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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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Is Your Style Transfer Doing Anything Useful? An Investigation into Hippocampus Segmentation and the Role of Preprocessing 7th

Is Your Style Transfer Doing Anything Useful? An Investigati...

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7th international Workshop on Machine Learning in Clinical Neuroimaging, MLCN 2024, Held in Conjunction with 27th international conference on medical image computing and computer-assisted intervention, MICCAI 2024

作者： Kalabizadeh, Hoda Griffanti, Ludovica Yeung, Pak-Hei Voets, Natalie Gillis, Grace Mackay, Clare Namburete, Ana IL Dinsdale, Nicola K. Kamnitsas, Konstantinos Department of Computer Science University of Oxford Oxford United Kingdom Department of Psychiatry University of Oxford Oxford United Kingdom College of Computing and Data Science Nanyang Technological University Singapore Singapore Nuffield Department of Clinical Neurosciences University of Oxford Oxford United Kingdom Department of Engineering Science University of Oxford Oxford United Kingdom

ISBN: (纸本)9783031787607

Brain atrophy assessment in MRI, particularly of the hippocampus, is commonly used to support diagnosis and monitoring of dementia. Consequently, there is a demand for accurate automated hippocampus quantification. Most existing segmentation methods have been developed and validated on research datasets and, therefore, may not be appropriate for clinical MR images and populations, leading to potential gaps between dementia research and clinical practice. In this study, we investigated the performance of segmentation models trained on research data that were style-transferred to resemble clinical scans. Our results highlighted the importance of intensity normalisation methods in MRI segmentation, and their relation to domain shift and style-transfer. We found that whilst normalising intensity based on min and max values, commonly used in generative MR harmonisation methods, may create a need for style transfer, Z-score normalisation effectively maintains style consistency, and optimises performance. Moreover, we show for our datasets spatial augmentations are more beneficial than style harmonisation. thus, emphasising robust normalisation techniques and spatial augmentation significantly improves MRI hippocampus segmentation. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Clinical research

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Degradation-Invariant Enhancement of Fundus images via Pyramid Constraint Network 25th

Degradation-Invariant Enhancement of Fundus Images via Pyram...

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

作者： Liu, Haofeng Li, Heng Fu, Huazhu Xiao, Ruoxiu Gao, Yunshu Hu, Yan Liu, Jiang Southern Univ Sci & Technol Guangdong Prov Key Lab Brain Inspired Intelligent Shenzhen Peoples R China Southern Univ Sci & Technol Dept Comp Sci & Engn Shenzhen Peoples R China ASTAR IHPC Singapore Singapore Univ Sci & Technol Beijing Sch Comp & Commun Engn Beijing Peoples R China Singapore Natl Eye Ctr Singapore Eye Res Inst Singapore Singapore

ISBN: (纸本)9783031164347;9783031164330

As an economical and efficient fundus imaging modality, retinal fundus images have been widely adopted in clinical fundus examination. Unfortunately, fundus images often suffer from quality degradation caused by imaging interferences, leading to misdiagnosis. Despite impressive enhancement performances that state-of-the-art methods have achieved, challenges remain in clinical scenarios. For boosting the clinical deployment of fundus image enhancement, this paper proposes the pyramid constraint to develop a degradation-invariant enhancement network (PCE-Net), which mitigates the demand for clinical data and stably enhances unknown data. Firstly, high-quality images are randomly degraded to form sequences of low-quality ones sharing the same content (SeqLCs). then individual low-quality images are decomposed to Laplacian pyramid features (LPF) as the multi-level input for the enhancement. Subsequently, a feature pyramid constraint (FPC) for the sequence is introduced to enforce the PCE-Net to learn a degradation-invariant model. Extensive experiments have been conducted under the evaluation metrics of enhancement and segmentation. the effectiveness of the PCENet was demonstrated in comparison with state-of-the-art methods and the ablation study. the source code of this study is publicly available at https://***/HeverLaw/PCENet-image-Enhancement.

关键词： Fundus image Degradation-invariant enhancement Laplacian pyramid feature Feature pyramid constraint

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Uni4Eye: Unified 2D and 3D Self-supervised Pre-training via Masked image Modeling Transformer for Ophthalmic image Classification 25th

Uni4Eye: Unified 2D and 3D Self-supervised Pre-training via ...

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

作者： Cai, Zhiyuan Lin, Li He, Huaqing Tang, Xiaoying Southern Univ Sci & Technol Dept Elect & Elect Engn Shenzhen Peoples R China Univ Hong Kong Dept Elect & Elect Engn Pok Fu Lam Hong Kong Peoples R China

ISBN: (纸本)9783031164521;9783031164514

A large-scale labeled dataset is a key factor for the success of supervised deep learning in computer vision. However, a limited number of annotated data is very common, especially in ophthalmic image analysis, since manual annotation is time-consuming and labor-intensive. Self-supervised learning (SSL) methods bring huge opportunities for better utilizing unlabeled data, as they do not need massive annotations. With an attempt to use as many as possible unlabeled ophthalmic images, it is necessary to break the dimension barrier, simultaneously making use of both 2D and 3D images. In this paper, we propose a universal self-supervised Transformer framework, named Uni4Eye, to discover the inherent image property and capture domain-specific feature embedding in ophthalmic images. Uni4Eye can serve as a global feature extractor, which builds its basis on a Masked image Modeling task with a Vision Transformer (ViT) architecture. We employ a Unified Patch Embedding module to replace the origin patch embedding module in ViT for jointly processing both 2D and 3D input images. Besides, we design a dual-branch multitask decoder module to simultaneously perform two reconstruction tasks on the input image and its gradient map, delivering discriminative representations for better convergence. We evaluate the performance of our pre-trained Uni4Eye encoder by fine-tuning it on six downstream ophthalmic image classification tasks. the superiority of Uni4Eye is successfully established through comparisons to other state-of-the-art SSL pre-training methods.

关键词： Self-supervised pre-training Unified 2D and 3D Vision transformer Ophthalmic disease classification Multitask

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Diabetic Foot Ulcer Grand Challenge 2022 Summary 3rd

Diabetic Foot Ulcer Grand Challenge 2022 Summary

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3rd Diabetic Foot Ulcers Grand Challenge, DFUC 2022, held in Conjunction with the 25th international conference on medical image computing and computer-assisted intervention, MICCAI 2022

作者： Kendrick, Connah Cassidy, Bill Reeves, Neil D. Pappachan, Joseph M. O’Shea, Claire Chandrabalan, Vishnu Yap, Moi Hoon Department of Computing and Mathematics Manchester Metropolitan University ManchesterM1 5GD United Kingdom Musculoskeletal Science and Sports Medicine Manchester Metropolitan University ManchesterM1 5GD United Kingdom Lancashire Teaching Hospitals NHS Foundation Trust PrestonPR2 9HT United Kingdom Waikato District Health Board Hamilton3240 New Zealand

ISBN: (纸本)9783031263538

the Diabetic Foot Ulcer Challenge 2022 focused on the task of diabetic foot ulcer segmentation, based on the work completed in previous DFU challenges. the challenge provided 4000 images of full-view foot ulcer images together with corresponding delineation of ulcer regions. this paper provides an overview of the challenge, a summary of the methods proposed by the challenge participants, the results obtained from each technique, and a comparison of the challenge results. the best-performing network was a modified HarDNet-MSEG, with a Dice score of 0.7287. © 2023, the Author(s), under exclusive license to Springer Nature Switzerland AG.

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Camera Adaptation for Fundus-image-Based CVD Risk Estimation 25th

Camera Adaptation for Fundus-Image-Based CVD Risk Estimation

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

作者： Lin, Zhihong Shi, Danli Zhang, Donghao Shang, Xianwen He, Mingguang Ge, Zongyuan Monash Univ Clayton Vic 3800 Australia Sun Yat Sen Univ Zhongshan Ophthalm Ctr Guangzhou Peoples R China Univ Melbourne Parkville Vic 3010 Australia Monash eResearch Ctr Monash Airdoc Res Clayton Vic 3800 Australia

ISBN: (纸本)9783031164347;9783031164330

Recent studies have validated the association between cardiovascular disease (CVD) risk and retinal fundus images. Combining deep learning (DL) and portable fundus cameras will enable CVD risk estimation in various scenarios and improve healthcare democratization. However, there are still significant issues to be solved. One of the top priority issues is the different camera differences between the databases for research material and the samples in the production environment. Most high-quality retinography databases ready for research are collected from high-end fundus cameras, and there is a significant domain discrepancy between different cameras. To fully explore the domain discrepancy issue, we first collect a Fundus Camera Paired (FCP) dataset containing pair-wise fundus images captured by the high-end Topcon retinal camera and the low-end Mediwork portable fundus camera of the same patients. then, we propose a cross-laterality feature alignment pre-training scheme and a self-attention camera adaptor module to improve the model robustness. the cross-laterality feature alignment training encourages the model to learn common knowledge from the same patient's left and right fundus images and improve model generalization. Meanwhile, the device adaptation module learns feature transformation from the target domain to the source domain. We conduct comprehensive experiments on both the UK Biobank database and our FCP data. the experimental results show that the CVD risk regression accuracy and the result consistency over two cameras are improved with our proposed method. the code is available here: https://***/linzhlalala/CVD-risk-based-on-retinal-fundus-images

关键词： Retinal fundus image Cardiovascular diseases Domain adaptation Domain generalization

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Calibrating Label Distribution for Class-Imbalanced Barely-Supervised Knee Segmentation 25th

Calibrating Label Distribution for Class-Imbalanced Barely-S...

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

作者： Lin, Yiqun Yao, Huifeng Li, Zezhong Zheng, Guoyan Li, Xiaomeng Hong Kong Univ Sci & Technol Hong Kong Peoples R China Hong Kong Univ Sci & Technol Shenzhen Res Inst Hong Kong Peoples R China Shanghai Jiao Tong Univ Shanghai Peoples R China

ISBN: (纸本)9783031164521;9783031164514

Segmentation of 3D knee MR images is important for the assessment of osteoarthritis. Like other medical data, the volume-wise labeling of knee MR images is expertise-demanded and time-consuming;hence semi-supervised learning (SSL), particularly barely-supervised learning, is highly desirable for training with insufficient labeled data. We observed that the class imbalance problem is severe in the knee MR images as the cartilages only occupy 6% of foreground volumes, and the situation becomes worse without sufficient labeled data. To address the above problem, we present a novel framework for barely-supervised knee segmentation with noisy and imbalanced labels. Our framework leverages label distribution to encourage the network to put more effort into learning cartilage parts. Specifically, we utilize 1) label quantity distribution for modifying the objective loss function to a class-aware weighted form and 2) label position distribution for constructing a cropping probability mask to crop more sub-volumes in cartilage areas from both labeled and unlabeled inputs. In addition, we design dual uncertainty-aware sampling supervision to enhance the supervision of low-confident categories for efficient unsupervised learning. Experiments show that our proposed framework brings significant improvements by incorporating the unlabeled data and alleviating the problem of class imbalance. More importantly, our method outperforms the state-of-the-art SSL methods, demonstrating the potential of our framework for the more challenging SSL setting. Our code is available at https:/***/xmed-lab/CLD-Semi.

关键词： Semi-supervised learning Class imbalance Knee segmentation MRI image

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Fine-Grained Correlation Loss for Regression 25th

Fine-Grained Correlation Loss for Regression

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

作者： Chen, Chaoyu Yang, Xin Huang, Ruobing Hu, Xindi Huang, Yankai Lu, Xiduo Zhou, Xinrui Luo, Mingyuan Ye, Yinyu Shuang, Xue Miao, Juzheng Xiong, Yi Ni, Dong Shenzhen Univ Sch Biomed Engn Hlth Sci Ctr Natl Reg Key Technol Engn Lab Med Ultrasound Shenzhen Peoples R China Shenzhen Univ Med Ultrasound Image Comp MUSIC Lab Shenzhen Peoples R China Shenzhen Univ Marshall Lab Biomed Engn Shenzhen Peoples R China Shenzhen RayShape Med Technol Co Ltd Shenzhen Peoples R China Luohu Peoples Hosp Dept Ultrasound Shenzhen Peoples R China Southeast Univ Sch Biol Sci & Med Engn Nanjing Peoples R China

ISBN: (纸本)9783031164521;9783031164514

Regression learning is classic and fundamental for medical image analysis. It provides the continuous mapping for many critical applications, like the attribute estimation, object detection, segmentation and non-rigid registration. However, previous studies mainly took the case-wise criteria, like the mean square errors, as the optimization objectives. they ignored the very important population-wise correlation criterion, which is exactly the final evaluation metric in many tasks. In this work, we propose to revisit the classic regression tasks with novel investigations on directly optimizing the fine-grained correlation losses. We mainly explore two complementary correlation indexes as learnable losses: Pearson linear correlation (PLC) and Spearman rank correlation (SRC). the contributions of this paper are two folds. First, for the PLC on global level, we propose a strategy to make it robust against the outliers and regularize the key distribution factors. these efforts significantly stabilize the learning and magnify the efficacy of PLC. Second, for the SRC on local level, we propose a coarse-to-fine scheme to ease the learning of the exact ranking order among samples. Specifically, we convert the learning for the ranking of samples into the learning of similarity relationships among samples. We extensively validate our method on two typical ultrasound image regression tasks, including the image quality assessment and bio-metric measurement. Experiments prove that, with the fine-grained guidance in directly optimizing the correlation, the regression performances are significantly improved. Our proposed correlation losses are general and can be extended to more important applications.

关键词： Regression Ultrasound image Correlation Loss Functions

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Rethinking Surgical Instrument Segmentation: A Background image Can Be All You Need 25th

Rethinking Surgical Instrument Segmentation: A Background Im...

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

作者： Wang, An Islam, Mobarakol Xu, Mengya Ren, Hongliang Chinese Univ Hong Kong Dept Elect Engn Shatin Hong Kong Peoples R China CUHK Shun Hing Inst Adv Engn Shatin Hong Kong Peoples R China Imperial Coll London BioMedIA Grp Dept Comp London England Natl Univ Singapore Dept Biomed Engn Singapore Singapore

ISBN: (纸本)9783031164491;9783031164484

Data diversity and volume are crucial to the success of training deep learning models, while in the medical imaging field, the difficulty and cost of data collection and annotation are especially huge. Specifically in robotic surgery, data scarcity and imbalance have heavily affected the model accuracy and limited the design and deployment of deep learning-based surgical applications such as surgical instrument segmentation. Considering this, we rethink the surgical instrument segmentation task and propose a one-to-many data generation solution that gets rid of the complicated and expensive process of data collection and annotation from robotic surgery. In our method, we only utilize a single surgical background tissue image and a few open-source instrument images as the seed images and apply multiple augmentations and blending techniques to synthesize amounts of image variations. In addition, we also introduce the chained augmentation mixing during training to further enhance the data diversities. the proposed approach is evaluated on the real datasets of the EndoVis-2018 and EndoVis-2017 surgical scene segmentation. Our empirical analysis suggests that without the high cost of data collection and annotation, we can achieve decent surgical instrument segmentation performance. Moreover, we also observe that our method can deal with novel instrument prediction in the deployment domain. We hope our inspiring results will encourage researchers to emphasize data-centric methods to overcome demanding deep learning limitations besides data shortage, such as class imbalance, domain adaptation, and incremental learning. Our code is available at https:// ***/lofrienger/Single_SurgicalScene_For_Segmentation.

关键词： medical imaging

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A Coarse-to-Fine Ensembling Framework for Head and Neck Tumor and Lymph Segmentation in CT and PET images 1

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3rd 3D Head and Neck Tumor Segmentation in PET/CT Challenge, HECKTOR 2022, held in Conjunction with the 25th international conference on medical image computing and computer assisted intervention, MICCAI 2022

作者： Sun, Xiao An, Chengyang Wang, Lisheng Department of Automation Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University Shanghai China

ISBN: (数字)9783031274206

ISBN: (纸本)9783031274190

Head and neck (H &N) cancer is one of the most prevalent cancers [1]. In its treatment and prognosis analysis, tumors and metastatic lymph nodes may play an important role but their manual segmentations are time-consuming and laborious. In this paper, we propose a coarse-to-fine ensembling framework to segment the H &N tumor and metastatic lymph nodes automatically from Positron Emission Tomography (PET) and Computed Tomography (CT) images. the framework consists of three steps. the first step is to locate the head region in CT images. the second step is a coarse segmentation, to locate the tumor and lymph region of interest (ROI) from the head region. the last step is a fine segmentation, to get the final precise predictions of tumors and metastatic lymph nodes, where we proposed a ensembling refinement model. this framework is evaluated quantitatively with aggregated Dice Similarity Coefficient (DSC) of 0.77782 in the task 1 of the HECKTOR 2022 challenge[2, 3] as team SJTU426. © 2023, the Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Tumors

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the 19th international conference on medical image computing and computer-assisted intervention (MICCAI 2016)

The 19th International Conference on Medical Image Computing...

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作者： Ourselin, Sebastien Sabuncu, Mert R. Wells, William Joskowicz, Leo Unal, Gozde Maier, Andreas Universität Erlangen-Nürnberg Department of Computer Science Erlangen Germany

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