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Fluorine-Containing Passivation Layer via Surface Chelation for Inorganic Perovskite Solar Cells

Angewandte Chemie 2022年第6期135卷

作者： Hao Zhang Prof. Wanchun Xiang Xuejiao Zuo Xiaojing Gu Shiang Zhang Yachao Du Zhiteng Wang Yali Liu Haifeng Wu Peijun Wang Qingyue Cui Hang Su Prof. Qingwen Tian Prof. Shengzhong (Frank) Liu Key Laboratory of Applied Surface and Colloid Chemistry Ministry of Education Shaanxi Key Laboratory for Advanced Energy Devices Shaanxi Engineering Lab for Advanced Energy Technology School of Materials Science and Engineering Shaanxi Normal University No. 620 West Chang'an Avenue Xi'an 710119 P. R. China Contribution: Data curation (lead) Formal analysis (lead) Investigation (lead) Writing - original draft (supporting) Contribution: Conceptualization (lead) Data curation (lead) Writing - original draft (lead) Contribution: Formal analysis (supporting) Investigation (supporting) Contribution: Data curation (supporting) Formal analysis (equal) Investigation (equal) Contribution: Data curation (supporting) Investigation (supporting) Contribution: Data curation (supporting) Formal analysis (supporting) State Key Laboratory of Organic-Inorganic Composites Key Lab of Biomedical Materials of Natural Macromolecules Ministry of Education Beijing Laboratory of Biomedical Materials Beijing University of Chemical Technology No. 15 East Road North Third Ring Chaoyang District Beijing 100029 P. R. China Contribution: Conceptualization (lead) Data curation (lead) Funding acquisition (lead) Project administration (lead) Supervision (lead) Validation (lead) Visualization (lead) Writing - review & editing (lead) Dalian National Laboratory for Clean Energy iChEM Dalian Institute of Chemical Physics Chinese Academy of Sciences No. 457 Zhongshan Road Dalian Liaoning 116023 P. R. China Contribution: Project administration (lead) Supervision (lead) Validation (lead) Writing - review & editing (equal)

Minimizing surface defect is vital to further improve power conversion efficiency (PCE) and stability of inorganic perovskite solar cells (PSCs). Herein, we designed a passivator trifluoroacetamidine (TFA) to suppress CsPbI 3− x Br x film defects. The amidine group of TFA can strongly chelate onto the perovskite surface to suppress the iodide vacancy, strengthened by additional hydrogen bonds. Moreover, three fluorine atoms allow strong intermolecular connection via intermolecular hydrogen bonds, thus constructing a robust shield against moisture. The TFA-treated PSCs exhibit remarkably suppressed recombination, yielding the record PCEs of 21.35 % and 17.21 % for 0.09 cm 2 and 1.0 cm 2 device areas, both of which are the highest for all-inorganic PSCs so far. The device also achieves a PCE of 39.78 % under indoor illumination, the highest for all-inorganic indoor photovoltaic devices. Furthermore, TFA greatly improves device ambient stability by preserving 93 % of the initial PCE after 960 h.

关键词： Chelation Defect Passivation Inorganic Perovskite Solar Cells

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Modulating Ligand-Exchange Dynamics on Metal-Organic Polyhedra for Reversible Sorting and Hybridization of Miktoarm Star Polymers

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Angewandte Chemie 2023年第49期135卷

作者： Yuyan Lai Xiaofeng He Binghui Xue Mu Li Dr. Huihui Wang Dr. Wei Huang Dr. Jia-Fu Yin Dr. Mingxin Zhang Prof. Dr. Panchao Yin State Key Laboratory of Luminescent Materials and Devices South China Advanced Institute for Soft Matter Science and Technology South China University of Technology Guangzhou 510640 P. R. China Contribution: Investigation (lead) Methodology (equal) Writing - original draft (equal) Contribution: Investigation (equal) Methodology (supporting) Writing - original draft (equal) Contribution: Data curation (equal) Methodology (supporting) Validation (supporting) Contribution: Formal analysis (equal) Resources (supporting) Validation (supporting) State Key Laboratory of Marine Resource Utilization in South China Sea Hainan Provincial Key Lab of Fine Chemistry School of Chemical Engineering and Technology Hainan University Haikou Hainan 570228 P. R. China Contribution: Methodology (supporting) Validation (supporting) Contribution: Formal analysis (supporting) Validation (supporting) Contribution: Formal analysis (supporting) Visualization (supporting) Contribution: Conceptualization (equal) Formal analysis (equal) Investigation (equal) Supervision (supporting) Visualization (lead) Writing - original draft (equal) Contribution: Conceptualization (lead) Formal analysis (equal) Funding acquisition (lead) Project administration (lead) Supervision (lead) Validation (equal) Writing - original draft (equal) Writing - review & editing (lead)

The precise synthesis of miktoarm star polymers (MSPs) remains one of the great challenges in synthetic chemistry due to the difficulty in locating appropriate structural templates and polymer grafting/growing strategies with high selectivity and efficiency. Herein, ≈2 nm metal-organic polyhedra (MOPs), constructed from the coordination of isophthalic acid (IPA) and Cu 2+ , are applied as templates for the precise synthesis of 24-arm MSPs for their unique logarithmic ligand-exchange dynamics. Six different polymers are prepared with IPA as an end group and they further coordinated with Cu 2+ to afford the corresponding 24-arm star homo-polymers. MSPs can be obtained by mixing targeted homo-arm star polymers in solutions upon thermal annealing. The compositions of MSPs can be facilely and precisely tuned by the recipe of the star polymer mixtures used. Interestingly, the obtained MSPs can be sorted into homo-arm star polymers through a typical solvent extraction procedure. The hybridization and sorting process can be reversibly conducted through the cycle of thermal annealing and solvent treatment. The complex coordination framework not only opens new avenues for the facile and precise synthesis of MSPs and MOPs with hybrid functionalities, but also provides the capability to design sustainable polymer systems.

关键词： Inorganic–Organic Hybrid Materials Metal–Organic Polyhedra Miktoarm Star Polymers Solution Dynamics Sustainable Polymers

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Carepre: An intelligent clinical decision assistance system

arXiv

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arXiv 2018年

作者： Jin, Zhuochen Yang, Jingshun Cui, Shuyuan Gotz, David Sun, Jimeng Cao, Nan Intelligent Big Data Visualization Lab Tongji University China University of North Carolina at Chapel Hill United States Georgia Institute of Technology United States

Clinical decision support systems (CDSS) are widely used to assist with medical decision making. However, CDSS typically require manually curated rules and other data which are difficult to maintain and keep up-to-date. Recent systems leverage advanced deep learning techniques and electronic health records (EHR) to provide a more timely and precise results. Many of these techniques have been developed with a common focus on predicting upcoming medical events. However, while the prediction results from these approaches are promising, their value is limited by their lack of interpretability. To address this challenge, we introduce CarePre , an intelligent clinical decision assistance system. The system extends a state-of-the-art deep learning model to predict upcoming diagnosis events for a focal patient based on his/her historical medical records. The system includes an interactive framework together with intuitive visualizations designed to support diagnosis, treatment outcome analysis, and the interpretation of the analysis results. We demonstrate the effectiveness and usefulness of CarePre system by reporting results from a quantities evaluation of the prediction algorithm and a case study and three interviews with senior physicians. Copyright © 2018, The Authors. All rights reserved.

关键词： Diagnosis

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Segregated interactions in urban and online spaces

arXiv

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arXiv 2019年

作者： Dong, Xiaowen Morales, Alfredo J. Jahani, Eaman Moro, Esteban Lepri, Bruno Bozkaya, Burcin Sarraute, Carlos Bar-Yam, Yaneer Pentland, Alex Department of Engineering Science University of Oxford Oxford United Kingdom MIT Media Lab CambridgeMA United States New England Complex Systems Institute CambridgeMA United States MIT Institute for Data Systems and Society CambridgeMA United States Department of Mathematics and GISC Universidad Carlos III de Madrid Leganés Spain Mobile and Social Computing Lab Fondazione Bruno Kessler Trento Italy Behavioral Analytics & Visualization Lab Sabancı University Istanbul Turkey New College of Florida SarasotaFL United States Grandata Labs Buenos Aires Argentina

Urban income segregation is a widespread phenomenon that challenges societies across the globe. Classical studies on segregation have largely focused on the geographic distribution of residential neighborhoods rather than on patterns of social behaviors and interactions. In this study, we analyze segregation in economic and social interactions by observing credit card transactions and Twitter mentions among thousands of individuals in three culturally different metropolitan areas. We show that segregated interaction is amplified relative to the expected effects of geographic segregation in terms of both shopping activity and online communication. Furthermore, we find that segregation increases with difference in socio-economic status but is asymmetric, i.e., the amount of interaction from poorer to wealthier neighborhoods is considerably larger than vice versa. Our results provide novel insights into the understanding of behavioral segregation in human interactions with significant socio-political and economic implications. Copyright © 2019, The Authors. All rights reserved.

关键词： Geographical distribution

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Anionic Co-insertion Charge Storage in Dinitrobenzene Cathodes for High-Performance Aqueous Zinc–Organic Batteries

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Angewandte Chemie 2022年第35期134卷

作者： Dr. Ziyang Song Dr. Ling Miao Hui Duan Prof. Laurent Ruhlmann Dr. Yaokang Lv Prof. Dazhang Zhu Prof. Liangchun Li Prof. Lihua Gan Prof. Mingxian Liu Shanghai Key Lab of Chemical Assessment and Sustainability School of Chemical Science and Engineering Tongji University Shanghai 200092 P. R. China Contribution: Conceptualization (lead) Data curation (lead) Formal analysis (lead) Investigation (lead) Methodology (lead) Validation (lead) Visualization (lead) Writing - original draft (lead) Writing - review & editing (lead) Contribution: Formal analysis (supporting) Funding acquisition (supporting) Investigation (supporting) Methodology (supporting) Contribution: Formal analysis (supporting) Investigation (supporting) Validation (supporting) Institut de Chimie (UMR au CNRS n°7177) Université de Strasbourg 4 rue Blaise Pascal CS 90032 67081 Strasbourg Cedex France Contribution: Formal analysis (supporting) Methodology (supporting) Validation (supporting) College of Chemical Engineering Zhejiang University of Technology Hangzhou 310014 P. R. China Contribution: Formal analysis (supporting) Validation (supporting) Contribution: Formal analysis (supporting) Funding acquisition (supporting) Supervision (supporting) Writing - review & editing (supporting) Contribution: Formal analysis (equal) Funding acquisition (lead) Project administration (equal) Supervision (equal) Validation (equal) Visualization (equal) Writing - review & editing (equal)

Highly active and stable cathodes are critical for aqueous Zn–organic batteries with high capacity, fast redox kinetics, and long life. We herein report para -, meta -, and ortho -dinitrobenzene ( p -, m -, and o -DB) containing two successive two-electron processes, as cathode materials to boost the battery performance. Theoretical and experimental studies reveal that nitro constitutional isomerism is key to zincophilic activity and redox kinetics. p -DB hosted in carbon nanoflower harvests a high capacity of 402 mAh g −1 and a superior stability up to 25 000 cycles at 5 A g −1 , giving a Zn–organic battery with a high energy density of 230 Wh kg −1 . An anionic co-insertion charge storage mechanism is proposed, entailing a two-step (de)coordination of Zn(CF 3 SO 3 ) + with nitro oxygen. Besides, dinitrobenzene can be electrochemically optimized by side group regulation via implanting electron-withdrawing motifs. This work opens a new window to design multielectron nitroaromatics for Zn–organic batteries.

关键词： Anionic Co-Insertion Charge Storage Dinitrobenzene Cathode High Capacity High Stability Zinc–Organic Batteries

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Flexible, ultrathin bioelectronic materials and devices for chronically stable neural interfaces

Brain‐X

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Brain‐X 2023年第4期1卷

作者： Lianjie Zhou Zhongyuan Wu Mubai Sun Jaejin Park Mengdi Han Ming Wang Junsheng Yu Zengfeng Di Yongfeng Mei Wubin Bai Xinge Yu Ki Jun Yu Enming Song Shanghai Frontiers Science Research Base of Intelligent Optoelectronics and Perception Institute of Optoelectronics Fudan University Shanghai China Contribution: Investigation Visualization Writing - original draft Institute of Agro-food Technology Jilin Academy of Agricultural Sciences Changchun Jilin China Contribution: Data curation Investigation Functional Bio-integrated Electronics and Energy Management Lab School of Electrical and Electronic Engineering Yonsei University Seoul Korea Department of Biomedical Engineering College of Future Technology Peking University Beijing China Contribution: Resources Validation State Key Laboratory of Integrated Chips and Systems Frontier Institute of Chip and System Fudan University Shanghai China State Key Laboratory of Electronic Thin Films and Integrated Devices School of Optoelectronic Science and Engineering University of Electronic Science and Technology of China (UESTC) Chengdu Sichuan China State Key Laboratory of Functional Materials for Informatics Shanghai Institute of Microsystem and Information Technology Chinese Academy of Sciences Shanghai China Department of Material Science Fudan University Shanghai China Department of Applied Physical Sciences University of North Carolina at Chapel Hill Chapel Hill North Carolina USA Department of Biomedical Engineering City University of Hong Kong Hong Kong SAR China Wubin Bai Xinge Yu Ki Jun Yu and Enming Song. Contribution: Conceptualization Methodology Resources Supervision Writing - review & editing

Advanced technologies that can establish intimate, long-lived functional interfaces with neural systems have attracted increasing interest due to their wide-ranging applications in neuroscience, bioelectronic medicine, and the associated treatment of neurodegenerative diseases. A critical challenge of significance remains in the development of electronic platforms that offer conformal contact with soft brain tissue for the sensing or stimulation of brain activities and chronically stable operation in vivo, at scales that range from cellular-level resolution to macroscopic areas. This review summarizes recent advances in this field, with an emphasis on the use of demonstrated concepts, constituent materials, engineered designs, and system integration to address the current challenges. The article begins with an overview of recent bioelectronic platforms with unique form factors, ranging from filamentary probes to conformal sheets and three-dimensional frameworks for alleviating the mechanical mismatch between interface materials and neural tissues. Next, active interfaces which utilize inorganic/organic semiconductor-enabled devices are reviewed, highlighting various working principles of recording mechanisms including capacitively and conductively coupled sensing enabled by high transistor matrices at high spatiotemporal resolution. The subsequent section presents approaches to biological integration which use active materials for multiplexed addressing, local amplification and multimodal operation with high-channel-count and large-scale electronic systems in a safe fashion that provides multi-decade stable performance in both animal models and human subjects. The advances summarized in this review will guide the future direction of this technology and provide a basis for next-generation chronic neural interfaces with long-lived high-performance operation.

关键词： neural interface flexibility passive electrodes active recording barrier materials chronic stability

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Analysis of the BraTS 2023 Intracranial Meningioma Segmentation Challenge

arXiv

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arXiv 2024年

作者： labella, Dominic Baid, Ujjwal Khanna, Omaditya McBurney-Lin, Shan McLean, Ryan Nedelec, Pierre Rashid, Arif Tahon, Nourel Hoda Altes, Talissa Bhalerao, Radhika Dhemesh, Yaseen Godfrey, Devon Hilal, Fathi Floyd, Scott Janas, Anastasia Kazerooni, Anahita Fathi Kirkpatrick, John Kent, Collin Kofler, Florian Leu, Kevin Maleki, Nazanin Menze, Bjoern Pajot, Maxence Reitman, Zachary J. Rudie, Jeffrey D. Saluja, Rachit Velichko, Yury Wang, Chunhao Warman, Pranav Adewole, Maruf Albrecht, Jake Anazodo, Udunna Anwar, Syed Muhammad Bergquist, Timothy Chen, Sully Francis Chung, Verena Conte, Gian-Marco Dako, Farouk Eddy, James Ezhov, Ivan Khalili, Nastaran Iglesias, Juan Eugenio Jiang, Zhifan Johanson, Elaine Van Leemput, Koen Li, Hongwei Bran Linguraru, Marius George Liu, Xinyang Mahtabfar, Aria Meier, Zeke Moawad, Ahmed W. Mongan, John Piraud, Marie Shinohara, Russell Takeshi Wiggins, Walter F. Abayazeed, Aly H. Akinola, Rachel Jakab, András Bilello, Michel de Verdier, Maria Correia Crivellaro, Priscila Davatzikos, Christos Farahani, Keyvan Freymann, John Hess, Christopher Huang, Raymond Lohmann, Philipp Moassefi, Mana Pease, Matthew W. Vollmuth, Phillipp Sollmann, Nico Diffley, David Nandolia, Khanak K. Warren, Daniel I. Hussain, Ali Fehringer, Pascal Bronstein, Yulia Deptula, Lisa Stein, Evan G. Taherzadeh, Mahsa de Oliveira, Eduardo Portela Haughey, Aoife Kontzialis, Marinos Saba, Luca Turner, Benjamin Brüßeler, Melanie M.T. Ansari, Shehbaz Gkampenis, Athanasios Weiss, David Maximilian Mansour, Aya Shawali, Islam H. Yordanov, Nikolay Stein, Joel M. Hourani, Roula Moshebah, Mohammed Yahya Abouelatta, Ahmed Magdy Rizvi, Tanvir Willms, Klara Martin, Dann C. Okar, Abdullah D'Anna, Gennaro Taha, Ahmed Sharifi, Yasaman Faghani, Shahriar Kite, Dominic Pinho, Marco Haider, Muhammad Ammar Aristizabal, Alejandro Karargyris, Alexandros Kassem, Hasan Pati, Sarthak Sheller, Micah Alonso-Basanta, Michelle Villanueva-Meyer, Javier Rauschecker, Andreas M. Nada, Ayman Aboian, Mariam Flanders, Adam E. Wiestler, Benedikt Bakas, Spyridon Ca Duke University Medical Center Department of Radiation Oncology DurhamNC United States Division of Computational Pathology Department of Pathology Laboratory Medicine Indiana University School of Medicine IndianapolisIN United States Center for Federated Learning in Medicine Indiana University IndianapolisIN United States Department of Neurosurgery Thomas Jefferson University PhiladelphiaPA United States University of California San FranciscoCA United States Yale University New HavenCT United States Department of Radiation Oncology Perelman School of Medicine University of Pennsylvania PhiladelphiaPA United States University of Missouri ColumbiaMO United States Department of Biostatistics and Health Data Science Indiana University School of Medicine IndianapolisIN United States Children's Hospital of Philadelphia University of Pennsylvania PhiladelphiaPA United States University of Pennsylvania PhiladelphiaPA United States Helmholtz AI Helmholtz Munich Germany Department of Informatics Technical University Munich Germany TranslaTUM - Central Institute for Translational Cancer Research Technical University of Munich Germany Department of Diagnostic and Interventional Neuroradiology School of Medicine Klinikum rechts der Isar Technical University of Munich Germany San FranciscoCA United States Biomedical Image Analysis and Machine Learning Department of Quantitative Biomedicine University of Zurich Switzerland Department of Neuroradiology Technical University of Munich Munich Germany University of San Diego CA United States Cornell University IthacaNY United States Department of Radiology Northwestern University EvanstonIL United States Duke University Medical Center School of Medicine DurhamNC United States Lab Crestview Radiology Lagos Nigeria Sage Bionetworks United States McGill University MontrealQC Canada Children's National Hospital WashingtonDC United States George Washington University WashingtonDC United State

We describe the design and results from the BraTS 2023 Intracranial Meningioma Segmentation Challenge. The BraTS Meningioma Challenge differed from prior BraTS Glioma challenges in that it focused on meningiomas, which are typically benign extra-axial tumors with diverse radiologic and anatomical presentation and a propensity for multiplicity. Nine participating teams each developed deep-learning automated segmentation models using image data from the largest multi-institutional systematically expert annotated multilabel multi-sequence meningioma MRI dataset to date, which included 1000 training set cases, 141 validation set cases, and 283 hidden test set cases. Each case included T2, T2/FLAIR, T1, and T1Gd brain MRI sequences with associated tumor compartment labels delineating enhancing tumor, non-enhancing tumor, and surrounding non-enhancing T2/FLAIR hyperintensity. Participant automated segmentation models were evaluated and ranked based on a scoring system evaluating lesion-wise metrics including dice similarity coefficient (DSC) and 95% Hausdorff Distance. The top ranked team had a lesion-wise median dice similarity coefficient (DSC) of 0.976, 0.976, and 0.964 for enhancing tumor, tumor core, and whole tumor, respectively and a corresponding average DSC of 0.899, 0.904, and 0.871, respectively. These results serve as state-of-the-art benchmarks for future pre-operative meningioma automated segmentation algorithms. Additionally, we found that 1286 of 1424 cases (90.3%) had at least 1 compartment voxel abutting the edge of the skull-stripped image edge, which requires further investigation into optimal pre-processing face anonymization steps. © 2024, CC BY.

关键词： Tumors

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100 essential questions for the future of agriculture

Modern Agriculture

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Modern Agriculture 2023年第1期1卷

作者： Yuming Hu Taolan Zhao Yafang Guo Meng Wang Kerstin Brachhold Chengcai Chu Andrew Hanson Sachin Kumar Rongcheng Lin Wenjin Long Ming Luo Jian Feng Ma Yansong Miao Shaoping Nie Yu Sheng Weiming Shi James Whelan Qingyu Wu Ziping Wu Wei Xie Yinong Yang Chao Zhao Lei Lei Yong-Guan Zhu Qifa Zhang Wiley-VCH GmbH Weinheim Germany Contribution: Conceptualization (equal) Data curation (equal) Formal analysis (equal) Investigation (equal) Methodology (equal) Project administration (equal) Writing - original draft (equal) Writing - review & editing (equal) Contribution: Investigation (equal) Resources (equal) Writing - original draft (equal) Contribution: Data curation (equal) Investigation (equal) Visualization (equal) Contribution: Writing - review & editing (equal) State Key Laboratory for Conservation and Utilization of Subtropical Agro-Bioresources College of Agriculture South China Agricultural University Guangzhou China Contribution: Investigation (equal) Resources (equal) Horticultural Sciences Department University of Florida Gainesville Florida USA Rumen Biotechnology Laboratory Animal Nutrition Division ICAR-National Dairy Research Institute Karnal Haryana India Key Laboratory of Photobiology Institute of Botany Chinese Academy of Sciences Beijing China School of Economics and Management China Agricultural University Beijing China Guangdong Provincial Key Laboratory of Applied Botany & Agriculture and Biotechnology Research Center South China Botanical Garden Chinese Academy of Sciences Guangzhou China Institute of Plant Science and Resources Okayama University Kurashiki Japan School of Biological Sciences Nanyang Technological University Singapore City Singapore State Key Laboratory of Food Science and Technology China-Canada Joint Lab of Food Science and Technology (Nanchang) Nanchang University Nanchang China China Center for Agricultural Policy School of Advanced Agricultural Sciences Peking University Beijing China State Key Laboratory of Soil and Sustainable Agriculture Institute of Soil Science Chinese Academy of Sciences Nanjing China State Key Laboratory of Plant Physiology and Biochemistry College of Life Sciences Zhejiang University Hangzhou Zhejiang China Department of Animal Plant and Soil Sciences La Trobe University Me

The world is at a crossroad when it comes to agriculture. The global population is growing, and the demand for food is increasing, putting a strain on our agricultural resources and practices. To address this challenge, innovative, sustainable, and inclusive approaches to agriculture are urgently required. In this paper, we launched a call for Essential Questions for the Future of Agriculture and identified a priority list of 100 questions. We focus on 10 primary themes: transforming agri-food systems, enhancing resilience of agriculture to climate change, mitigating climate change through agriculture, exploring resources and technologies for breeding, advancing cultivation methods, sustaining healthy agroecosystems, enabling smart and controlled-environment agriculture for food security, promoting health and nutrition-driven agriculture, exploring economic opportunities and addressing social challenges, and integrating one health and modern agriculture. We emphasise the critical importance of interdisciplinary and multidisciplinary research that integrates both basic and applied sciences and bridges the gaps among various stakeholders for achieving sustainable agriculture.

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Biomedical image analysis competitions: The state of current participation practice

arXiv

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arXiv 2022年

作者： Eisenmann, Matthias Reinke, Annika Weru, Vivienn Tizabi, Minu Dietlinde Isensee, Fabian Adler, Tim J. Godau, Patrick Cheplygina, Veronika Kozubek, Michal Maier-Hein, Klaus Jäger, Paul F. Kopp-Schneider, Annette Maier-Hein, Lena Ali, Sharib Gupta, Anubha Kybic, Jan Noble, Alison de Solórzano, Carlos Ortiz Pachade, Samiksha Petitjean, Caroline Sage, Daniel Wei, Donglai Wilden, Elizabeth Alapatt, Deepak Andrearczyk, Vincent Baid, Ujjwal Bakas, Spyridon Balu, Niranjan Bano, Sophia Bawa, Vivek Singh Bernal, Jorge Bodenstedt, Sebastian Casella, Alessandro Choi, Jinwook Commowick, Olivier Daum, Marie Depeursinge, Adrien Dorent, Reuben Egger, Jan Eichhorn, Hannah Engelhardt, Sandy Ganz, Melanie Girard, Gabriel Hansen, Lasse Heinrich, Mattias Heller, Nicholas Hering, Alessa Huaulmé, Arnaud Kim, Hyunjeong Li, Hongwei Bran Landman, Bennett Li, Jianning Ma, Jun Martel, Anne Martín-Isla, Carlos Menze, Bjoern Nwoye, Chinedu Innocent Oreiller, Valentin Padoy, Nicolas Pati, Sarthak Payette, Kelly Sudre, Carole van Wijnen, Kimberlin Vardazaryan, Armine Vercauteren, Tom Wagner, Martin Wang, Chuanbo Yap, Moi Hoon Yu, Zeyun Yuan, Chun Zenk, Maximilian Zia, Aneeq Zimmerer, David Bao, Rina Choi, Chanyeol Cohen, Andrew Dzyubachyk, Oleh Galdran, Adrian Gan, Tianyuan Guo, Tianqi Gupta, Pradyumna Haithami, Mahmood Ho, Edward Jang, Ikbeom Li, Zhili Luo, Zhengbo Lux, Filip Makrogiannis, Sokratis Müller, Dominik Oh, Young-Tack Pang, Subeen Pape, Constantin Polat, Gorkem Reed, Charlotte Rosalie Ryu, Kanghyun Scherr, Tim Thambawita, Vajira Wang, Haoyu Wang, Xinliang Xu, Kele Yeh, Hung Yeo, Doyeob Yuan, Yixuan Zeng, Yan Zhao, Xin Abbing, Julian Adam, Jannes Adluru, Nagesh Agethen, Niklas Ahmed, Salman Al Khalil, Yasmina Alenyà, Mireia Alhoniemi, Esa An, Chengyang Arega, Tewodros Weldebirhan Avisdris, Netanell Aydogan, Dogu Baran Bai, Yingbin Calisto, Maria Baldeon Basaran, Berke Doga Beetz, Marcel Bian, Hao Blansit, Kevin Bloch, Louise Bohnsack, Robert Bosticardo, Sara Breen, Jack Brudfors, Mikael Brüngel, Raphael Cabezas, Mariano Cacciola, Alb Heidelberg Division of Intelligent Medical Systems Germany Heidelberg HI Helmholtz Imaging Germany Faculty of Mathematics and Computer Science Heidelberg University Heidelberg Germany Heidelberg Division of Biostatistics Germany Heidelberg Division of Medical Image Computing Germany Heidelberg HI Applied Vision Lab Germany IT University of Copenhagen Copenhagen Denmark Centre for Biomedical Image Analysis Masaryk University Brno Czech Republic Heidelberg Interactive Machine Learning Group Germany Faculty of Mathematics and Computer Science and Medical Faculty Heidelberg University Heidelberg Germany NCT Heidelberg DKFZ University Hospital Heidelberg Germany School of Computing University of Leeds Leeds United Kingdom SBILab Department of ECE IIIT-Delhi India Faculty of Electrical Engineering Czech Technical University Prague Czech Republic Institute of Biomedical Engineering University of Oxford United Kingdom Center for Applied Medical Research Pamplona Spain Shri Guru Gobind Singhji Institute of Engineering and Technology Maharashtra Nanded India Université de Rouen Normandie France Lausanne Switzerland School of Engineering and Applied Science Harvard University United States ICube University of Strasbourg CNRS France Institute of Informatics School of Management HES-SO Valais-Wallis University of Applied Sciences and Arts Western Switzerland Techno-Pôle 3 Sierre3960 Switzerland Department of Nuclear Medicine and Molecular Imaging Lausanne University Hospital Rue du Bugnon 46 LausanneCH-1011 Switzerland University of Pennsylvania PhiladelphiaPA United States Department of Radiology University of Washington United States Wellcome EPSRC Centre for Interventional and Surgical Sciences University College London London United Kingdom Visual Artificial Intelligence Lab Oxford Brookes University Oxford United Kingdom Universitat Autònoma de Barcelona & Computer Vision Center Spain Dresden Fetscherstraße 74 PF 64 Dresden01307 Germany

The number of international benchmarking competitions is steadily increasing in various fields of machine learning (ML) research and practice. So far, however, little is known about the common practice as well as bottlenecks faced by the community in tackling the research questions posed. To shed light on the status quo of algorithm development in the specific field of biomedical imaging analysis, we designed an international survey that was issued to all participants of challenges conducted in conjunction with the IEEE ISBI 2021 and MICCAI 2021 conferences (80 competitions in total). The survey covered participants' expertise and working environments, their chosen strategies, as well as algorithm characteristics. A median of 72% challenge participants took part in the survey. According to our results, knowledge exchange was the primary incentive (70%) for participation, while the reception of prize money played only a minor role (16%). While a median of 80 working hours was spent on method development, a large portion of participants stated that they did not have enough time for method development (32%). 25% perceived the infrastructure to be a bottleneck. Overall, 94% of all solutions were deep learning-based. Of these, 84% were based on standard architectures. 43% of the respondents reported that the data samples (e.g., images) were too large to be processed at once. This was most commonly addressed by patch-based training (69%), downsampling (37%), and solving 3D analysis tasks as a series of 2D tasks. K-fold cross-validation on the training set was performed by only 37% of the participants and only 50% of the participants performed ensembling based on multiple identical models (61%) or heterogeneous models (39%). 48% of the respondents applied postprocessing steps. © 2022, CC BY.

关键词： Benchmarking

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The Brain Tumor Sequence Registration (BraTS-Reg) Challenge: Establishing Correspondence Between Pre-Operative and Follow-up MRI Scans of Diffuse Glioma Patients

arXiv

引用

arXiv 2021年

作者： Baheti, Bhakti Chakrabarty, Satrajit Akbari, Hamed Bilello, Michel Wiestler, Benedikt Schwarting, Julian Calabrese, Evan Rudie, Jeffrey Abidi, Syed Mousa, Mina Villanueva-Meyer, Javier Fields, Brandon K.K. Kofler, Florian Shinohara, Russell Takeshi Iglesias, Juan Eugenio Mok, Tony C.W. Chung, Albert C.S. Wodzinski, Marek Jurgas, Artur Marini, Niccolò Atzori, Manfredo Müller, Henning Grosbröhmer, Christoph Siebert, Hanna Hansen, Lasse Heinrich, Mattias P. Canalini, Luca Klein, Jan Gerken, Annika Heldmann, Stefan Hering, Alessa Hahn, Horst K. Meng, Mingyuan Bi, Lei Feng, Dagan Kim, Jinman Zeineldinal, Ramy A. Karar, Mohamed E. Mathis-Ullrich, Franziska Burgert, Oliver Abderezaei, Javid Pionteck, Aymeric Chopra, Agamdeep Kurt, Mehmet Yan, Kewei Yan, Yonghong Tang, Zhe Ma, Jianqiang Nasser, Sahar Almahfouz Kurian, Nikhil Cherian Meena, Mohit Shamsi, Saqib Sethi, Amit Tustison, Nicholas J. Avants, Brian B. Cook, Philip Gee, James C. Tian, Lin Greer, Hastings Niethammer, Marc Hoopesar, Andrew Hoffmannas, Malte Dalcaar, Adrian V. Christodoulidis, Stergios Estiene, Théo Vakalopoulou, Maria Paragios, Nikos Marcus, Daniel S. Davatzikos, Christos Sotiras, Aristeidis Menze, Bjoern Bakas, Spyridon Waldmannstetter, Diana Division of Computational Pathology Department of Pathology and Laboratory Medicine Indiana University School of Medicine IndianapolisIN United States Department of Electrical and Systems Engineering Washington University in St. Louis St. LouisMO United States GE HealthCare San RamonCA United States University of Pennsylvania PhiladelphiaPA United States Department of Radiology Perelman School of Medicine University of Pennsylvania PhiladelphiaPA United States Mallinckrodt Institute of Radiology Washington University School of Medicine St. LouisMO United States Institute for Informatics Data Science & Biostatistics Washington University School of Medicine St. LouisMO United States Department of Bioengineering Santa Clara University Santa ClaraCA United States Department of Quantitative Biomedicine University of Zurich Zurich Switzerland Department of Computer Science TUM School of Computation Information and Technology Technical University of Munich Munich Germany Department of Diagnostic and Interventional Neuroradiology School of Medicine Klinikum rechts der Isar Technical University of Munich Munich Germany Department of Nuclear Medicine University Hospital Regensburg Regensburg Germany University Hospital Regensburg Regensburg Germany Department of Radiology & Biomedical Imaging University of California San FranciscoCA United States Department of Radiology Duke University Medical Center DurhamNC United States Department of Radiology University of California San DiegoCA United States Helmholtz AI Helmholtz Munich Neuherberg Germany Penn Statistics in Imaging and Visualization Center Department of Biostatistics Epidemiology and Informatics University of Pennsylvania Philadelphia United States Massachusetts General Hospital Harvard Medical School United States Centre for Medical Imaging Computing University College London United Kingdom The Hong Kong University of Science and Technology Hong Kong University of Applied Sciences West

Registration of longitudinal brain Magnetic Resonance Imaging (MRI) scans containing pathologies is challenging due to dramatic changes in tissue appearance. Although there has been considerable progress in developing general-purpose medical image registration techniques, they have not yet attained the requisite precision and reliability for this task, highlighting its inherent complexity. Here we describe the Brain Tumor Sequence Registration (BraTS-Reg) challenge, as the first public benchmark environment for deformable registration algorithms focusing on estimating correspondences between pre-operative and follow-up scans of the same patient diagnosed with a diffuse brain glioma. The challenge was conducted in conjunction with both the IEEE International Symposium on Biomedical Imaging (ISBI) 2022 and the International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI) 2022. The BraTS-Reg data comprise de-identified multi-institutional multi-parametric MRI (mpMRI) scans, curated for size and resolution according to a canonical anatomical template, and divided into training, validation, and testing sets. Clinical experts annotated ground truth (GT) landmark points of anatomical locations distinct across the temporal domain. The training data with their GT annotations, were publicly released to enable the development of registration algorithms. The validation data, without their GT annotations, were also released to allow for algorithmic evaluation prior to the testing phase, which only allowed submission of containerized algorithms for evaluation on hidden hold-out testing data. Quantitative evaluation and ranking was based on the Median Euclidean Error (MEE), Robustness, and the determinant of the Jacobian of the displacement field. The top-ranked methodologies yielded similar performance across all evaluation metrics and shared several methodological commonalities, including pre-alignment, deep neural networks, inverse consistency

关键词： Magnetic resonance imaging

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