence. With the attention mechanism, our model automatically detects the relevance between slices and predicts the transformation of one slice using information from other slices. We also estimate the underlying 3D volume to assist slice-to-volume registration and update the volume and transformations alternately to improve accuracy. Results on synthetic data show that our method achieves lower registration error and better reconstruction quality compared with existing state-of-the-art methods. Experiments with real-world MRI data are also performed to demonstrate the ability of the proposed model to improve the quality of 3D reconstruction under severe fetal motion.

关键词： Slice-to-volume registration 3D reconstruction Fetal MRI Transformer

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Research on Cloud computing Security & Privacy Protection of Massive Unstructured Multi-Modal Data 8

Research on Cloud Computing Security & Privacy Protection of...

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8th IEEE international conference on computer and Communications, ICCC 2022

作者： Yang, Hui Cao, Yang Cetc Big Data Research Institute Co. Ltd Chengdu China

ISBN: (纸本)9781665450515

With the rapid development of big data and AI technologies, the sharing and opening-up, development and utilization of big data from government, medical becomes into one of our priorities. However, a problem that has puzzled us for a long time is that, the usage rate of massive unstructured multi-modal data as an absolute main body is still very low, which seriously restricts the circulation of data factors and faces the increasingly serious challenge of security-compliance. To speed up the circulation process and value release of data factors, this paper proposed a security-compliant and lightweight cloud computing model for massive unstructured multi-modal data. the implementation method includes multi-modal feature recognition and extraction from unstructured big data with NLP, CNN, image processing, audio processing technologies, fast and safe cipher-text calculation, and cipher-text retrieval with HE (homomorphic encryption) against the feature index database, and multi-modal data fusion, etc. With the above model & algorithms including the optimization solution of the HE algorithm, we verified the 'compute-able but invisible' of massive cross-media unstructured data, and proved the security, validity, and time efficiency. © 2022 IEEE.

关键词： Data privacy

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Dental Detection and Classification of YOLOv3-SPP based on Convolutional Block Attention Module 8

Dental Detection and Classification of YOLOv3-SPP based on C...

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8th IEEE international conference on computer and Communications, ICCC 2022

作者： Li, Ning Guo, Ruifeng Liu, Xiaozhou Wu, Lin Wang, Hongliang Shenyang Institute of Computing Technology Chinese Academy of Sciences Shenyang China University of Chinese Academy of Sciences Shenyang China School and Hospital of Stomatology China Medical University Liaoning Provincial Key Laboratory of Oral Diseases Shenyang China Liaoning Provience Hum.-Comp. Interaction System Engineering Research Center Based on Digital Twin Shenyang China

ISBN: (纸本)9781665450515

the purpose of this research paper is to implement the tooth target detection task by deep convolutional neural networks. In order to solve the problems of low accuracy of target detection due to the high similarity between teeth and complex tooth textures, an improved tooth detection method with YOLOv3-SPP model is proposed in this paper. this method incorporates the convolutional block attention module (CBAM) in the YOLOv3-SPP algorithm framework, and improves the performance of the network features by adding the channel attention mechanism and spatial attention mechanism to the feature extraction network to enhance the saliency of the tooth target region in the image. Secondly, CIoU border regression loss is introduced to improve the localization accuracy. In addition, a Non-maximum suppression (NMS) method is used to solve the candidate frame overlap problem. through experiments, it is shown that the mAP of the modified YOLOv3-SPP target detection model is improved to 86.8 percent, which indicates that the improved model can be applied to the detection of teeth. © 2022 IEEE.

关键词： image enhancement

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An Ultra-efficient Method for Real-Time Ultra-widefield Fundus image Quality Assessment 1st

An Ultra-efficient Method for Real-Time Ultra-widefield Fun...

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作者： Engelmann, Justin Gago, Lucas Institute of Ophthalmology University College London London United Kingdom Departament de Matemátiques i Informática Universitat de Barcelona Barcelona Spain

ISBN: (纸本)9783031893872

this manuscript presents a highly efficient method for assessing the quality of ultra-widefield (UWF) fundus images, which we developed for the MICCAI Ultra-Widefield Fundus Imaging for Diabetic Retinopathy (UWF4DR) Challenge 2024 task 1. We used a lightweight MobileNetV3 model at a relatively low resolution of 254×200 pixels. We augment the training data with synthetic quality degradation to simulate additional poor quality cases and use highly efficient parallelized test time augmentations. Our method achieved the highest throughput of all submitted solutions with 24.7 images per second, while ranking third in terms of area under the ROC curve (AUROC) with an AUROC of 0.9623. Our final overall ranking for task 1 was second place. Notably, we achieve the highest specificity with 97.5%. While there are many potential avenues for improvements, e.g. better test time augmentations, our ultra-efficient UWF image quality assessment solution can process an image in 40.5 ms on a CPU, potentially enabling real-time assessment at the point of capture without specialised hardware. Our code is available on Github. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Deep learning

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