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arXiv 2024年

作者： Li, Hongwei Bran Navarro, Fernando Ezhov, Ivan Bayat, Amirhossein Das, Dhritiman Kofler, Florian Shit, Suprosanna Waldmannstetter, Diana Paetzold, Johannes C. Hu, Xiaobin Wiestler, Benedikt Zimmer, Lucas Amiranashvili, Tamaz Prabhakar, Chinmay Berger, Christoph Weidner, Jonas Alonso-Basanta, Michelle Rashid, Arif Baid, Ujjwal Adel, Wesam Alis, Deniz Baheti, Bhakti Bai, Yingbin Bhat, Ishaan Cetindag, Sabri Can Chen, Wenting Cheng, Li Dutande, Prasad Dular, Lara Elattar, Mustafa A. Feng, Ming Gao, Shengbo Huisman, Henkjan Hu, Weifeng Innani, Shubham Ji, Wei Karimi, Davood Kuijf, Hugo J. Kwak, Jin Tae Le, Hoang Long Li, Xiang Lin, Huiyan Liu, Tongliang Ma, Jun Ma, Kai Ma, Ting Oksuz, Ilkay Holland, Robbie Oliveira, Arlindo L. Pal, Jimut Bahan Pei, Xuan Qiao, Maoying Saha, Anindo Selvan, Raghavendra Shen, Linlin Silva, Joao Lourenco Spiclin, Ziga Talbar, Sanjay Wang, Dadong Wang, Wei Wang, Xiong Wang, Yin Xi, Ruiling Xu, Kele Yang, Yanwu Yergin, Mert Yu, Shuang Zeng, Lingxi Zhang, YingLin Zhao, Jiachen Zheng, Yefeng Zukovec, Martin Do, Richard Becker, Anton Simpson, Amber Konukoglu, Ender Jakab, Andras Bakas, Spyridon Joskowicz, Leo Menze, Bjoern Department of Informatics Technical University of Munich Germany Athinoula A. Martinos Center for Biomedical Imaging Massachusetts General Hospital Harvard Medical School United States Department of Quantitative Biomedicine University of Zurich Switzerland University Children’s Hospital Zurich University of Zurich Switzerland Department of Radioncology and Radiation Theraphy Klinikum rechts der Isar Technical University of Munich Germany Department of Information Technology and Electrical Engineering ETH-Zurich Switzerland Department of Radiology Memorial Sloan Kettering Cancer Center New York City United States Department of Biomedical and Molecular Sciences Queen’s University Canada TranslaTUM - Central Institute for Translational Cancer Research Technical University of Munich Germany McGovern Institute Massachusetts Institute of Technology United States Institute for Diagnostic and Interventional Radiology Unveristy Zurich Hospital Switzerland BioMedIA Imperial College London United Kingdom Department of Radiation Oncology University of Pennsylvania PA United States University of Pennsylvania PA United States Department of Radiation Oncology Winship Cancer Institute of Emory University Georgia United States Nile University Cairo Egypt Department of Medical Sciences Acibadem University Istanbul Turkey Shri Guru Gobind Singhji Institute of Engineering and Technology Maharashtra Nanded India Trustworthy Machine Learning Lab University of Sydney Australia Image Sciences Institute University Medical Center Utrecht Netherlands Computer Engineering Department Istanbul Technical University Istanbul Turkey School of Computer Science Shenzhen University Shenzhen China University of Alberta United States University of Ljubljana Faculty of Electrical Engineering Ljubljana Slovenia Tongji University Shanghai China OPPO Research Institute Shanghai China School of Biological and Medical Engineering Beihang University Beijing China Harvard Medical School Boston

Uncertainty in medical image segmentation tasks, especially inter-rater variability, arising from differences in interpretations and annotations by various experts, presents a significant challenge in achieving consistent and reliable image segmentation. This variability not only reflects the inherent complexity and subjective nature of medical image interpretation but also directly impacts the development and evaluation of automated segmentation algorithms. Accurately modeling and quantifying this variability is essential for enhancing the robustness and clinical applicability of these algorithms. We report the set-up and summarize the benchmark results of the Quantification of Uncertainties in Biomedical Image Quantification Challenge (QUBIQ), which was organized in conjunction with International Conferences on Medical Image Computing and computer-Assisted Intervention (MICCAI) 2020 and 2021. The challenge focuses on the uncertainty quantification of medical image segmentation which considers the omnipresence of inter-rater variability in imaging datasets. The large collection of images with multi-rater annotations features various modalities such as MRI and CT;various organs such as the brain, prostate, kidney, and pancreas;and different image dimensions 2D-vs-3D. A total of 24 teams submitted different solutions to the problem, combining various baseline models, Bayesian neural networks, and ensemble model techniques. The obtained results indicate the importance of the ensemble models, as well as the need for further research to develop efficient 3D methods for uncertainty quantification methods in 3D segmentation tasks. © 2024, CC BY-NC-ND.

关键词： Image segmentation

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The state-of-the-art in Cardiac MRI Reconstruction: Results of the CMRxRecon Challenge in MICCAI 2023

arXiv

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arXiv 2024年

作者： Lyu, Jun Qin, Chen Wang, Shuo Wang, Fanwen Li, Yan Wang, Zi Guo, Kunyuan Ouyang, Cheng Tänzer, Michael Liu, Meng Sun, Longyu Sun, Mengting Li, Qin Shi, Zhang Hua, Sha Li, Hao Chen, Zhensen Zhang, Zhenlin Xin, Bingyu Metaxas, Dimitris N. Yiasemis, George Teuwen, Jonas Zhang, Liping Chen, Weitian Pang, Yanwei Liu, Xiaohan Razumov, Artem Dylov, Dmitry V. Dou, Quan Yan, Kang Xue, Yuyang Du, Yuning Dietlmeier, Julia Garcia-Cabrera, Carles Hemidi, Ziad Al-Haj Vogt, Nora Xu, Ziqiang Zhang, Yajing Chu, Ying-Hua Chen, Weibo Bai, Wenjia Zhuang, Xiahai Qin, Jing Wu, Lianmin Yang, Guang Qu, Xiaobo Wang, He Wang, Chengyan Psychiatry Neuroimaging Laboratory Brigham and Women’s Hospital Harvard Medical School 399 Revolution Drive BostonMA02215 United States Department of Electrical and Electronic Engineering & I-X Imperial College London United Kingdom Digital Medical Research Center School of Basic Medical Sciences Fudan University Shanghai China Department of Bioengineering & I-X Imperial College London LondonW12 7SL United Kingdom Cardiovascular Magnetic Resonance Unit Royal Brompton Hospital Guy’s and St Thomas’ NHS Foundation Trust LondonSW3 6NP United Kingdom Department of Radiology Ruijin Hospital Shanghai Jiao Tong University School of Medicine Shanghai China Department of Electronic Science Fujian Provincial Key Laboratory of Plasma and Magnetic Resonance National Institute for Data Science in Health and Medicine Institute of Artificial Intelligence Xiamen University Xiamen361102 China Department of Computing Department of Brain Sciences Imperial College London LondonSW7 2AZ United Kingdom Human Phenome Institute Fudan University 825 Zhangheng Road Pudong New District Shanghai201203 China Department of Radiology Zhongshan Hospital Fudan University Shanghai China Department of Cardiovascular Medicine Ruijin Hospital Lu Wan Branch Shanghai Jiao Tong University School of Medicine Shanghai China Institute of Science and Technology for Brain-Inspired Intelligence Fudan University Shanghai200433 China Department of Computer Science Rutgers University PiscatawayNJ08854 United States AI for Oncology Netherlands Cancer Institute Plesmanlaan 121 Amsterdam1066 CX Netherlands Department of Imaging and Interventional Radiology The Chinese University of Hong Kong Hong Kong TJK-BIIT Lab School of Electrical and Information Engineering Tianjin University Tianjin300072 China Skolkovo Institute Of Science And Technology Center for Artificial Intelligence Technology 30/1 Bolshoy blvd. Moscow121205 Russia Department of Biomedical Engineering University of Virginia

Cardiac magnetic resonance imaging (MRI) provides detailed and quantitative evaluation of the heart’s structure, function, and tissue characteristics with high-resolution spatial-temporal imaging. However, its slow imaging speed and motion artifacts are notable limitations. Undersampling reconstruction, especially data-driven algorithms, has emerged as a promising solution to accelerate scans and enhance imaging performance using highly under-sampled data. Nevertheless, the scarcity of publicly available cardiac k-space datasets and evaluation platform hinder the development of data-driven reconstruction algorithms. To address this issue, we organized the Cardiac MRI Reconstruction Challenge (CMRxRecon) in 2023, in collaboration with the 26th International Conference on Medical Image Computing and computer-Assisted Intervention (MICCAI). CMRxRecon presented an extensive k-space dataset comprising cine and mapping raw data, accompanied by detailed annotations of cardiac anatomical structures. With overwhelming participation, the challenge attracted more than 285 teams and over 600 participants. Among them, 22 teams successfully submitted Docker containers for the testing phase, with 7 teams submitted for both cine and mapping tasks. All teams use deep learning based approaches, indicating that deep learning has predominately become a promising solution for the problem. The first-place winner of both tasks utilizes the E2E-VarNet architecture as backbones. In contrast, U-Net is still the most popular backbone for both multi-coil and single-coil reconstructions. This paper provides a comprehensive overview of the challenge design, presents a summary of the submitted results, reviews the employed methods, and offers an in-depth discussion that aims to inspire future advancements in cardiac MRI reconstruction models. The summary emphasizes the effective strategies observed in Cardiac MRI reconstruction, including backbone architecture, loss function, pre-processing techn

关键词： Mapping

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GAMMA Challenge: Glaucoma grAding from Multi-Modality imAges

arXiv

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arXiv 2022年

作者： Wu, Junde Fang, Huihui Li, Fei Fu, Huazhu Lin, Fengbin Li, Jiongcheng Huang, Yue Yu, Qinji Song, Sifan Xu, Xinxing Xu, Yanyu Wang, Wensai Wang, Lingxiao Lu, Shuai Li, Huiqi Huang, Shihua Lu, Zhichao Ou, Chubin Wei, Xifei Liu, Bingyuan Kobbi, Riadh Tang, Xiaoying Lin, Li Zhou, Qiang Hu, Qiang Bogunović, Hrvoje Orlando, José Ignacio Zhang, Xiulan Xu, Yanwu State Key Laboratory of Ophthalmology Zhongshan Ophthalmic Center Sun Yat-sen University Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science Guangzhou China Intelligent Healthcare Unit Baidu Inc. Beijing China Singapore School of Informatics Xiamen University Xiamen China Shanghai Jiao Tong University Shanghai China Xi'an Jiaotong-Liverpool University Suzhou China Institute of High Performance Computing A*STAR Singapore Institute of Biomedical Engineering Chinese Academy of Medical Sciences Peking Union Medical College Tianjin China School of Medical Technology Beijing Institute of Technology Beijing China Department of Computing Hong Kong Polytechnic University Hong Kong Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen China Weizhi Medical Technology Company Suzhou China École de Technologie Supérieure Montreal Montreal Canada DIAGNOS Inc. Quebec Canada Department of Electrical and Electronic Engineering Southern University of Science and Technology Shenzhen China Department of Electrical and Electronic Engineering The University of Hong Kong Hong Kong Technology Co. Ltd. Shanghai China School of Information and Electronics Beijing Institute of Technology Beijing China Christian Doppler Lab for Artificial Intelligence in Retina Department of Ophthalmology Medical University of Vienna Austria Yatiris Group PLADEMA Institute CONICET UNICEN Tandil Argentina

Glaucoma is a chronic neuro-degenerative condition that is one of the world's leading causes of irreversible but preventable blindness. The blindness is generally caused by the lack of timely detection and treatment. Early screening is thus essential for early treatment to preserve vision and maintain life quality. Color fundus photography and Optical Coherence Tomography (OCT) are the two most cost-effective tools for glaucoma screening. Both imaging modalities have prominent biomarkers to indicate glaucoma suspects, such as the vertical cup-to-disc ratio (vCDR) on fundus images and retinal nerve fiber layer (RNFL) thickness on OCT volume. In clinical practice, it is often recommended to take both of the screenings for a more accurate and reliable diagnosis. However, although numerous algorithms are proposed based on fundus images or OCT volumes for the automated glaucoma detection, there are few methods that leverage both of the modalities to achieve the target. To fulfill the research gap, we set up the Glaucoma grAding from Multi-Modality imAges (GAMMA) Challenge to encourage the development of fundus & OCT-based glaucoma grading. The primary task of the challenge is to grade glaucoma from both the 2D fundus images and 3D OCT scanning volumes. As part of GAMMA, we have publicly released a glaucoma annotated dataset with both 2D fundus color photography and 3D OCT volumes, which is the first multi-modality dataset for machine learning based glaucoma grading. In addition, an evaluation framework is also established to evaluate the performance of the submitted methods. During the challenge, 1272 results were submitted, and finally, ten best performing teams were selected for the final stage. We analyze their results and summarize their methods in the paper. Since all the teams submitted their source code in the challenge, we conducted a detailed ablation study to verify the effectiveness of the particular modules proposed. Finally, we identify the proposed techniqu

关键词： Grading

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Multi-view graph learning by joint modeling of consistency and inconsistency

arXiv

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arXiv 2020年

作者： Liang, Youwei Huang, Dong Wang, Chang-Dong Yu, Philip S. College of Mathematics and Informatics South China Agricultural University Guangzhou China Tencent Shenzhen China College of Mathematics and Informatics South China Agricultural University Guangzhou China Pazhou Lab Guangzhou China School of Data and Computer Science Sun Yat-sen University Guangzhou China Guangdong Key Laboratory of Information Security Technology Guangzhou China Key Laboratory of Machine Intelligence and Advanced Computing Ministry of Education China Department of Computer Science University of Illinois at Chicago ChicagoIL60607 United States

—Graph learning has emerged as a promising technique for multi-view clustering due to its ability to learn a unified and robust graph from multiple views. However, existing graph learning methods mostly focus on the multi-view consistency issue, yet often neglect the inconsistency between views, which makes them vulnerable to possibly low-quality or noisy datasets. To overcome this limitation, we propose a new multi-view graph learning framework, which for the first time simultaneously and explicitly models multi-view consistency and inconsistency in a unified objective function, through which the consistent and inconsistent parts of each single-view graph as well as the unified graph that fuses the consistent parts can be iteratively learned. Though optimizing the objective function is NP-hard, we design a highly efficient optimization algorithm that can obtain an approximate solution with linear time complexity in the number of edges in the unified graph. Furthermore, our multi-view graph learning approach can be applied to both similarity graphs and dissimilarity graphs, which lead to two graph fusion-based variants in our framework. Experiments on twelve multi-view datasets have demonstrated the robustness and efficiency of the proposed approach. Copyright © 2020, The Authors. All rights reserved.

关键词： Optimization

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Effectiveness of Neurofeedback-Assisted and Conventional 6-Week Web-Based Mindfulness Interventions on Mental Health of Chinese Nursing Students: Randomized Controlled Trial

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Journal of Medical Internet Research 2025年 27卷 e71741页

作者： Jing, Shu Dai, Zhenwei Liu, Xiaoyang Yang, Xuelin Cheng, Jinglei Chen, Tianming Feng, Zihang Liu, Xin Dong, Fenghe Xin, You Han, Zhuoyan Hu, Haiyan Su, Xiaoyou Wang, Chen School of Population Medicine and Public Health Chinese Academy of Medical Sciences Peking Union Medical College Beijing China Peking University Sixth Hospital Peking University Beijing China Peking University Institute of Mental Health Peking University Beijing China National Health Commission Key Laboratory of Mental Health Peking University Beijing China National Clinical Research Center for Mental Disorders Peking University Sixth Hospital Beijing China School of Nursing Institute of Nursing Research School of Medicine Zhejiang University Hangzhou China School of Nursing Chinese Academy of Medical Sciences Peking Union Medical College Beijing China College of Computer Science and Technology Zhejiang University Hangzhou China The State Key Laboratory of Brain-machine Intelligence Zhejiang University Hangzhou China School of Translation and Interpreting Beijing Language and Culture University Beijing China Shenzhen Maternity and Child Healthcare Hospital Southern Medical University Shenzhen China State Key Laboratory of Respiratory Health and Multimorbidity Beijing China

Background: Nursing students experience disproportionately high rates of mental health challenges, underscoring the urgent need for innovative, scalable interventions. Web-based mindfulness programs, and more recently, neurofeedback-enhanced approaches, present potentially promising avenues for addressing this critical issue. Objective: This study aimed to explore the effectiveness of the neurofeedback-assisted online mindfulness intervention (NAOM) and the conventional online mindfulness intervention (COM) in reducing mental health symptoms among Chinese nursing students. Methods: A 3-armed randomized controlled trial was conducted among 147 nursing students in Beijing, China, using a 6-week web-based mindfulness program. Participants received NAOM, COM, or general mental health education across 6 weeks. Electroencephalogram and validated tools such as the Patient Health Questionnaire and the Generalized Anxiety Disorder Questionnaire were used to primarily assess symptoms of depression and anxiety at baseline, immediately after the intervention, and at 1 and 3 months after the intervention. Generalized estimating equations were used to evaluate the effects of intervention and time. Results: A total of 155 participants enrolled in the study, and 147 finished all assessments. Significant reductions in the symptoms of depression, anxiety, and fatigue were observed in the NAOM (mean difference [MD]=−3.330, Cohen d=0.926, P<.001;MD=−3.468, Cohen d=1.091, P<.001;MD=−2.620, Cohen d=0.743, P<.001, respectively) and the COM (MD=−1.875, Cohen d=0.490, P=.03;MD=−1.750, Cohen d=0.486, P=.02;MD=−2.229, Cohen d=0.629, P=.01, respectively) groups compared with the control group at postintervention assessment. Moreover, the NAOM group showed significantly better effects than the COM group in alleviating depressive symptoms (MD=−1.455;Cohen d=0.492;P=.04) and anxiety symptoms (MD=−1.718;Cohen d=0.670;P=.04) and improving the level of mindfulness (MD=−3.765;Cohen d=1.245;P<.001) at

关键词： anxiety depression neurofeedback nursing student mental health online mindfulness randomized controlled trial

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Variational quantum circuits for quantum state tomography

引用

Physical Review A 2020年第5期101卷 052316-052316页

作者： Yong Liu Dongyang Wang Shichuan Xue Anqi Huang Xiang Fu Xiaogang Qiang Ping Xu He-Liang Huang Mingtang Deng Chu Guo Xuejun Yang Junjie Wu Institute for Quantum Information & State Key Laboratory of High Performance Computing College of Computer National University of Defense Technology Changsha 410073 China National Innovation Institute of Defense Technology AMS 100071 Beijing China Henan Key Laboratory of Quantum Information and Cryptography IEU Zhengzhou 450001 China Hefei National Laboratory for Physical Sciences at Microscale and Department of Modern Physics University of Science and Technology of China Hefei Anhui 230026 China CAS Centre for Excellence and Synergetic Innovation Centre in Quantum Information and Quantum Physics University of Science and Technology of China Hefei Anhui 230026 China Quantum Intelligence Lab Supremacy Future Technologies Guangzhou 511340 China

Quantum state tomography is a key process in most quantum experiments. In this work, we employ quantum machine learning for state tomography. Given an unknown quantum state, it can be learned by maximizing the fidelity between the output of a variational quantum circuit and this state. The number of parameters of the variational quantum circuit grows linearly with the number of qubits and the circuit depth, so that only polynomial measurements are required, even for highly entangled states. After that, a subsequent classical circuit simulator is used to transform the information of the target quantum state from the variational quantum circuit into a familiar format. We demonstrate our method by performing numerical simulations for the tomography of the ground state of a one-dimensional quantum spin chain, using a variational quantum circuit simulator. Our method is suitable for near-term quantum computing platforms, and could be used for relatively large-scale quantum state tomography for experimentally relevant quantum states.

关键词： machine learning Quantum algorithms Quantum tomography

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Author Correction: A concept-based interpretable model for the diagnosis of choroid neoplasias using multimodal data

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Nature communications 2025年第1期16卷 4381页

作者： Yifan Wu Yang Liu Yue Yang Michael S Yao Wenli Yang Xuehui Shi Lihong Yang Dongjun Li Yueming Liu Shiyi Yin Chunyan Lei Meixia Zhang James C Gee Xuan Yang Wenbin Wei Shi Gu University of Pennsylvania Philadelphia PA USA. University of Electronic Science and Technology of China Chengdu China. Beijing Tongren Eye Center Beijing Tongren Hospital Capital Medical University Beijing China. Beijing Key Laboratory of Intraocular Tumor Diagnosis and Treatment Beijing Tongren Hospital Capital Medical University Beijing China. Beijing Ophthalmology and Visual Sciences Key Lab Beijing Tongren Hospital Capital Medical University Beijing China. Department of Ophthalmology and Research Laboratory of Macular Disease West China Hospital Sichuan University Chengdu China. Beijing Tongren Eye Center Beijing Tongren Hospital Capital Medical University Beijing China. yangxuan153@***. Beijing Key Laboratory of Intraocular Tumor Diagnosis and Treatment Beijing Tongren Hospital Capital Medical University Beijing China. yangxuan153@***. Beijing Ophthalmology and Visual Sciences Key Lab Beijing Tongren Hospital Capital Medical University Beijing China. yangxuan153@***. Beijing Tongren Eye Center Beijing Tongren Hospital Capital Medical University Beijing China. weiwenbintr@***. Beijing Key Laboratory of Intraocular Tumor Diagnosis and Treatment Beijing Tongren Hospital Capital Medical University Beijing China. weiwenbintr@***. Beijing Ophthalmology and Visual Sciences Key Lab Beijing Tongren Hospital Capital Medical University Beijing China. weiwenbintr@***. University of Electronic Science and Technology of China Chengdu China. gus@***. College of Computer Science and Technology Zhejiang University Hangzhou China. gus@***. State Key Laboratory of Brain Machine Intelligence Zhejiang University Hangzhou China. gus@***.

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Why is the Winner the Best?

Why is the Winner the Best?

引用

Conference on computer Vision and Pattern Recognition (CVPR)

作者： M. Eisenmann A. Reinke V. Weru M. D. Tizabi F. Isensee T. J. Adler S. Ali V. Andrearczyk M. Aubreville U. Baid S. Bakas N. Balu S. Bano J. Bernal S. Bodenstedt A. Casella V. Cheplygina M. Daum M. De Bruijne A. Depeursinge R. Dorent J. Egger D. G. Ellis S. Engelhardt M. Ganz N. Ghatwary G. Girard P. Godau A. Gupta L. Hansen K. Harada M. Heinrich N. Heller A. Hering A. Huaulmé P. Jannin A. E. Kavur O. Kodym M. Kozubek J. Li H. Li J. Ma C. Martín-Isla B. Menze A. Noble V. Oreiller N. Padoy S. Pati K. Payette T. Rädsch J. Rafael-Patiño V. Singh Bawa S. Speidel C. H. Sudre K. Van Wijnen M. Wagner D. Wei A. Yamlahi M. H. Yap C. Yuan M. Zenk A. Zia D. Zimmerer D. Aydogan B. Bhattarai L. Bloch R. Brüngel J. Cho C. Choi Q. Dou I. Ezhov C. M. Friedrich C. Fuller R. R. Gaire A. Galdran Á. García Faura M. Grammatikopoulou S. Hong M. Jahanifar I. Jang A. Kadkhodamohammadi I. Kang F. Kofler S. Kondo H. Kuijf M. Li M. Luu T. Martinčič P. Morais M. A. Naser B. Oliveira D. Owen S. Pang J. Park S. Park S. Płotka E. Puybareau N. Rajpoot K. Ryu N. Saeed A. Shephard P. Shi D. Štepec R. Subedi G. Tochon H. R. Torres H. Urien J. L. Vilaça K. A. Wahid H. Wang J. Wang L. Wang X. Wang B. Wiestler M. Wodzinski F. Xia J. Xie Z. Xiong S. Yang Y. Yang Z. Zhao K. Maier-Hein P. F. Jäger A. Kopp-Schneider L. Maier-Hein Division of Intelligent Medical Systems German Cancer Research Center (DKFZ) Heidelberg Germany Helmholtz Imaging German Cancer Research Center (DKFZ) Heidelberg Germany Faculty of Mathematics and Computer Science Heidelberg University Heidelberg Germany Division of Biostatistics German Cancer Research Center (DKFZ) Heidelberg Germany Division of Medical Image Computing German Cancer Research Center (DKFZ) Heidelberg Germany Faculty of Engineering and Physical Sciences School of Computing University of Leeds Leeds UK Institute of Informatics School of Management HES-SO Valais-Wallis University of Applied Sciences and Arts Western Switzerland Sierre Switzerland Department of Nuclear Medicine and Molecular Imaging Lausanne University Hospital Lausanne Switzerland Technische Hochschule Ingolstadt Ingolstadt Germany Center for Artificial Intelligence and Data Science for Integrated Diagnostics (AI2D) and Center for Biomedical Image Computing and Analytics (CBICA) University of Pennsylvania Philadelphia PA USA Department of Pathology and Laboratory Medicine Perelman School of Medicine University of Pennsylvania Philadelphia PA USA Department of Radiology Perelman School of Medicine University of Pennsylvania Philadelphia PA USA Department of Radiology University of Washington Seattle WA USA Department of Computer Science Wellcome/EPSRC Centre for Interventional and Surgical Sciences (WEISS) University College London London UK Universitat Autònoma de Barcelona & Computer Vision Center Barcelona Spain Division of Translational Surgical Oncology National Center for Tumor Diseases (NCT/UCC) Dresden Dresden Germany Department of Advanced Robotics Istituto Italiano di Tecnologia Italy Department of Electronics Information and Bioengineering Politecnico di Milano Milan Italy IT University of Copenhagen Copenhagen Denmark Department of General Visceral and Transplantation Surgery Heidelberg University Hospital Heidelberg Germany Department of Radiology and Nuc

International benchmarking competitions have become fundamental for the comparative performance assessment of image analysis methods. However, little attention has been given to investigating what can be learnt from these competitions. Do they really generate scientific progress? What are common and successful participation strategies? What makes a solution superior to a competing method? To address this gap in the literature, we performed a multicenter study with all 80 competitions that were conducted in the scope of IEEE ISBI 2021 and MICCAI 2021. Statistical analyses performed based on comprehensive descriptions of the submitted algorithms linked to their rank as well as the underlying participation strategies revealed common characteristics of winning solutions. These typically include the use of multi-task learning (63%) and/or multi-stage pipelines (61%), and a focus on augmentation (100%), image preprocessing (97%), data curation (79%), and post-processing (66%). The “typical” lead of a winning team is a computer scientist with a doctoral degree, five years of experience in biomedical image analysis, and four years of experience in deep learning. Two core general development strategies stood out for highly-ranked teams: the reflection of the metrics in the method design and the focus on analyzing and handling failure cases. According to the organizers, 43% of the winning algorithms exceeded the state of the art but only 11% completely solved the respective domain problem. The insights of our study could help researchers (1) improve algorithm development strategies when approaching new problems, and (2) focus on open research questions revealed by this work.

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Distinct neural networks of emotional valence and uncertainty in the human amygdala underlying different mental disorders

引用

science Bulletin 2025年

作者： Xiang, Shitong Xie, Chao Xiao, Xiao Banaschewski, Tobias Barker, Gareth J. Bokde, Arun L.W. Flor, Herta Grigis, Antoine Garavan, Hugh Gowland, Penny Heinz, Andreas Brühl, Rüdiger Martinot, Jean-Luc Martinot, Marie-Laure Paillère Penttilä, Jani Artiges, Eric Nees, Frauke Orfanos, Dimitri Papadopoulos Poustka, Luise Hohmann, Sarah Holz, Nathalie Baeuchl, Christian Smolka, Michael N. Vaidya, Nilakshi Walter, Henrik Whelan, Robert Bobou, Marina Broulidakis, John van Noort, Betteke Maria Stringaris, Argyris Zhang, Zuo Robinson, Lauren Winterer, Jeanne Zhang, Yuning King, Sinead Schmidt, Ulrike Sinclair, Julia Desrivières, Sylvane Schumann, Gunter Becker, Benjamin Robbins, Trevor W. Jia, Tianye Feng, Jianfeng Institute of Science and Technology for Brain-Inspired Intelligence Fudan University Shanghai200433 China Ministry of Education Shanghai200433 China Behavioral and Cognitive Neuroscience Center Institute of Science and Technology for Brain-Inspired Intelligence Fudan University Shanghai200433 China Ministry of Education Frontiers Center for Brain Science Fudan University Shanghai200433 China Department of Child and Adolescent Psychiatry and Psychotherapy Central Institute of Mental Health Medical Faculty Mannheim Heidelberg University Mannheim68159 Germany Department of Neuroimaging Institute of Psychiatry Psychology & Neuroscience King's College London WC2R 2LS United Kingdom Discipline of Psychiatry School of Medicine and Trinity College Institute of Neuroscience Trinity College Dublin DublinD02 PN40 Iceland Institute of Cognitive and Clinical Neuroscience Central Institute of Mental Health Medical Faculty Mannheim Heidelberg University Square J5 Mannheim68159 Germany Department of Psychology School of Social Sciences University of Mannheim Mannheim68131 Germany NeuroSpin CEA Université Paris-Saclay Gif-sur-Yvette91191 France Departments of Psychiatry and Psychology University of Vermont BurlingtonVT05405 United States Sir Peter Mansfield Imaging Centre School of Physics and Astronomy University of Nottingham University Park NottinghamNG7 2RD United Kingdom Department of Psychiatry and Psychotherapy CCM Charité – Universitätsmedizin Berlin Corporate Member of Freie Universität Berlin Humboldt-Universität zu Berlin and Berlin Institute of Health Berlin10117 Germany Berlin10587 Germany Université Paris-Saclay Université Paris Cité INSERM U1299"Trajectoires Développementales & Psychiatrie" Ecole Normale Supérieure Paris-Saclay Centre Borelli CNRS UMR9010 Gif-sur-Yvette91191 France AP-HP.Sorbonne Université Department of Child and Adolescent Psychiatry Pitié-Salpêtrière Hospital Paris75013 France Department of Social and Health Care

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Establishing dorsal-ventral patterning in human neural tube organoids with synthetic organizers

引用

Cell Stem Cell 2025年

作者： Luo, Tao Liu, Cong Cheng, Tao Zhao, Guo-Qin Huang, Ying Luan, Jing-Yun Guo, Junyu Liu, Xiang Wang, Yi-Fan Dong, Yang Xiao, Yu He, Enhui Sun, Rui-Zhen Chen, Xiuyu Chen, Jiekai Ma, Jun Megason, Sean Ji, Junfeng Xu, Peng-Fei Center of Stem Cell and Regenerative Medicine and Bone Marrow Transplantation Center of the First Affiliated Hospital Zhejiang University School of Medicine Zhejiang Hangzhou 310058 China Nanhu Brain-Computer Interface Institute Zhejiang Hangzhou 311100 China Center for Genetic Medicine the Fourth Affiliated Hospital of School of Medicine and International School of Medicine Zhejiang University Yiwu 322000 China Institute of Genetics Zhejiang University School of Medicine Zhejiang Hangzhou 310058 China Women's Hospital Zhejiang University School of Medicine Zhejiang Hangzhou 310006 China Precision Medicine Research Programme Yong Loo Lin School of Medicine National University of Singapore Singapore 117599 Singapore College of Computer Science and Technology Zhejiang University Hangzhou China The State Key Laboratory of Brain-Machine Intelligence Zhejiang University Hangzhou China Department of Histology and Embryology Harbin Medical University Heilongjiang Harbin 150081 China Center for Biomedical Digital Science Guangdong Provincial Key Laboratory of Stem Cell and Regenerative Medicine Guangzhou Institutes of Biomedicine and Health Chinese Academy of Sciences Guangzhou 510530 China Department of Systems Biology Harvard Medical School 200 Longwood Avenue Boston 02115 MA United States

Precise dorsal-ventral (D-V) patterning of the neural tube (NT) is essential for the development and function of the central nervous system. However, existing models for studying NT D-V patterning and related human diseases remain inadequate. Here, we present organizers derived from pluripotent stem cell aggregate fusion (“ORDER”), a method that establishes opposing BMP and SHH gradients within neural ectodermal cell aggregates. Using this approach, we generated NT organoids with ordered D-V patterning from both zebrafish and human pluripotent stem cells (hPSCs). Single-cell transcriptomic analysis revealed that the synthetic human NT organoids (hNTOs) closely resemble the human embryonic spinal cord at Carnegie stage 12 (CS12) and exhibit greater similarity to human NT than to mouse models. Furthermore, using the hNTO model, we demonstrated the critical role of WNT signaling in regulating intermediate progenitors, modeled TCTN2-related D-V patterning defects, and identified a rescue strategy. © 2025 Elsevier Inc.

关键词： BMP neural tube organoid pluripotent stem cells SHH

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