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

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Domain Unlearning Boosts Lesion Segmentation Performance on Seen and Unseen MR Scanner Data 9th

Domain Unlearning Boosts Lesion Segmentation Performance on ...

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9th international Workshop on Brain Lesion workshop, BrainLes 2023 and 3rd Stroke Workshop on Imaging and Treatment CHallenges, SWITCH 2023 Held in Conjunction with 26th medical image computing and computer assisted intervention, MICCAI 2023

作者： Preložnik, Domen Špiclin, Žiga Faculty of Electrical Engineering University of Ljubljana Tržaška Cesta 24 Ljubljana1000 Slovenia

ISBN: (纸本)9783031761591

Inter-scanner variability was highlighted in the 2020 MAGNIMS consensus guidelines as most-detrimental factor in image acquisition with high impact on the diagnostic and prognostic quality of the MR scans. this study aimed to evaluate and compare the contributions of domain unlearning on white matter lesion segmentation from the MR scans. We used the MSSEG 2016 challenge dataset of 53 MS patients, where MR images were acquired on 4 different scanners, one of which was absent in the training split. Our approach was the state-of-the-art Swin UNETR segmentation model that was adopted for domain unlearning to better handle inter-scanner bias. Performance of lesion segmentation was evaluated according to challenge protocol and compared to three best challenge results and the state-of-the-art results of recently proposed Dense Residual UNET method. the baseline Swin UNETR model achieved comparable results to the three challenge methods, while our domain unlearning model consistently improved in all metrics versus the baseline model and achieved the highest Dice score among all tested methods. the principal impact of unlearning was in reducing false positive annotations, whereas performance consistently improved on seen and unseen scanner data, indicating that scanner-specific intensity artifacts do not overlap with the information required for lesion segmentation. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： image segmentation

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End-to-End Multi-Slice-to-Volume Concurrent Registration and Multimodal Generation 25th

End-to-End Multi-Slice-to-Volume Concurrent Registration and...

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

作者： Leroy, Amaury Lerousseau, Marvin Henry, theophraste Cafaro, Alexandre Paragios, Nikos Gregoire, Vincent Deutsch, Eric Therapanacea Paris France Paris Saclay Univ Cent Supelec Gif Sur Yvette France Ctr Leon Berard Dept Radiat Oncol Lyon France Paris Saclay Univ Gustave Roussy Inserm Villejuif France

ISBN: (纸本)9783031164460;9783031164453

For interventional procedures, a real-time mapping between treatment guidance images and planning data is challenging yet essential for successful therapy implementation. Because of time and machine constraints, it involves imaging of different modalities, resolutions and dimensions, along with severe out-of-plane deformations to handle. In this paper, we introduce MSV-RegSyn-Net, a novel, scalable, deep learning-based framework for concurrent slice-to-volume registration and high-resolution modality transfer synthesis. It consists of an end-to-end pipeline made up of (i) a cycle generative adversarial network for multimodal image translation combined with (ii) a multi-slice-to-volume deformable registration network. the concurrent nature of our approach creates mutual benefit for both tasks: image translation is naturally eased by explicit handling of out-of-plane deformations while registration benefits from bringing multimodal signals into the same domain. Our model is fully unsupervised and does not require any ground-truth deformation or segmentation mask. It obtains superior qualitative and quantitative performance for multi-slice MR to 3D CT pelvic imaging compared to state-of-the-art traditional and learning-based methods on both tasks.

关键词： Multimodal registration 2D-3D image-to-image translation Generative adversarial network Unsupervised learning

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Domain Specific Convolution and High Frequency Reconstruction Based Unsupervised Domain Adaptation for medical image Segmentation 25th

Domain Specific Convolution and High Frequency Reconstructio...

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

作者： Hu, Shishuai Liao, Zehui Xia, Yong Northwestern Polytech Univ Natl Engn Lab Integrated Aerosp Ground Ocean Big Sch Comp Sci & Engn Xian 710072 Peoples R China Northwestern Polytech Univ Ningbo Inst Ningbo 315048 Peoples R China Northwestern Polytech Univ Shenzhen Res & Dev Inst Shenzhen 518057 Peoples R China

ISBN: (纸本)9783031164491;9783031164484

Although deep learning models have achieved remarkable success in medical image segmentation, the domain shift issue caused mainly by the highly variable quality of medical images is a major hurdle that prevents these models from being deployed for real clinical practices, since no one can predict the performance of a 'well-trained' model on a set of unseen clinical data. Previously, many methods have been proposed based on, for instance, CycleGAN or the Fourier transform to address this issue, which, however, suffer from either an inadequate ability to preserve anatomical structures or unexpectedly introduced artifacts. In this paper, we propose a multi-source-domain unsupervised domain adaptation (UDA) method called Domain specific Convolution and high frequency Reconstruction (DoCR) for medical image segmentation. We design an auxiliary high frequency reconstruction (HFR) task to facilitate UDA, and hence avoid the interference of the artifacts generated by the low-frequency component replacement. We also construct the domain specific convolution (DSC) module to boost the segmentation model's ability to domain-invariant features extraction. We evaluate DoCR on a benchmark fundus image dataset. Our results indicate that the proposed DoCR achieves superior performance over other UDA methods in multi-domain joint optic cup and optic disc segmentation. Code is available at: https://***/ShishuaiHu/DoCR.

关键词： Unsupervised domain adaptation medical image segmentation Domain specific convolution High frequency reconstruction

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Unsupervised Domain Adaptive Fundus image Segmentation with Category-Level Regularization 25th

Unsupervised Domain Adaptive Fundus Image Segmentation with ...

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

作者： Feng, Wei Wang, Lin Ju, Lie Zhao, Xin Wang, Xin Shi, Xiaoyu Ge, Zongyuan Monash Univ Monash Med AI Grp Melbourne Australia Monash Univ Airdoc Monash Res Ctr Clayton Vic Australia Monash Univ Monash eResearch Ctr Clayton Vic Australia Airdoc LLC Beijing Peoples R China

ISBN: (纸本)9783031164347;9783031164330

Existing unsupervised domain adaptation methods based on adversarial learning have achieved good performance in several medical imaging tasks. However, these methods focus only on global distribution adaptation and ignore distribution constraints at the category level, which would lead to sub-optimal adaptation performance. this paper presents an unsupervised domain adaptation framework based on category-level regularization that regularizes the category distribution from three perspectives. Specifically, for inter-domain category regularization, an adaptive prototype alignment module is proposed to align feature prototypes of the same category in the source and target domains. In addition, for intra-domain category regularization, we tailored a regularization technique for the source and target domains, respectively. In the source domain, a prototype-guided discriminative loss is proposed to learn more discriminative feature representations by enforcing intra-class compactness and inter-class separability, and as a complement to traditional supervised loss. In the target domain, an augmented consistency category regularization loss is proposed to force the model to produce consistent predictions for augmented/unaugmented target images, which encourages semantically similar regions to be given the same label. Extensive experiments on two publicly fundus datasets show that the proposed approach significantly outperforms other state-of-theart comparison algorithms' (Our code is available at https://***/ fengweie/UDA_CLR.).

关键词： Unsupervised domain adaptation Category level regularization Fundus image segmentation

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Self-learning and One-Shot Learning Based Single-Slice Annotation for 3D medical image Segmentation 25th

Self-learning and One-Shot Learning Based Single-Slice Annot...

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

作者： Wu, Yixuan Zheng, Bo Chen, Jintai Chen, Danny Z. Wu, Jian Zhejiang Univ Sch Med Hangzhou Peoples R China Zhejiang Univ Polytech Inst Hangzhou Peoples R China Zhejiang Univ Coll Comp Sci & Technol Hangzhou Peoples R China Univ Notre Dame Dept Comp Sci & Engn Notre Dame IN USA Zhejiang Univ Affiliated Hosp 2 Sch Med Sch Publ Hlth Hangzhou Peoples R China Zhejiang Univ Inst Wenzhou Hangzhou Peoples R China

ISBN: (纸本)9783031164521;9783031164514

As deep learning methods continue to improve medical image segmentation performance, data annotation is still a big bottleneck due to the labor-intensive and time-consuming burden on medical experts, especially for 3D images. To significantly reduce annotation efforts while attaining competitive segmentation accuracy, we propose a self-learning and one-shot learning based framework for 3D medical image segmentation by annotating only one slice of each 3D image. Our approach takes two steps: (1) self-learning of a reconstruction network to learn semantic correspondence among 2D slices within 3D images, and (2) representative selection of single slices for one-shot manual annotation and propagating the annotated data with the well-trained reconstruction network. Extensive experiments verify that our new framework achieves comparable performance with less than 1% annotated data compared with fully supervised methods and generalizes well on several out-of-distribution testing sets.

关键词： 3D medical image segmentation Sparse annotation Self-learning One-shot learning

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Surgical Scene Segmentation Using Semantic image Synthesis with a Virtual Surgery Environment 25th

Surgical Scene Segmentation Using Semantic Image Synthesis w...

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

作者： Yoon, Jihun Hong, SeulGi Hong, Seungbum Lee, Jiwon Shin, Soyeon Park, Bokyung Sung, Nakjun Yu, Hayeong Kim, Sungjae Park, SungHyun Hyung, Woo Jin Choi, Min-Kook Hutom Seoul South Korea Yonsei Univ Dept Surg Coll Med Seoul South Korea

ISBN: (纸本)9783031164491;9783031164484

the previous image synthesis research for surgical vision had limited results for real-world applications with simple simulators, including only a few organs and surgical tools and outdated segmentation models to evaluate the quality of the image. Furthermore, none of the research released complete datasets to the public enabling the open research. therefore, we release a new dataset to encourage further study and provide novel methods with extensive experiments for surgical scene segmentation using semantic image synthesis with a more complex virtual surgery environment. First, we created three cross-validation sets of real image data considering demographic and clinical information from 40 cases of real surgical videos of gastrectomy with the da Vinci Surgical System (dVSS). Second, we created a virtual surgery environment in the Unity engine with five organs from real patient CT data and 22 the da Vinci surgical instruments from actual measurements. third, We converted this environment photo-realistically with representative semantic image synthesis models, SEAN and SPADE. Lastly, we evaluated it with various state-of-the-art instance and semantic segmentation models. We succeeded in highly improving our segmentation models with the help of synthetic training data. More methods, statistics, and visualizations on https://***/.

关键词： Surgical instrument localization Class imbalance Domain randomization Synthetic data Semantic image snythesis

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A Self-attentive Meta-learning Approach for image-Based Few-Shot Disease Detection 1

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1st international Workshop on Resource-Efficient medical image Analysis (REMIA) / 25th international conference on medical image computing and computer-assisted intervention (MICCAI)

作者： Ouahab, Achraf Ben-Ahmed, Olfa Fernandez-Maloigne, Christine Univ Poitiers XLIM Res Inst URM CNRS 7252 Poitiers France

ISBN: (数字)9783031168765

ISBN: (纸本)9783031168765;9783031168758

In this paper, we propose a few-shot medical images classification framework for low prevalence disease detection. the proposed method leans to transfer medical knowledge from common diseases to low prevalence cases using meta-learning. Indeed, compared to natural images, medical images vary less diversely from one image to another, with complex patterns and less semantic information. Hence, extracting clinically relevant features and learning a disease-specific signature from few images is challenging. Inspired by clinician's cognitive and visual diagnosis, we integrate an attention mechanism in the meta-learning process and we revised the meta-loss function to learn clinically disease-specific features over tasks. the proposed approach has been evaluated on two image-based diagnosis problems namely low prevalence skin and low prevalence thorax diseases diagnosis. We obtained respectively for those two use-cases 84.3% and 73.4% of average AUC in 2-way 5-shot classification setting. Obtained results demonstrate the effectiveness of the proposed framework compared to baselines and state-of the-art few-shot disease detection methods.

关键词： Few-Shot Learning Meta-learning Visual attention Rare/Low prevalence disease diagnosis

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Hand Hygiene Quality Assessment Using image-to-image Translation 25th

Hand Hygiene Quality Assessment Using Image-to-Image Transla...

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

作者： Wang, Chaofan Yang, Kangning Jiang, Weiwei Wei, Jing Sarsenbayeva, Zhanna Goncalves, Jorge Kostakos, Vassilis Univ Melbourne Sch Comp & Informat Syst Melbourne Vic Australia

ISBN: (纸本)9783031164491;9783031164484

Hand hygiene can reduce the transmission of pathogens and prevent healthcare-associated infections. Ultraviolet (UV) test is an effective tool for evaluating and visualizing hand hygiene quality during medical training. However, due to various hand shapes, sizes, and positions, systematic documentation of the UV test results to summarize frequently untreated areas and validate hand hygiene technique effectiveness is challenging. Previous studies often summarize errors within predefined hand regions, but this only provides low-resolution estimations of hand hygiene quality. Alternatively, previous studies manually translate errors to hand templates, but this lacks standardized observational practices. In this paper, we propose a novel automatic image-to-image translation framework to evaluate hand hygiene quality and document the results in a standardized manner. the framework consists of two models, including an Attention U-Net model to segment hands from the background and simultaneously classify skin surfaces covered with hand disinfectants, and a U-Net-based generator to translate the segmented hands to hand templates. Moreover, due to the lack of publicly available datasets, we conducted a lab study to collect 1218 valid UV test images containing different skin coverage with hand disinfectants. the proposed framework was then evaluated on the collected dataset through five-fold cross-validation. Experimental results show that the proposed framework can accurately assess hand hygiene quality and document UV test results in a standardized manner. the benefit of our work is that it enables systematic documentation of hand hygiene practices, which in turn enables clearer communication and comparisons.

关键词： Hand hygiene Handrub Six-step hand hygiene technique Healthcare-associated infections Nosocomial infections

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A Transformer-Based Iterative Reconstruction Model for Sparse-View CT Reconstruction 25th

A Transformer-Based Iterative Reconstruction Model for Spars...

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

作者： Xia, Wenjun Yang, Ziyuan Zhou, Qizheng Lu, Zexin Wang, Zhongxian Zhang, Yi Sichuan Univ Chengdu 610065 Peoples R China SUNY Stony Brook New York NY 11794 USA

ISBN: (纸本)9783031164460;9783031164453

Sparse-view computed tomography (CT) is one of the primary means to reduce the radiation risk. But the reconstruction of sparse-view CT will be contaminated by severe artifacts. By carefully designing the regularization terms, the iterative reconstruction (IR) algorithm can achieve promising results. With the introduction of deep learning techniques, learned regularization terms with convolution neural network (CNN) attracts much attention and can further improve the performance. In this paper, we propose a learned local-nonlocal regularization-based model called RegFormer to reconstruct CT images. Specifically, we unroll the iterative scheme into a neural network and replace handcrafted regularization terms with learnable kernels. the convolution layers are used to learn local regularization with excellent denoising performance. Simultaneously, transformer encoders and decoders incorporate the learned nonlocal prior into the model, preserving the structures and details. To improve the ability to extract deep features during iteration, we introduce an iteration transmission (IT) module, which can further promote the efficiency of each iteration. the experimental results show that our proposed RegFormer achieves competitive performance in artifact reduction and detail preservation compared to some state-of-the-art sparse-view CT reconstruction methods.

关键词： Computed tomography image reconstruction Deep learning Nonlocal regularization Transformer

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Noise2SR: Learning to Denoise from Super-Resolved Single Noisy Fluorescence image 25th

Noise2SR: Learning to Denoise from Super-Resolved Single Noi...

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

作者： Tian, Xuanyu Wu, Qing Wei, Hongjiang Zhang, Yuyao ShanghaiTech Univ Sch Informat Sci & Technol Shanghai Peoples R China Shanghai Jiao Tong Univ Sch Biomed Engn Shanghai Peoples R China ShanghaiTech Univ Shanghai Engn Res Ctr Intelligent Vis & Imaging Shanghai Peoples R China

ISBN: (纸本)9783031164460;9783031164453

Fluorescence microscopy is a key driver to promote discoveries of biomedical research. However, with the limitation of microscope hardware and characteristics of the observed samples, the fluorescence microscopy images are susceptible to noise. Recently, a few self-supervised deep learning (DL) denoising methods have been proposed. However, the training efficiency and denoising performance of existing methods are relatively low in real scene noise removal. To address this issue, this paper proposed self-supervised image denoising method Noise2SR (N2SR) to train a simple and effective image denoising model based on single noisy observation. Our Noise2SR denoising model is designed for training with paired noisy images of different dimensions. Benefiting from this training strategy, Noise2SR is more efficiently self-supervised and able to restore more image details from a single noisy observation. Experimental results of simulated noise and real microscopy noise removal show that Noise2SR outperforms two blind-spot based self-supervised deep learning image denoising methods. We envision that Noise2SR has the potential to improve more other kind of scientific imaging quality.

关键词： image denoising Self-supervised learning Fluorescence microscopy image

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