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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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Patcher: Patch Transformers with Mixture of Experts for Precise medical image Segmentation 25th

Patcher: Patch Transformers with Mixture of Experts for Prec...

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

作者： Ou, Yanglan Yuan, Ye Huang, Xiaolei Wong, Stephen T. C. Volpi, John Wang, James Z. Wong, Kelvin Penn State Univ Univ Pk State Coll PA 16801 USA Carnegie Mellon Univ Pittsburgh PA USA Houston Methodist Hosp TT & WF Chao Ctr BRAIN Houston TX USA Houston Methodist Hosp Houston Methodist Canc Ctr Houston TX USA Houston Methodist Hosp Dept Neurol Eddy Scurlock Comprehens Stroke Ctr Houston TX USA

ISBN: (纸本)9783031164439;9783031164422

We present a new encoder-decoder Vision Transformer architecture, Patcher, for medical image segmentation. Unlike standard Vision Transformers, it employs Patcher blocks that segment an image into large patches, each of which is further divided into small patches. Transformers are applied to the small patches within a large patch, which constrains the receptive field of each pixel. We intentionally make the large patches overlap to enhance intra-patch communication. the encoder employs a cascade of Patcher blocks with increasing receptive fields to extract features from local to global levels. this design allows Patcher to benefit from both the coarse-to-fine feature extraction common in CNNs and the superior spatial relationship modeling of Transformers. We also propose a new mixture-of-experts (MoE) based decoder, which treats the feature maps from the encoder as experts and selects a suitable set of expert features to predict the label for each pixel. the use of MoE enables better specializations of the expert features and reduces interference between them during inference. Extensive experiments demonstrate that Patcher outperforms state-of-the-art Transformer- and CNN-based approaches significantly on stroke lesion segmentation and polyp segmentation. Code for Patcher is released to facilitate related research. (Code: https: ***/ YanglanOu/***.).

关键词： medical image segmentation Vision transformers Mixture of experts

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Cell Segmentation Algorithm of Fully Connected U-Net ++ Network 5

Cell Segmentation Algorithm of Fully Connected U-Net ++ Netw...

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5th international conference on Machine Learning, Big Data and Business Intelligence, MLBDBI 2023

作者： Yuan, Shuping Mariano, Vladimir Y. National University College of Computing and Information Technologies Manila Philippines AnHui XinHua University School of Big Data and Artificial Intelligence Hefei China

ISBN: (纸本)9798350359930

With the continuous emergence and wide application of new medical imaging technologies, doctors can easily use them to provide key and reliable information for the diagnosis of lesions in real time. At the same time, with the continuous development of computer vision, deep learning has derived many model structures in the field of medical image segmentation. Among them, U-net network structure has become one of the important segmentation models in the field of medical image because of its clever skip connection, which can retain complete features while locating clear boundaries. Algorithms based on deep learning often rely on the training of massive data sets. In the field of cell images, dataset calibration is very difficult. Especially in multi-cell images, the position and boundary of nucleus and cytoplasm are ambiguous, and can not be correctly calibrated even by a large amount of manpower. And from the perspective of imaging principles, medical image data is complex and not informative enough. Based on the comparison between the improved fully connected U-net++ network and FCN, SegNet, U-Net and other network models, this paper proposes a data augmentation method for the selection and splicing of cell sample data from the perspective of data sampling and augmentation, which can effectively transform the weak supervised data containing part of the label into strong supervised data for training. © 2023 IEEE.

关键词： Cytology

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Evidence Fusion with Contextual Discounting for Multi-modality medical image Segmentation 25th

Evidence Fusion with Contextual Discounting for Multi-modali...

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

作者： Huang, Ling Denoeux, thierry Vera, Pierre Ruan, Su Univ Technol Compiegne CNRS Heudiasyc Compiegne France Inst Univ France Paris France Henri Becquerel Canc Ctr Dept Nucl Med Rouen France Univ Rouen Normandy Quantif LITIS Rouen France

ISBN: (纸本)9783031164439;9783031164422

As information sources are usually imperfect, it is necessary to take into account their reliability in multi-source information fusion tasks. In this paper, we propose a new deep framework allowing us to merge multi-MR image segmentation results using the formalism of Dempster-Shafer theory while taking into account the reliability of different modalities relative to different classes. the framework is composed of an encoder-decoder feature extraction module, an evidential segmentation module that computes a belief function at each voxel for each modality, and a multi-modality evidence fusion module, which assigns a vector of discount rates to each modality evidence and combines the discounted evidence using Dempster's rule. the whole framework is trained by minimizing a new loss function based on a discounted Dice index to increase segmentation accuracy and reliability. the method was evaluated on the BraTs 2021 database of 1251 patients with brain tumors. Quantitative and qualitative results show that our method outperforms the state of the art, and implements an effective new idea for merging multi-information within deep neural networks.

关键词： Information fusion Dempster-Shafer theory Evidence theory Uncertainty quantification Contextual discounting Deep learning Brain tumor segmentation

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Head and Neck Tumor Segmentation for MRI-Guided Radiation therapy Using Pre-trained STU-Net Models 1st

Head and Neck Tumor Segmentation for MRI-Guided Radiation Th...

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1st Challenge on Head and Neck Tumor Segmentation for MRI-Guided Applications, HNTS-MRG 2024, Held in Conjunction with 27th international conference on medical image computing and computer-assisted intervention, MICCAI 2024

作者： Wang, Zihao Lyu, Mengye College of Health Science and Environmental Engineering Shenzhen Technology University Shenzhen China College of Applied Sciences Shenzhen University Shenzhen China

ISBN: (纸本)9783031832734

Accurate segmentation of tumors in MRI-guided radiation therapy (RT) is crucial for effective treatment planning, particularly for complex malignancies such as head and neck cancer (HNC). this study presents a comparative analysis between two state-of-the-art deep learning models, nnU-Net v2 and STU-Net, for automatic tumor segmentation in pre-RT MRI images. While both models are designed for medical image segmentation, STU-Net introduces critical improvements in scalability and transferability, with parameter sizes ranging from 14 million to 1.4 billion. Leveraging large-scale pre-training on datasets such as TotalSegmentator, STU-Net captures complex and variable tumor structures more effectively. We modified the default nnU-Net v2 by adding additional convolutional layers to both the encoder and decoder, improving its performance for MRI data. Based on our experimental results, STU-Net demonstrated better performance than nnU-Net v2 in the head and neck tumor segmentation challenge. these findings suggest that integrating advanced models like STU-Net into clinical work- flows could remarkably enhance the precision of RT planning, poten- tially improving patient outcomes. Ultimately, the performance of the fine-tuned STU-Net-B model submitted for the final evaluation phase of Task 1 in this challenge achieved a DSCagg-GTVp of 0.76, a DSCagg- GTVn of 0.85, and an overall DSCagg-mean score of 0.81, securing ninth place in the Task 1 rankings. the described solution is by team SZTU- SingularMatrix for Head and Neck Tumor Segmentation for MR-Guided Applications (HNTS-MRG) 2024 challenge. Link to the trained model weights: https://***/Duskwang/Weight/releases. © the Author(s) 2025.

关键词： image segmentation

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Assessing Self-supervised xLSTM-UNet Architectures for Head and Neck Tumor Segmentation in MR-Guided Applications 1st

Assessing Self-supervised xLSTM-UNet Architectures for Head ...

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作者： Qayyum, Abdul Mazher, Moona Niederer, Steven A. National Heart and Lung Institute Faculty of Medicine Imperial College London London United Kingdom Centre for Medical Image Computing Department of Computer Science University College London London United Kingdom

ISBN: (纸本)9783031832734

Radiation therapy (RT) plays a pivotal role in treating head and neck cancer (HNC), with MRI-guided approaches offering superior soft tissue con- trast and daily adaptive capabilities that significantly enhance treatment preci- sion while minimizing side effects. To optimize MRI-guided adaptive RT for HNC, we propose a novel two-stage model for Head and Neck Tumor Segmenta- tion. In the first stage, we leverage a Self-Supervised 3D Student-Teacher Learn- ing Framework, specifically utilizing the DINOv2 architecture, to learn effec- tive representations from a limited unlabeled dataset. this approach effectively addresses the challenge posed by the scarcity of annotated data, enabling the model to generalize better in tumor identification and segmentation. In the second stage, we fine-tune an xLSTM-based UNet model that is specifically designed to capture both spatial and sequential features of tumor progression. this hybrid architecture improves segmentation accuracy by integrating temporal dependen- cies, making it particularly well-suited for MRI-guided adaptive RT planning in HNC. the model’s performance is rigorously evaluated on a diverse set of HNC cases, demonstrating significant improvements over state-of-the-art deep learning models in accurately segmenting tumor structures. Our proposed solu- tion achieved an impressive mean aggregated Dice Coefficient of 0.81 for pre- RT segments and 0.65 for mid-RT segments, underscoring its effectiveness in automated segmentation tasks. this work advances the field of HNC imaging by providing a robust, generalizable solution for automated Head and Neck Tumor Segmentation, ultimately enhancing the quality of care for patients undergo- ing RT. Our team name is DeepLearnAI (CEMRG). the code for this work is available at https://***/RespectKnowledge/SSL-based-DINOv2_Vision- LSTM_Head-and-Neck-Tumor_Segmentation. © the Author(s) 2025.

关键词： Self-supervised learning

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DeSD: Self-Supervised Learning with Deep Self-Distillation for 3D medical image Segmentation 1

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

作者： Ye, Yiwen Zhang, Jianpeng Chen, Ziyang Xia, Yong Northwestern Polytech Univ Sch Comp Sci & Engn Natl Engn Lab Integrated Aerosp Ground Ocean Big Xian 710072 Peoples R China Northwestern Polytech Univ Ningbo Inst Ningbo 315048 Peoples R China Northwestern Polytech Univ Shenzhen Inst Res & Dev Shenzhen 518057 Peoples R China

ISBN: (数字)9783031164408

ISBN: (纸本)9783031164408;9783031164392

Self-supervised learning (SSL), enabling advanced performance with few annotations, has demonstrated a proven successful in medical image segmentation. Usually, SSL relies on measuring the similarity of features obtained at the deepest layer to attract the features of positive pairs or repulse the features of negative pairs, and then may suffer from the weak supervision at shallow layers. To address this issue, we reformulate SSL in a Deep Self-Distillation (DeSD) manner to improve the representation quality of both shallow and deep layers. Specifically, the DeSD model is composed of an online student network and a momentum teacher network, both being stacked by multiple sub-encoders. the features produced by each sub-encoder in the student network are trained to match the features produced by the teacher network. Such a deep self-distillation supervision is able to improve the representation quality of all sub-encoders, including both shallow ones and deep ones. We pretrain the DeSD model on a large-scale unlabeled dataset and evaluate it on seven downstream segmentation tasks. Our results indicate that the proposed DeSD model achieves superior pre-training performance over existing SSL methods, setting the new state of the art. the code is available at https://***/yeerwen/DeSD.

关键词： Self-supervised learning Deep self-distillation medical image segmentation

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Deformer: Towards Displacement Field Learning for Unsupervised medical image Registration 25th

Deformer: Towards Displacement Field Learning for Unsupervis...

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

作者： Chen, Jiashun Lu, Donghuan Zhang, Yu Wei, Dong Ning, Munan Shi, Xinyu Xu, Zhe Zheng, Yefeng Southeast Univ Sch Comp Sci & Engn Nanjing Peoples R China Tencent Healthcare Co Jarvis Lab Shenzhen Peoples R China Chinese Univ Hong Kong Biomed Engn Hong Kong Peoples R China

ISBN: (纸本)9783031164460;9783031164453

Recently, deep-learning-based approaches have been widely studied for deformable image registration task. However, most efforts directly map the composite image representation to spatial transformation through the convolutional neural network, ignoring its limited ability to capture spatial correspondence. On the other hand, Transformer can better characterize the spatial relationship with attention mechanism, its long-range dependency may be harmful to the registration task, where voxels with too large distances are unlikely to be corresponding pairs. In this study, we propose a novel Deformer module along with a multi-scale framework for the deformable image registration task. the Deformer module is designed to facilitate the mapping from image representation to spatial transformation by formulating the displacement vector prediction as the weighted summation of several bases. With the multi-scale framework to predict the displacement fields in a coarse-to-fine manner, superior performance can be achieved compared with traditional and learning-based approaches. Comprehensive experiments on two public datasets are conducted to demonstrate the effectiveness of the proposed Deformer module as well as the multi-scale framework.

关键词： Deformable image registration Displacement bases Multi-scale framework

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ChrSNet: Chromosome Straightening Using Self-attention Guided Networks 1

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

作者： Zheng, Sunyi Li, Jingxiong Shui, Zhongyi Zhu, Chenglu Zhang, Yunlong Chen, Pingyi Yang, Lin Westlake Univ Sch Engn Artificial Intelligence & Biomed Image Anal Lab Hangzhou Peoples R China Westlake Inst Adv Study Inst Adv Technol Hangzhou Peoples R China Zhejiang Univ Coll Comp Sci & Technol Hangzhou Peoples R China

ISBN: (数字)9783031164408

ISBN: (纸本)9783031164408;9783031164392

Karyotyping is an important procedure to assess the possible existence of chromosomal abnormalities. However, because of the non-rigid nature, chromosomes are usually heavily curved in microscopic images and such deformed shapes hinder the chromosome analysis for cytogeneticists. In this paper, we present a self-attention guided framework to erase the curvature of chromosomes. the proposed framework extracts spatial information and local textures to preserve banding patterns in a regression module. With complementary information from the bent chromosome, a refinement module is designed to further improve fine details. In addition, we propose two dedicated geometric constraints to maintain the length and restore the distortion of chromosomes. To train our framework, we create a synthetic dataset where curved chromosomes are generated from the real-world straight chromosomes by grid-deformation. Quantitative and qualitative experiments are conducted on synthetic and real-world data. Experimental results show that our proposed method can effectively straighten bent chromosomes while keeping banding details and length.

关键词： Karyotyping Chromosome straightening Self-attention Microscopy

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UMamba Adjustment: Advancing GTV Segmentation for Head and Neck Cancer in MRI-Guided RT with UMamba and NnU-Net ResEnc Planner 1st

UMamba Adjustment: Advancing GTV Segmentation for Head and N...

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作者： Ren, Jintao Hochreuter, Kim Kallehauge, Jesper Folsted Korreman, Stine Sofia Department of Clinical Medicine Aarhus University Nordre Pall e Juul-Jensens Blvd. 11 Aarhus8200 Denmark Aarhus University Hospital Danish Centre for Particle Therapy Palle Juul-Jensens Blvd. 25 Aarhus8200 Denmark Aarhus University Department of Oncology Palle Juul-Jensens Blvd. 35 Aarhus8200 Denmark

ISBN: (纸本)9783031832734

Magnetic Resonance Imaging (MRI) plays a crucial role in MRI-guided adaptive radiotherapy for head and neck cancer (HNC) due to its superior soft-tissue contrast. However, accurately segmenting the gross tumor volume (GTV), which includes both the primary tumor (GTVp) and lymph nodes (GTVn), remains challenging. Recently, two deep learning segmentation innovations have shown great promise: UMamba, which effectively captures long-range dependencies, and the nnU-Net Residual Encoder (ResEnc), which enhances feature extraction through multistage residual blocks. In this study, we integrate these strengths into a novel approach, termed ‘UMambaAdj’. Our proposed method was evaluated on the HNTS-MRG 2024 challenge test set using pre-RT T2-weighted MRI images, achieving an aggregated Dice Similarity Coefficient (DSCagg) of 0.751 for GTVp and 0.842 for GTVn, with a mean DSCagg of 0.796. this approach demonstrates potential for more precise tumor delineation in MRI-guided adaptive radiotherapy, ultimately improving treatment outcomes for HNC patients. Team: DCPT-Stine’s group. © the Author(s) 2025.

关键词： image segmentation

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A Simple and Effective Regional Contrastive Learning Method for 3D medical images 5

A Simple and Effective Regional Contrastive Learning Method ...

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2025 5th international conference on Bioinformatics and Intelligent computing, BIC 2025

作者： Liu, Jin Liu, Jiaqi Tang, Huifang School of Computer University of South China Hunan Hengyang China The First Affiliated Hospital Department of Cardiology Hengyang Medical School University of South China Hunan Hengyang China The First Affiliated Hospital Hunan Provincial Key Laboratory of Multi-omics and Artificial Intelligence of Cardiovascular Diseases Hengyang Medical School University of South China Hunan Hengyang China

ISBN: (纸本)9798400712203

3D medical images can visualize the internal structure of human organs, which have significant advantages over 2D medical images. However, the annotations of 3D medical images are more difficult to obtain compared to 2D medical images, which makes it challenging to capture complete spatial information in 3D medical images. To address this challenge, we propose a simple but effective self-supervised learning method that utilizes the similarity between medical images for region comparison and learns the organ correspondence information from different images but between the same regions. We pre-trained our method on the UK Biobank's cardiac short-axis nuclear magnetic resonance dataset. Our method performs well on three downstream task-related datasets. © 2025 Copyright held by the owner/author(s).

关键词： Contrastive learning medical image analysis Self-supervised learning

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