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

作者： Guo, Xiaoqing Yuan, Yixuan City Univ Hong Kong Dept Elect Engn Kowloon Tong Hong Kong Peoples R China

ISBN: (纸本)9783031164408;9783031164392

Noisy labels collected with limited annotation cost prevent medical image segmentation algorithms from learning precise semantic correlations. Previous segmentation arts of learning with noisy labels merely perform a pixel-wise manner to preserve semantics, such as pixel-wise label correction, but neglect the pair-wise manner. In fact, we observe that the pair-wise manner capturing affinity relations between pixels can greatly reduce the label noise rate. Motivated by this observation, we present a novel perspective for noisy mitigation by incorporating both pixel-wise and pair-wise manners, where supervisions are derived from noisy class and affinity labels, respectively. Unifying the pixel-wise and pair-wise manners, we propose a robust Joint Class-Affinity Segmentation (JCAS) framework to combat label noise issues in medical image segmentation. Considering the affinity in pair-wise manner incorporates contextual dependencies, a differentiated affinity reasoning (DAR) module is devised to rectify the pixel-wise segmentation prediction by reasoning about intra-class and inter-class affinity relations. To further enhance the noise resistance, a class-affinity loss correction (CALC) strategy is designed to correct supervision signals via the modeled noise label distributions in class and affinity labels. Meanwhile, CALC strategy interacts the pixel-wise and pair-wise manners through the theoretically derived consistency regularization. Extensive experiments under both synthetic and real-world noisy labels corroborate the efficacy of the proposed JCAS framework with a minimum gap towards the upper bound performance. the source code is available at https://***/CityU-AIM-Group/JCAS.

关键词： Class and affinity Loss correction Noisy label

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An Efficient Defending Mechanism Against image Attacking on medical image Segmentation Models 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： Le, Linh D. Fu, Huazhu Xu, Xinxing Liu, Yong Xu, Yanyu Du, Jiawei Zhou, Joey T. Goh, Rick ASTAR Inst High Performance Comp Singapore Singapore

ISBN: (数字)9783031168765

ISBN: (纸本)9783031168765;9783031168758

image attacking has been studied for a long time. However, in reality, the number of research on defending against the attacks on segmentation models is still limited especially for medical imaging. To fill this research gap, we propose a novel defending mechanism against adversarial attacks for the segmentation models. We focus on segmentation as robustness improvement on segmentation is much more challenging due to its dense nature, and segmentation is at the center of medical imaging tasks. In this paper, we are the first time to employ Transformer as a technique to protect the segmentation models from attacks. Our result on several medical well-known benchmark datasets shows that the proposed defending mechanism to enhance the segmentation models is effective with high scores and better compared to other strong methods.

关键词： Defending Adversarial attacking medical image segmentation Transformer

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Exploring Smoothness and Class-Separation for Semi-supervised medical image Segmentation 25th

Exploring Smoothness and Class-Separation for Semi-supervise...

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

作者： Wu, Yicheng Wu, Zhonghua Wu, Qianyi Ge, Zongyuan Cai, Jianfei Monash Univ Fac Informat Technol Dept Data Sci & AI Melbourne Vic 3800 Australia Nanyang Technol Univ Sch Comp Sci & Engn Singapore 639798 Singapore Monash Univ Monash Airdoc Res Melbourne Vic 3800 Australia Monash eRes Ctr Monash Med AI Melbourne Vic 3800 Australia

ISBN: (纸本)9783031164439;9783031164422

Semi-supervised segmentation remains challenging in medical imaging since the amount of annotated medical data is often scarce and there are many blurred pixels near the adhesive edges or in the low-contrast regions. To address the issues, we advocate to firstly constrain the consistency of pixels with and without strong perturbations to apply a sufficient smoothness constraint and further encourage the class-level separation to exploit the low-entropy regularization for the model training Particularly, in this paper, we propose the SS-Net for semi-supervised medical image segmentation tasks, via exploring the pixel-level Smoothness and inter-class Separation at the same time. the pixel-level smoothness forces the model to generate invariant results under adversarial perturbations. Meanwhile, the inter-class separation encourages individual class features should approach their corresponding high-quality prototypes, in order to make each class distribution compact and separate different classes. We evaluated our SS-Net against five recent methods on the public LA and ACDC datasets. Extensive experimental results under two semi-supervised settings demonstrate the superiority of our proposed SS-Net model, achieving new state-of-the-art (SOTA) performance on both datasets.

关键词： Semi-supervised segmentation Pixel-level smoothness Inter-class separation

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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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Diabetic Foot Ulcer Grand Challenge 2024: Overview and Baseline Methods 4th

Diabetic Foot Ulcer Grand Challenge 2024: Overview and Base...

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4th Diabetic Foot Ulcers Grand Challenge, DFUC 2024 held in Conjunction 27th international conference on medical image computing and computer assisted intervention, MICCAI 2024

作者： Brüngel, Raphael Kendrick, Connah Cassidy, Bill Bracke, Benjamin Friedrich, Christoph M. Reeves, Neil D. Pappachan, Joseph M. Yap, Moi Hoon Dortmund44227 Germany University Hospital Essen Essen45130 Germany University Hospital Essen Essen45131 Germany Department of Computing and Mathematics Manchester Metropolitan University ManchesterM1 5GD United Kingdom Lancaster University LancasterLA1 4YW United Kingdom Lancashire Teaching Hospitals NHS Foundation Trust PrestonPR2 9HT United Kingdom

ISBN: (纸本)9783031808708

In its 4th edition, the Diabetic Foot Ulcer Challenge (DFUC) 2024 focuses on the task of Diabetic Foot Ulcer (DFU) segmentation by involving aspects of self-supervised learning as contrast to the DFUC 2022. this paper provides information on the challenge background, outlines details on an synthetic extension of the DFU dataset, elaborates rules for participation, and reports performance of two vastly different baseline methods that highlight the potential diversity of welcome approaches. It further provides a condensed summary on final challenge results and methods employed by participating teams. A discussion puts the different approaches and outcomes into perspective, considering their creativity and applicability on a larger scale. Eventually, a retrospective on the DFUC 2024 finds the given task to be considerably challenging, inspiring diverse contributions. Even though accurate DFU segmentation currently seems out of reach when involving a higher level of self-supervision, respective contributions demonstrate promising kick-off strategies for scenarios in which unlabelled data needs to be harnessed. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Self-supervised learning

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Denoising Diffusion Models for Inpainting of Healthy Brain Tissue 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： Durrer, Alicia Cattin, Philippe C. Wolleb, Julia Center for Medical Image Analysis and Navigation Department of Biomedical Engineering University of Basel Allschwil Switzerland

ISBN: (数字)9783031761638

ISBN: (纸本)9783031761621

this paper is a contribution to the "BraTS 2023 Local Synthesis of Healthy Brain Tissue via Inpainting Challenge". the task of this challenge is to transform tumor tissue into healthy tissue in brain magnetic resonance (MR) images. this idea originates from the problem that MR images can be evaluated using automatic processing tools, however, many of these tools are optimized for the analysis of healthy tissue. By solving the given inpainting task, we enable the automatic analysis of images featuring lesions, and further downstream tasks. Our approach builds on denoising diffusion probabilistic models. We use a 2D model that is trained using slices in which healthy tissue was cropped out and is learned to be inpainted again. this allows us to use the ground truth healthy tissue during training. In the sampling stage, we replace the slices containing diseased tissue in the original 3D volume with the slices containing the healthy tissue inpainting. With our approach, we achieve the second place in the challenge. On the test set our model achieves a mean SSIM of 0.8271, a PSNR of 20.4949 and a MSE of 0.0115. In future we plan to extend our 2D model to a 3D model, allowing to inpaint the region of interest as a whole without losing context information of neighboring slices. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Magnetic resonance imaging

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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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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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PSFHS challenge report: Pubic symphysis and fetal head segmentation from intrapartum ultrasound images

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medical image ANALYSIS 2025年 99卷 103353页

作者： Bai, Jieyun Zhou, Zihao Ou, Zhanhong Koehler, Gregor Stock, Raphael Maier-Hein, Klaus Elbatel, Marawan Marti, Robert Li, Xiaomeng Qiu, Yaoyang Gou, Panjie Chen, Gongping Zhao, Lei Zhang, Jianxun Dai, Yu Wang, Fangyijie Silvestre, Guenole Curran, Kathleen Sun, Hongkun Xu, Jing Cai, Pengzhou Jiang, Lu Lan, Libin Ni, Dong Zhong, Mei Chen, Gaowen Campello, Victor M. Lu, Yaosheng Lekadir, Karim Jinan Univ Guangdong Prov Key Lab Tradit Chinese Med Informat Guangzhou Peoples R China Univ Auckland Auckland Bioengn Inst Private Bag 92019 Auckland 1142 New Zealand German Canc Res Ctr Div Med Image Comp Heidelberg Germany Hong Kong Univ Sci & Technol Dept Elect & Comp Engn Hong Kong Peoples R China Univ Girona Comp Vis & Robot Grp Girona Spain Canon Med Syst China Co Ltd Beijing Peoples R China Nankai Univ Coll Artificial Intelligence Tianjin Peoples R China Hunan Univ Coll Comp Sci & Elect Engn Changsha Peoples R China Univ Coll Dublin Sch Med Dublin Ireland Univ Coll Dublin Sch Comp Sci Dublin Ireland Zhejiang Gongshang Univ Sch Stat & Math Hangzhou Peoples R China Chongqing Univ Technol Sch Comp Sci & Engn Chongqing Peoples R China Shenzhen Univ Hlth Sci Ctr Natl Reg Key Technol Engn Lab Med Ultrasound Shenzhen Peoples R China Shenzhen Univ Hlth Sci Ctr Guangdong Prov Key Lab Biomed Measurements & Ultra Shenzhen Peoples R China Shenzhen Univ Hlth Sci Ctr Sch Biomed Engn Shenzhen Peoples R China Southern Med Univ NanFang Hosp Guangzhou Peoples R China Southern Med Univ Zhujiang Hosp Guangzhou Peoples R China Univ Barcelona Dept Matemat & Informat Barcelona Spain INst Catalana Recerca & Estudis Avancats ICREA Barcelona Spain

Segmentation of the fetal and maternal structures, particularly intrapartum ultrasound imaging as advocated by the international Society of Ultrasound in Obstetrics and Gynecology (ISUOG) for monitoring labor progression, is a crucial first step for quantitative diagnosis and clinical decision-making. this requires specialized analysis by obstetrics professionals, in a task that i) is highly time- and cost-consuming and ii) often yields inconsistent results. the utility of automatic segmentation algorithms for biometry has been proven, though existing results remain suboptimal. To push forward advancements in this area, the Grand Challenge on Pubic Symphysis-Fetal Head Segmentation (PSFHS) was held alongside the 26th international conference on medical image computing and computer assisted intervention (MICCAI 2023). this challenge aimed to enhance the development of automatic segmentation algorithms at an international scale, providing the largest dataset to date with 5,101 intrapartum ultrasound images collected from two ultrasound machines across three hospitals from two institutions. the scientific community's enthusiastic participation led to the selection of the top 8 out of 179 entries from 193 registrants in the initial phase to proceed to the competition's second stage. these algorithms have elevated the state-of-the-art in automatic PSFHS from intrapartum ultrasound images. A thorough analysis of the results pinpointed ongoing challenges in the field and outlined recommendations for future work. the top solutions and the complete dataset remain publicly available, fostering further advancements in automatic segmentation and biometry for intrapartum ultrasound imaging.

关键词： Intrapartum ultrasound Fetal biometry Deep learning Challenge Angle of progress image segmentation

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Reinforcement Learning Driven Intra-modal and Inter-modal Representation Learning for 3D medical image Classification 1

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

作者： Zhu, Zhonghang Wang, Liansheng Magnier, Baptiste Zhu, Lei Zhang, Defu Yu, Lequan Xiamen Univ Sch Informat Dept Comp Sci Xiamen Peoples R China Univ Montpellier IMT Mines Ales Euromov Digital Hlth Motion Ales France Hong Kong Univ Sci & Technol Guangzhou Guangzhou Peoples R China Hong Kong Univ Sci & Technol Hong Kong Peoples R China Univ Hong Kong Dept Stat & Actuarial Sci Hong Kong Peoples R China

ISBN: (数字)9783031164378

ISBN: (纸本)9783031164378;9783031164361

Multi-modality 3D medical images play an important role in the clinical practice. Due to the effectiveness of exploring the complementary information among different modalities, multi-modality learning has attracted increased attention recently, which can be realized by Deep Learning (DL) models. However, it remains a challenging task for two reasons. First, the prediction confidence of multi-modality learning network cannot be guaranteed when the model is trained with weakly-supervised volume-level labels. Second, it is difficult to effectively exploit the complementary information across modalities and also preserve the modality-specific properties when fusion. In this paper, we present a novel Reinforcement Learning (RL) driven approach to comprehensively address these challenges, where two Recurrent Neural Networks (RNN) based agents are utilized to choose reliable and informative features within modality (intra-learning) and explore complementary representations across modalities (inter-learning) with the guidance of dynamic weights. these agents are trained via Proximal Policy Optimization (PPO) with the confidence increment of the prediction as the reward. We take the 3D image classification as an example and conduct experiments on a multi-modality brain tumor MRI data. Our approach outperforms other methods when employing the proposed RL-based multi-modality representation learning.

关键词： Multi-modality learning 3D medical images Reinforcement learning Classification

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