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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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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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A Novel Knowledge Keeper Network for 7T-Free but 7T-Guided Brain Tissue Segmentation 25th

A Novel Knowledge Keeper Network for 7T-Free but 7T-Guided B...

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

作者： Lee, Jieun Oh, Kwanseok Shen, Dinggang Suk, Heung-Il Korea Univ Dept Artificial Intelligence Seoul South Korea Korea Univ Dept Brain & Cognit Engn Seoul South Korea ShanghaiTech Univ Sch Biomed Engn Shanghai Peoples R China Shanghai United Imaging Intelligence Co Ltd Shanghai Peoples R China

ISBN: (纸本)9783031164439;9783031164422

An increase in signal-to-noise ratio (SNR) and susceptibility-induced contrast at higher field strengths, e.g., 7T, is crucial for medical image analysis by providing better insights for the pathophysiology, diagnosis, and treatment of several disease entities. However, it is difficult to obtain 7T images in real clinical practices due to the high cost and low accessibility. In this paper, we propose a novel knowledge keeper network (KKN) to guide brain tissue segmentation by taking advantage of 7T representations without explicitly using 7T images. By extracting features of a 3T input image substantially and then transforming them to 7T features via knowledge distillation (KD), our method achieves deriving 7T-like representations from a given 3T image and exploits them for tissue segmentation. On two independent datasets, we evaluated our method's validity in qualitative and quantitative manners on 7T-like image synthesis and 7T-guided tissue segmentation by comparing with the comparative methods in the literature.

关键词： Brain tissue segmentation Knowledge distillation medical image synthesis 7T MRI

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Non-iterative Coarse-to-Fine Registration Based on Single-Pass Deep Cumulative Learning 25th

Non-iterative Coarse-to-Fine Registration Based on Single-Pa...

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

作者： Meng, Mingyuan Bi, Lei Feng, Dagan Kim, Jinman Univ Sydney Sch Comp Sci Sydney Australia Shanghai Jiao Tong Univ Med X Res Inst Shanghai Peoples R China

ISBN: (纸本)9783031164460;9783031164453

Deformable image registration is a crucial step in medical image analysis for finding a non-linear spatial transformation between a pair of fixed and moving images. Deep registration methods based on Convolutional Neural Networks (CNNs) have been widely used as they can perform image registration in a fast and end-to-end manner. However, these methods usually have limited performance for image pairs with large deformations. Recently, iterative deep registration methods have been used to alleviate this limitation, where the transformations are iteratively learned in a coarse-to-fine manner. However, iterative methods inevitably prolong the registration runtime, and tend to learn separate image features for each iteration, which hinders the features from being leveraged to facilitate the registration at later iterations. In this study, we propose a Non-Iterative Coarse-to-finE registration Network (NICE-Net) for deformable image registration. In the NICE-Net, we propose: (i) a Single-pass Deep Cumulative Learning (SDCL) decoder that can cumulatively learn coarse-to-fine transformations within a single pass (iteration) of the network, and (ii) a Selectively-propagated Feature Learning (SFL) encoder that can learn common image features for the whole coarse-to-fine registration process and selectively propagate the features as needed. Extensive experiments on six public datasets of 3D brain Magnetic Resonance Imaging (MRI) show that our proposed NICE-Net can outperform state-of-the-art iterative deep registration methods while only requiring similar runtime to non-iterative methods.

关键词： image registration Cumulative learning Large deformations

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Self-supervised Depth Estimation in Laparoscopic image Using 3D Geometric Consistency 25th

Self-supervised Depth Estimation in Laparoscopic Image Using...

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

作者： Huang, Baoru Zheng, Jian-Qing Nguyen, Anh Xu, Chi Gkouzionis, Ioannis Vyas, Kunal Tuch, David Giannarou, Stamatia Elson, Daniel S. Imperial Coll London Hamlyn Ctr Robot Surg London England Imperial Coll London Dept Surg & Canc London England Univ Oxford Kennedy Inst Rheumatol Oxford England Univ Oxford Big Data Inst Oxford England Univ Liverpool Dept Comp Sci Liverpool Merseyside England Lightpoint Med Ltd Chesham England

ISBN: (纸本)9783031164491;9783031164484

Depth estimation is a crucial step for image-guided intervention in robotic surgery and laparoscopic imaging system. Since per-pixel depth ground truth is difficult to acquire for laparoscopic image data, it is rarely possible to apply supervised depth estimation to surgical applications. As an alternative, self-supervised methods have been introduced to train depth estimators using only synchronized stereo image pairs. However, most recent work focused on the left-right consistency in 2D and ignored valuable inherent 3D information on the object in real world coordinates, meaning that the left-right 3D geometric structural consistency is not fully utilized. To overcome this limitation, we present M3Depth, a self-supervised depth estimator to leverage 3D geometric structural information hidden in stereo pairs while keeping monocular inference. the method also removes the influence of border regions unseen in at least one of the stereo images via masking, to enhance the correspondences between left and right images in overlapping areas. Extensive experiments show that our method outperforms previous self-supervised approaches on both a public dataset and a newly acquired dataset by a large margin, indicating a good generalization across different samples and laparoscopes.

关键词： Self-supervised monocular depth estimation Laparoscopic images 3D geometric consistency

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Test-Time Adaptation with Shape Moments for image Segmentation 1

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

作者： Bateson, Mathilde Lombaert, Herve Ben Ayed, Ismail ETS Montreal Montreal PQ Canada

ISBN: (数字)9783031164408

ISBN: (纸本)9783031164408;9783031164392

Supervised learning is well-known to fail at generalization under distribution shifts. In typical clinical settings, the source data is inaccessible and the target distribution is represented with a handful of samples: adaptation can only happen at test time on a few (or even a single) subject(s). We investigate test-time single-subject adaptation for segmentation, and propose a shape-guided entropy minimization objective for tackling this task. During inference for a single testing subject, our loss is minimized with respect to the batch normalization's scale and bias parameters. We show the potential of integrating various shape priors to guide adaptation to plausible solutions, and validate our method in two challenging scenarios: MRI-to-CT adaptation of cardiac segmentation and cross-site adaptation of prostate segmentation. Our approach exhibits substantially better performances than the existing test-time adaptation methods. Even more surprisingly, it fares better than state-of-the-art domain adaptation methods, although it forgoes training on additional target data during adaptation. Our results question the usefulness of training on target data in segmentation adaptation, and points to the substantial effect of shape priors on test-time inference. Our framework can be readily used for integrating various priors and for adapting any segmentation network. the code is publicly available (https://*** mathilde-b/TTA).

关键词： Test-time adaptation Segmentation Shape moments Deep networks Entropy minimization

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