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检索条件"任意字段=5th International Conference on Medical Image Computing and Computer-Assisted Intervention"

共 2874 条记录，以下是421-430 订阅

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Democratizing Pathological image Segmentation with Lay Annotators via Molecular-Empowered Learning 26th

Democratizing Pathological Image Segmentation with Lay Annot...

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

作者： Deng, Ruining Li, Yanwei Li, Peize Wang, Jiacheng Remedios, Lucas W. Agzamkhodjaev, Saydolimkhon Asad, Zuhayr Liu, Quan Cui, Can Wang, Yaohong Wang, Yihan Tang, Yucheng Yang, Haichun Huo, Yuankai Vanderbilt Univ Nashville TN 37215 USA NVIDIA Corp Santa Clara CA USA NVIDIA Corp Bethesda MD USA Vanderbilt Univ Med Ctr Nashville TN 37232 USA

ISBN: (纸本)9783031439865;9783031439872

Multi-class cell segmentation in high-resolution Giga-pixel whole slide images (WSI) is critical for various clinical applications. Training such an AI model typically requires labor-intensive pixel-wise manual annotation from experienced domain experts (e.g., pathologists). Moreover, such annotation is error-prone when differentiating fine-grained cell types (e.g., podocyte and mesangial cells) via the naked human eye. In this study, we assess the feasibility of democratizing pathological AI deployment by only using lay annotators (annotators without medical domain knowledge). the contribution of this paper is threefold: (1) We proposed a molecular-empowered learning scheme for multi-class cell segmentation using partial labels from lay annotators;(2) the proposed method integrated Giga-pixel level molecular-morphology cross-modality registration, molecular-informed annotation, and molecular-oriented segmentation model, so as to achieve significantly superior performance via 3 lay annotators as compared with 2 experienced pathologists;(3) A deep corrective learning (learning with imperfect labels) method is proposed to further improve the segmentation performance using partially annotated noisy data. From the experimental results, our learning method achieved F1 = 0.8496 using molecular-informed annotations from lay annotators, which is better than conventional morphology-based annotations (F1 = 0.7015) from experienced pathologists. Our method democratizes the development of a pathological segmentation deep model to the lay annotator level, which consequently scales up the learning process similar to a non-medical computer vision task. the official implementation and cell annotations are publicly available at https://***/hrlblab/MolecularEL.

关键词： image annotation Registration Noisy label learning Pathology

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Frequency-Mixed Single-Source Domain Generalization for medical image Segmentation 26th

Frequency-Mixed Single-Source Domain Generalization for Medi...

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

作者： Li, Heng Li, Haojin Zhao, Wei Fu, Huazhu Su, Xiuyun Hu, Yan Liu, Jiang Southern Univ Sci & Technol Res Inst Trustworthy Autonomous Syst Shenzhen Peoples R China Southern Univ Sci & Technol Dept Comp Sci & Engn Shenzhen Peoples R China Southern Univ Sci & Technol Med Intelligence & Innovat Acad Shenzhen Peoples R China Southern Univ Sci & Technol Hosp Shenzhen Peoples R China Agcy Sci Res & Technol Inst High Performance Comp Singapore Singapore Southern Univ Sci & Technol Guangdong Prov Key Lab Braininspired Intelligent Shenzhen Peoples R China

ISBN: (纸本)9783031439865;9783031439872

the annotation scarcity of medical image segmentation poses challenges in collecting sufficient training data for deep learning models. Specifically, models trained on limited data may not generalize well to other unseen data domains, resulting in a domain shift issue. Consequently, domain generalization (DG) is developed to boost the performance of segmentation models on unseen domains. However, the DG setup requires multiple source domains, which impedes the efficient deployment of segmentation algorithms in clinical scenarios. To address this challenge and improve the segmentation model's generalizability, we propose a novel approach called the Frequency-mixed Single-source Domain Generalization method (FreeSDG). By analyzing the frequency's effect on domain discrepancy, FreeSDG leverages a mixed frequency spectrum to augment the single-source domain. Additionally, self-supervision is constructed in the domain augmentation to learn robust context-aware representations for the segmentation task. Experimental results on five datasets of three modalities demonstrate the effectiveness of the proposed algorithm. FreeSDG outperforms state-of-the-art methods and significantly improves the segmentation model's generalizability. therefore, FreeSDG provides a promising solution for enhancing the generalization of medical image segmentation models, especially when annotated data is scarce. the code is available at https://***/liamheng/Non-IID_medical_image_Segmentation.

关键词： medical image segmentation single-source domain generalization domain augmentation frequency spectrum

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Retinal Age Estimation with Temporal Fundus images Enhanced Progressive Label Distribution Learning 26th

Retinal Age Estimation with Temporal Fundus Images Enhanced ...

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

作者： Yu, Zhen Chen, Ruiye Gui, Peng Ju, Lie Shang, Xianwen Zhu, Zhuoting He, Mingguang Ge, Zongyuan Monash Univ Cent Clin Sch Fac Med Nursing & Hlth Sci Melbourne Vic Australia Monash Univ AIM Hlth Lab Melbourne Vic Australia Monash Univ Monash Med AI Melbourne Vic Australia Wuhan Univ Sch Comp Sci Wuhan Hubei Peoples R China Univ Melbourne Ctr Eye Res Australia Melbourne Vic Australia Univ Melbourne Dept Surg Ophthalmol Melbourne Vic Australia Monash Univ Fac Engn Melbourne Vic Australia Monash Univ Fac IT Melbourne Vic Australia

ISBN: (纸本)9783031439896;9783031439902

Retinal age has recently emerged as a reliable ageing biomarker for assessing risks of ageing-related diseases. Several studies propose to train deep learning models to estimate retinal age from fundus images. However, the limitation of these studies lies in 1) both of them only train models on snapshot images from single cohorts;2) they ignore label ambiguity and individual variance in the modeling part. In this study, we propose a progressive label distribution learning (LDL) method with temporal fundus images to improve the retinal age estimation on snapshot fundus images from multiple cohorts. First, we design a two-stage LDL regression head to estimate adaptive age distribution for individual images. then, we eliminate cohort variance by introducing ordinal constraints to align image features from different data sources. Finally, we add a temporal branch to model sequential fundus images and use the captured temporal evolution as auxiliary knowledge to enhance the model's predictive performance on snapshot fundus images. We use a large retinal fundus image dataset which consists of similar to 130k images from multiple cohorts to verify our method. Extensive experiments provide evidence that our model can achieve lower age prediction errors than existing methods.

关键词： Retinal age estimation label distribution learning temporal fundus image

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3D medical image Segmentation with Sparse Annotation via Cross-Teaching Between 3D and 2D Networks 1

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

作者： Cai, Heng Qi, Lei Yu, Qian Shi, Yinghuan Gao, Yang Nanjing Univ Natl Inst Hlth Care Data Sci State Key Lab Novel Software Technol Nanjing Peoples R China Southeast Univ Sch Comp Sci & Engn Nanjing Peoples R China Shandong Womens Univ Sch Data & Comp Sci Jinan Peoples R China

ISBN: (数字)9783031438981

ISBN: (纸本)9783031438974;9783031438981

medical image segmentation typically necessitates a large and precisely annotated dataset. However, obtaining pixel-wise annotation is a labor-intensive task that requires significant effort from domain experts, making it challenging to obtain in practical clinical scenarios. In such situations, reducing the amount of annotation required is a more practical approach. One feasible direction is sparse annotation, which involves annotating only a few slices, and has several advantages over traditional weak annotation methods such as bounding boxes and scribbles, as it preserves exact boundaries. However, learning from sparse annotation is challenging due to the scarcity of supervision signals. To address this issue, we propose a framework that can robustly learn from sparse annotation using the cross-teaching of both 3D and 2D networks. Considering the characteristic of these networks, we develop two pseudo label selection strategies, which are hard-soft confidence threshold and consistent label fusion. Our experimental results on the MMWHS dataset demonstrate that our method outperforms the state-of-the-art (SOTA) semi-supervised segmentation methods. Moreover, our approach achieves results that are comparable to the fully-supervised upper bound result. Our code is available at https://***/HengCai-NJU/3D2DCT.

关键词： 3D segmentation Sparse annotation Cross-teaching

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Adaptive Region Selection for Active Learning in Whole Slide image Semantic Segmentation 26th

Adaptive Region Selection for Active Learning in Whole Slide...

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

作者： Qiu, Jingna Wilm, Frauke Oettl, Mathias Schlereth, Maja Liu, Chang Heimann, Tobias Aubreville, Marc Breininger, Katharina Friedrich Alexander Univ Erlangen Nurnberg Dept Artificial Intelligence Biomed Engn Erlangen Germany Friedrich Alexander Univ Erlangen Nurnberg Dept Comp Sci Pattern Recognit Lab Erlangen Germany Siemens Healthineers Digital Technol & Innovat Erlangen Germany Tech Hsch Ingolstadt Ingolstadt Germany

ISBN: (纸本)9783031438943;9783031438950

the process of annotating histological gigapixel-sized whole slide images (WSIs) at the pixel level for the purpose of training a supervised segmentation model is time-consuming. Region-based active learning (AL) involves training the model on a limited number of annotated image regions instead of requesting annotations of the entire images. these annotation regions are iteratively selected, with the goal of optimizing model performance while minimizing the annotated area. the standard method for region selection evaluates the informativeness of all square regions of a specified size and then selects a specific quantity of the most informative regions. We find that the efficiency of this method highly depends on the choice of AL step size (i.e., the combination of region size and the number of selected regions per WSI), and a suboptimal AL step size can result in redundant annotation requests or inflated computation costs. this paper introduces a novel technique for selecting annotation regions adaptively, mitigating the reliance on this AL hyperparameter. Specifically, we dynamically determine each region by first identifying an informative area and then detecting its optimal bounding box, as opposed to selecting regions of a uniform predefined shape and size as in the standard method. We evaluate our method using the task of breast cancer metastases segmentation on the public CAMELYON16 dataset and show that it consistently achieves higher sampling efficiency than the standard method across various AL step sizes. With only 2.6% of tissue area annotated, we achieve full annotation performance and thereby substantially reduce the costs of annotating a WSI dataset. the source code is available at https://***/ DeepMicroscopy/AdaptiveRegionSelection.

关键词： Active learning Region selection Whole slide images

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SATTA: Semantic-Aware Test-Time Adaptation for Cross-Domain medical image Segmentation 26th

SATTA: Semantic-Aware Test-Time Adaptation for Cross-Domain ...

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

作者： Zhang, Yuhan Huang, Kun Chen, Cheng Chen, Qiang Pheng-Ann Heng Chinese Univ Hong Kong Dept Comp Sci & Engn Hong Kong Peoples R China Chinese Univ Hong Kong Inst Med Intelligence & XR Hong Kong Peoples R China Chinese Univ Hong Kong Shenzhen Res Inst Hong Kong Peoples R China Nanjing Univ Sci & Technol Dept Comp Sci & Engn Nanjing Peoples R China Harvard Med Sch Ctr Adv Med Comp & Anal Boston MA 02115 USA Massachusetts Gen Hosp Boston MA 02114 USA

ISBN: (纸本)9783031438943;9783031438950

Cross-domain distribution shift is a common problem for medical image analysis because medical images from different devices usually own varied domain distributions. Test-time adaptation (TTA) is a promising solution by efficiently adapting source-domain distributions to target-domain distributions at test time with unsupervised manners, which has increasingly attracted important attention. Previous TTA methods applied to medical image segmentation tasks usually carry out a global domain adaptation for all semantic categories, but global domain adaptation would be sub-optimal as the influence of domain shift on different semantic categories may be different. To obtain improved domain adaptation results for different semantic categories, we propose Semantic-Aware Test-Time Adaptation (SATTA), which can individually update the model parameters to adapt to target-domain distributions for each semantic category. Specifically, SATTA deploys an uncertainty estimation module to measure the discrepancies of semantic categories in domain shift effectively. then, a semantic adaptive learning rate is developed based on the estimated discrepancies to achieve a personalized degree of adaptation for each semantic category. Lastly, semantic proxy contrastive learning is proposed to individually adjust the model parameters with the semantic adaptive learning rate. Our SATTA is extensively validated on retinal fluid segmentation based on SD-OCT images. the experimental results demonstrate that SATTA consistently improves domain adaptation performance on semantic categories over other state-of-the-art TTA methods.

关键词： test-time adaptation domain shift medical image segmentation

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AI-Based Bone Cancer Detection Using image Processing and CNN 5th

AI-Based Bone Cancer Detection Using Image Processing and CN...

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5th EAI international conference on Cognitive computing and Cyber Physical Systems, IC4S 2024

作者： Srividya, K. Reddy, Gangannagari Varunteja Bakki, Vishwaja Adilakshmi, T. Department of Computer Science and Engineering Vasavi College of Engineering Hyderabad India

ISBN: (纸本)9783031770777

this study presents a new Technology-driven approach for early detection of bone cancer using preliminary image processing technologies and neural networks (CNN) used for diagnosing cancer from pathological images. the main aim is to provide a powerful and reliable tool for cancer diagnosis done in clinics. Survival rates of cancer decrease with an increase in age, urging the need for early diagnosis and precaution. the project aims to use machine learning and image preprocessing to provide solutions for the detection of cancer, necessary for timely intervention. Using carefully selected datasets and advanced image preprocessing techniques, the project aims to provide doctors with reliable tools to accurately diagnose bone cancer. Analysis of existing literature demonstrates the effectiveness of CNN and deep learning algorithms in the analysis of medical images as the basis of the proposed method. the proposed methodology includes image enhancement, data acquisition, CNN development, training, validation, transformation, and model evaluation. the CNN model is evaluated on accuracy, precision, recall, and f1-scores of bone cancer diagnosis images. It provides insight into model development by addressing issues such as classification error, class inequality, and biases. While comparisons with existing literature demonstrate the model's uses, practical considerations are also discussed. the findings suggest a significant potential to improve bone cancer diagnosis and stimulate further research on improving CNN model architecture and developing real-world deployment strategies. the training accuracy of the CNN model came out to be 94.64%. © ICST Institute for computer Sciences, Social Informatics and Telecommunications Engineering 2025.

关键词： Diseases

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atTRACTive: Semi-automatic White Matter Tract Segmentation Using Active Learning 26th

atTRACTive: Semi-automatic White Matter Tract Segmentation U...

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

作者： Peretzke, Robin Maier-Hein, Klaus H. Bohn, Jonas Kirchhoff, Yannick Roy, Saikat Oberli-Palma, Sabrina Becker, Daniela Lenga, Pavlina Neher, Peter German Canc Res Ctr Div Med Image Comp MIC Heidelberg Germany Heidelberg Univ Med Fac Heidelberg Heidelberg Germany German Canc Consortium DKTK Partner Site Heidelberg Heidelberg Germany NCT Heidelberg Natl Ctr Tumor Dis NCT Heidelberg Germany DKFZ Heidelberg Germany Univ Med Ctr Heidelberg Heidelberg Germany Univ Heidelberg Hosp Pattern Anal & Learning Grp Heidelberg Germany HIDSS4Health Helmholtz Informat & Data Sci Sch Hlth Heidelberg Germany Heidelberg Univ Fac Math & Comp Sci Heidelberg Germany Heidelberg Univ Fac Biosci Heidelberg Germany Univ Heidelberg Hosp Dept Neurosurg Heidelberg Germany IU Int Univ Appl Sci Erfurt Germany

ISBN: (纸本)9783031439926;9783031439933

Accurately identifying white matter tracts in medical images is essential for various applications, including surgery planning and tract-specific analysis. Supervised machine learning models have reached state-of-the-art solving this task automatically. However, these models are primarily trained on healthy subjects and struggle with strong anatomical aberrations, e.g. caused by brain tumors. this limitation makes them unsuitable for tasks such as preoperative planning, wherefore time-consuming and challenging manual delineation of the target tract is typically employed. We propose semi-automatic entropy-based active learning for quick and intuitive segmentation of white matter tracts from whole-brain tractography consisting of millions of streamlines. the method is evaluated on 21 openly available healthy subjects from the Human Connectome Project and an internal dataset of ten neurosurgical cases. With only a few annotations, the proposed approach enables segmenting tracts on tumor cases comparable to healthy subjects (dice = 0.71), while the performance of automatic methods, like TractSeg dropped substantially (dice = 0.34) in comparison to healthy subjects. the method is implemented as a prototype named atTRACTive in the freely available software MITK Diffusion. Manual experiments on tumor data showed higher efficiency due to lower segmentation times compared to traditional ROI-based segmentation.

关键词： DWI and tractography Active learning Tract segmentation Fiber tracking

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Coupling Bracket Segmentation and Tooth Surface Reconstruction on 3D Dental Models 26th

Coupling Bracket Segmentation and Tooth Surface Reconstructi...

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

作者： Tan, Yuwen Xiang, Xiang Chen, Yifeng Jing, Hongyi Ye, Shiyang Xue, Chaoran Xu, Hui Huazhong Univ Sci & Technol Minist Educ Key Lab Image Proc & Intelligent Control Sch Artificial Intelligence & Automat Wuhan Peoples R China Sichuan Univ West China Hosp Stomatol State Key Lab Oral Dis Natl Clin Res Ctr Oral Dis Chengdu Peoples R China

ISBN: (纸本)9783031439865;9783031439872

Delineating and removing brackets on 3D dental models and then reconstructing the tooth surface can enable orthodontists to premake retainers for patients. It eliminates the waiting time and avoids the change of tooth position. However, it is time-consuming and laborintensive to process 3D dental models manually. To automate the entire process, accurate bracket segmentation and tooth surface reconstruction algorithms are of high need. In this paper, we propose a graph-based network named BSegNet for bracket segmentation on 3D dental models. the dynamic dilated neighborhood construction and residual connection in the graph network promote the bracket segmentation performance. then, we propose a simple yet effective projection-based method to reconstruct the tooth surface. We project the vertices of the hole boundary on the tooth surface onto a 2D plane and then triangulate the projected polygon. We evaluate the performance of BSegNet on the bracket segmentation dataset and the results show the superiority of our method. the framework integrating the segmentation and reconstruction achieves a low reconstruction error and can be used as an effective tool to assist orthodontists in orthodontic treatment.

关键词： 3D dental surface bracket segmentation surface reconstruction deep learning orthodontic treatment

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Learning Iterative Optimisation for Deformable image Registration of Lung CT with Recurrent Convolutional Networks 25th

Learning Iterative Optimisation for Deformable Image Registr...

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

作者： Falta, Fenja Hansen, Lasse Heinrich, Mattias P. Univ Lubeck Inst Med Informat Lubeck Germany

ISBN: (纸本)9783031164460;9783031164453

Deep learning-based methods for deformable image registration have continually been increasing in accuracy. However, conventional methods using optimisation remain ubiquitous, as they often outperform deep learning-based methods regarding accuracy on test data. Recent learning-based methods for lung registration tasks prevalently employ instance optimisation on test data to achieve state-of-the-art performance. We propose a fully deep learning-based approach, that aims to emulate the structure of gradient-based optimisation as used in conventional registration and thus learns how to optimise. Our architecture consists of recurrent updates on a convolutional network with deep supervision. It uses a dynamic sampling of the cost function, hidden states to imitate information flow during optimisation and incremental displacements for multiple iterations. Our code is publicly available at https:// *** multimodallearning/Learn2Optimise/.

关键词： image registration Optimisation Convolutional network

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