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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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Cross-View Deformable Transformer for Non-displaced Hip Fracture Classification from Frontal-Lateral X-Ray Pair 26th

Cross-View Deformable Transformer for Non-displaced Hip Frac...

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

作者： Zhu, Zhonghang Chen, Qichang Yu, Lequan Wang, Lianxin Zhang, Defu Magnier, Baptiste Wang, Liansheng Xiamen Univ Sch Informat Dept Comp Sci Xiamen Peoples R China Univ Hong Kong Dept Stat & Actuarial Sci Hong Kong Peoples R China Xiamen Univ Affiliated Hosp 1 Dept Orthoped Xiamen Peoples R China Univ Montpellier IMT Mines Ales Euromov Digital Hlth Motion Ales France

ISBN: (纸本)9783031439865;9783031439872

Hip fractures are a common cause of morbidity and mortality and are usually diagnosed from the X-ray images in clinical routine. Deep learning has achieved promising progress for automatic hip fracture detection. However, for fractures where displacement appears not obvious (i.e., non-displaced fracture), the single-view X-ray image can only provide limited diagnostic information and integrating features from cross-view X-ray images (i.e., Frontal/Lateral-view) is needed for an accurate diagnosis. Nevertheless, it remains a technically challenging task to find reliable and discriminative cross-view representations for automatic diagnosis. First, it is difficult to locate discriminative task-related features in each X-ray view due to the weak supervision of image-level classification labels. Second, it is hard to extract reliable complementary information between different X-ray views as there is a displacement between them. To address the above challenges, this paper presents a novel cross-view deformable transformer framework to model relations of critical representations between different views for non-displaced hip fracture identification. Specifically, we adopt a deformable self-attention module to localize discriminative task-related features for each X-ray view only with the image-level label. Moreover, the located discriminative features are further adopted to explore correlated representations across views by taking advantage of the query of the dominated view as guidance. Furthermore, we build a dataset including 768 hip cases, in which each case has paired hip X-ray images (Frontal/Lateral-view), to evaluate our framework for the non-displaced fracture and normal hip classification task.

关键词： Hip fracture diagnosis X-ray image Deformable transformer Cross-view correspondence

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Domain Generalization in Myocardial Pathology Segmentation with MixUp Augmentation 1st

Domain Generalization in Myocardial Pathology Segmentation ...

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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： Chen, Leyang Tu, Yaosheng Bai, Penggang Pan, Lin College of Physics and Information Engineering Fuzhou University Fuzhou China Department of Radiation Oncology Fujian Cancer Hospital and Fujian Medical University Cancer Hospital Fujian Fuzhou China

ISBN: (纸本)9783031870088

MyoPS (Myocardial Pathology Segmentation) is used for auxiliary diagnosis of myocardial infarction by accurately segmenting myocardial lesions (such as scars and edema). However, CMR images are complex, manual segmentation is time-consuming and relies on professional knowledge, and there are differences in imaging data from different centers, which increases the difficulty of segmentation. To this end, this study developed a domain generalization module that flexibly integrates LGE, T2-weighted, and Cine sequences to improve cross-center and multi-sequence adaptability and robustness. Our method combines the domain generalization module with the nnUNet segmentation network, and reduces the differences between different data distributions by utilizing the domain generalization module for data mixing enhancement, thereby enhancing the model’s generalization ability and improving segmentation performance. In tests conducted on the data set of the MyoPS++ Challenge, our network performed well in segmenting scars and edema. Compared with the native segmentation network, it has a greater performance improvement, which verifies its ability to handle multi-center, Effectiveness in multi-sequence CMR data. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： image segmentation

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Y-Net: A Spatiospectral Dual-Encoder Network for medical image Segmentation 25th

Y-Net: A Spatiospectral Dual-Encoder Network for Medical Ima...

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

作者： Farshad, Azade Yeganeh, Yousef Gehlbach, Peter Navab, Nassir Tech Univ Munich Munich Germany Johns Hopkins Univ Baltimore MD USA

ISBN: (纸本)9783031164347;9783031164330

Automated segmentation of retinal optical coherence tomography (OCT) images has become an important recent direction in machine learning for medical applications. We hypothesize that the anatomic structure of layers and their high-frequency variation in OCT images make retinal OCT a fitting choice for extracting spectral domain features and combining them with spatial domain features. In this work, we present YNet , an architecture that combines the frequency domain features with the image domain to improve the segmentation performance of OCT images. the results of this work demonstrate that the introduction of two branches, one for spectral and one for spatial domain features, brings very significant improvement in fluid segmentation performance and allows outperformance as compared to the well-known U-Net model. Our improvement was 13% on the fluid segmentation dice score and 1.9% on the average dice score. Finally, removing selected frequency ranges in the spectral domain demonstrates the impact of these features on the fluid segmentation outperformance. Code: ***/azadef/ynet

关键词： OCT segmentation Frequency domain in OCT U-Net

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Fine-Grained Unsupervised Cross-Modality Domain Adaptation for Vestibular Schwannoma Segmentation 1

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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： Han, Luyi Tan, Tao Mann, Ritse Department of Radiology and Nuclear Medicine Radboud University Medical Center Geert Grooteplein 10 Nijmegen6525 GA Netherlands Department of Radiology The Netherlands Cancer Institute Plesmanlaan 121 Amsterdam1066 CX Netherlands Faculty of Applied Sciences Macao Polytechnic University Macao999078 China

ISBN: (数字)9783031761638

ISBN: (纸本)9783031761621

the domain adaptation approach has gained significant acceptance in transferring styles across various vendors and centers, along with filling the gaps in modalities. However, multi-center application faces the challenge of the difficulty of domain adaptation due to their intra-domain differences. We focus on introducing a fine-grained unsupervised framework for domain adaptation to facilitate cross-modality segmentation of vestibular schwannoma (VS) and cochlea. We propose to use a vector to control the generator to synthesize a fake image with given features. And then, we can apply various augmentations to the dataset by searching the feature dictionary. the diversity augmentation can increase the performance and robustness of the segmentation model. On the CrossMoDA2023 test phase leaderboard, our method received a mean Dice score of 0.773 and 0.843 on VS and cochlea, respectively. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： image segmentation

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Contrastive Learning for Echocardiographic View Integration 25th

Contrastive Learning for Echocardiographic View Integration

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

作者： Cheng, Li-Hsin Sun, Xiaowu van der Geest, Rob J. Leiden Univ Med Ctr Dept Radiol Div Image Proc Leiden Netherlands

ISBN: (纸本)9783031164408;9783031164392

In this work, we aimed to tackle the challenge of fusing information from multiple echocardiographic views, mimicking cardiologists making diagnoses with an integrative approach. For this purpose, we used the available information provided in the CAMUS dataset to experiment combining 2D complementary views to derive 3D information of left ventricular (LV) volume. We proposed intra-subject and inter-subject volume contrastive losses with varying margin to encode heterogeneous input views to a shared view-invariant volume-relevant feature space, where feature fusion can be facilitated. the results demonstrated that the proposed contrastive losses successfully improved the integration of complementary information from the input views, achieving significantly better volume predictive performance (MAE: 10.96 ml, RMSE: 14.75 ml, R-2 : 0.88) than that of the late-fusion baseline without contrastive losses (MAE: 13.17 ml, RMSE: 17.91 ml, R-2 : 0.83). Code available at: https://***/LishinC/VCN.

关键词： Contrastive learning Multi-view integration Echocardiogram Left ventricular volume regression

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11th international Workshop on Biomedical image Registration, WBIR 2024, held in conjunction with the 27th international conference on medical image computing and computer assisted intervention, MICCAI 2024

作者： Grzech, Daniel Folgoc, Loïc Le Azampour, Mohammad Farid Vlontzos, Athanasios Glocker, Ben Navab, Nassir Schnabel, Julia Kainz, Bernhard Imperial College London London United Kingdom Munich Center for Machine Learning Munich Germany Technische Universität München Munich Germany Spotify Technology SA London United Kingdom Télécom Paris Palaiseau France King’s College London London United Kingdom Johns Hopkins University BaltimoreMD United States Friedrich-Alexander-Universität Erlangen-Nürnberg Erlangen Germany

ISBN: (纸本)9783031734793

We present a new model for deformable image registration, which learns in an unsupervised way a data-specific similarity metric. the proposed method consists of two neural networks, one that maps pairs of input images to transformations which align them, and one that provides the similarity metric whose maximisation guides the image alignment. We parametrise the similarity metric as an energy-based model, which is simple to train and allows us to improve the accuracy of image registration compared to other models with learnt similarity metrics by taking advantage of a more general mathematical formulation, as well as larger datasets. We also achieve substantial improvement in the accuracy of inter-patient image registration on MRI scans from the OASIS dataset compared to models that rely on traditional functions. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Contrastive Learning

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Embedding Gradient-Based Optimization in image Registration Networks 25th

Embedding Gradient-Based Optimization in Image Registration ...

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

作者： Qiu, Huaqi Hammernik, Kerstin Qin, Chen Chen, Chen Rueckert, Daniel Imperial Coll London BioMedIA Grp London England Tech Univ Munich Klinikum Rechts Isar Munich Germany Univ Edinburgh Inst Digital Commun Edinburgh Scotland

ISBN: (纸本)9783031164460;9783031164453

Deep learning (DL) image registration methods amortize the costly pair-wise iterative optimization by training deep neural networks to predict the optimal transformation in one fast forward-pass. In this work, we bridge the gap between traditional iterative energy optimization-based registration and network-based registration, and propose Gradient Descent Network for image Registration (GraDIRN). Our proposed approach trains a DL network that embeds unrolled multi-resolution gradient-based energy optimization in its forward pass, which explicitly enforces image dissimilarity minimization in its update steps. Extensive evaluations were performed on registration tasks using 2D cardiac MR and 3D brain MR images. We demonstrate that our approach achieved state-of-the-art registration performance while using fewer learned parameters, with good data efficiency and domain robustness.

关键词： medical image registration Deep learning Unrolled optimization network

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Regression Metric Loss: Learning a Semantic Representation Space for medical images 25th

Regression Metric Loss: Learning a Semantic Representation S...

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

作者： Chao, Hanqing Zhang, Jiajin Yan, Pingkun Rensselaer Polytech Inst Dept Biomed Engn Ctr Biotechnol & Interdisciplinary Studies Troy NY 12180 USA

ISBN: (纸本)9783031164521;9783031164514

Regression plays an essential role in many medical imaging applications for estimating various clinical risk or measurement scores. While training strategies and loss functions have been studied for the deep neural networks in medical image classification tasks, options for regression tasks are very limited. One of the key challenges is that the high-dimensional feature representation learned by existing popular loss functions like Mean Squared Error or L1 loss is hard to interpret. In this paper, we propose a novel Regression Metric Loss (RM-Loss), which endows the representation space with the semantic meaning of the label space by finding a representation manifold that is isometric to the label space. Experiments on two regression tasks, i.e. coronary artery calcium score estimation and bone age assessment, show that RM-Loss is superior to the existing popular regression losses on both performance and interpretability. Code is available at https://github. com/DIAL-RPI/Regression-Metric-Loss.

关键词： medical image regression Metric learning Representation learning Semantic representation Interpretability

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DGMIL: Distribution Guided Multiple Instance Learning for Whole Slide image Classification 25th

DGMIL: Distribution Guided Multiple Instance Learning for Wh...

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

作者： Qu, Linhao Luo, Xiaoyuan Liu, Shaolei Wang, Manning Song, Zhijian Fudan Univ Shanghai Key Lab Med Image Comp & Comp Assisted I Sch Basic Med Sci Digital Med Res Ctr Shanghai 200032 Peoples R China

ISBN: (纸本)9783031164347;9783031164330

Multiple Instance Learning (MIL) is widely used in analyzing histopathological Whole Slide images (WSIs). However, existing MIL methods do not explicitly model the data distribution, and instead they only learn a bag-level or instance-level decision boundary discriminatively by training a classifier. In this paper, we propose DGMIL: a feature distribution guided deep MIL framework for WSI classification and positive patch localization. Instead of designing complex discriminative network architectures, we reveal that the inherent feature distribution of histopathological image data can serve as a very effective guide for instance classification. We propose a cluster-conditioned feature distribution modeling method and a pseudo label-based iterative feature space refinement strategy so that in the final feature space the positive and negative instances can be easily separated. Experiments on the CAME-LYON16 dataset and the TCGA Lung Cancer dataset show that our method achieves new SOTA for both global classification and positive patch localization tasks.

关键词： Histopathological images Multiple Instance Learning

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Hybrid image Processing Device as Wearable Aide for Visually Impaired 8

Hybrid Image Processing Device as Wearable Aide for Visually...

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8th international conference on Advanced computing and Communication Systems, ICACCS 2022

作者： Papanai, Ashish Kaushik, Harsh Maharaja Agrasen Institute of Technology Department of Computer Science and Engineering Delhi India

ISBN: (纸本)9781665408165

With the advent of technology and algorithms in image processing, many wearable aides are available in the market, and researchers across the globe are developing new solutions. the existing solutions and products fail to provide an economically viable and single solution to the problem of visually impaired people. this study introduces AIde, a novel system amalgamating optimized computer vision algorithms and wearable IoT devices to convert the information extracted from the camera mounted on the user to audio-based signals for assisted mobility. the proposed system has three user modes: indoor, outdoor, and hybrid, which can be modified using the buttons provided on the wearable device. the indoor mode can be used for object detection and classification to increase the accessibility of the objects inside a confined space, and the outdoor mode will provide the user with enhanced mobility and directional sense based on the highlighted path captured by the mounted camera. the hybrid model combines both the features of indoor and outdoor modes, which will detect objects and improve mobility simultaneously. the proposed model is optimized to work in a computationally efficient environment with an at par efficiency and accuracy as the state-of-art and costly market alternatives. © 2022 IEEE.

关键词： Edge computing

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