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DP03 Melanoma detection in whole-slide images using a convolutional neural network for objective prognostic biomarker generation

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British Journal of Dermatology 2024年第SUPPLEMENT_1期191卷 i108–i108页

作者： Clarke, Emily L Magee, Derek Newton-Bishop, Julia Merchant, William Hall, Marlous Insall, Robert Maher, Nigel G Scolyer, Richard A Farnworth, Grace Ali, Anisah Saldanha, Gerald Bamford, Mark Sitcova, Eva Kujal, Petr O’Shea, Sally Treanor, Darren Department of Histopathology Leeds Teaching Hospitals NHS Trust Leeds UK Division of Pathology and Data Analytics University of Leeds Leeds UK School of Computing University of Leeds Leeds UK Leeds Institute for Medical Research University of Leeds Leeds UK Department of Clinical and Population Sciences Leeds Institute of Cardiovascular and Metabolic Medicine University of Leeds Leeds UK Department of Cell and Developmental Biology University College London London UK Melanoma Institute Australia The University of Sydney Sydney Australia Faculty of Medicine and Health The University of Sydney Sydney Australia Tissue Pathology and Diagnostic Oncology Royal Prince Alfred Hospital and NSW Health Pathology Sydney Australia Charles Perkins Centre The University of Sydney Sydney NSW Australia Leicester Cancer Research Centre University of Leicester Leicester UK Department of Histopathology University Hospitals of Leicester NHS Trust Leicester UK Department of Pathology Third Faculty of Medicine Charles University and Teaching Hospital Královské Vinohrady Prague Czechia Dermatology Department South Infirmary Victoria University Hospital Cork Ireland School of Medicine University College Cork Ireland Department of Clinical Pathology and Department of Clinical and Experimental Medicine Linköping University Linköping Sweden Center for Medical Image Science and Visualization (CMIV) Linköping University Linköping Sweden

The current melanoma staging system is predictive of 74% of the variance in survival, with prognostic biomarkers subject to high levels of interobserver and intraobserver variation. Melanoma morphology appears to be of greater significance than in other solid tumours, with Breslow thickness remaining the strongest prognostic indicator. The application of convolutional neural networks (CNNs) to whole-slide images (WSIs) may reveal new insights into tumour morphology and therefore patient prognosis. This work outlines the development and evaluation of a CNN for invasive cutaneous melanoma detection in WSIs, to enable the creation of objective prognostic biomarkers based on the tissue morphology. In total, 1157 WSIs containing cutaneous melanoma from five sources have been used in the initial development and evaluation of a custom-designed two-class tumour segmentation network with a fully convolutional architecture. The CNN detected and located invasive melanoma tissue of no specific type with an average per-pixel sensitivity and specificity of 97.6% and 99.9%, respectively across, the five test sets (three external). There were no statistical differences between tumour dimensions generated by the CNN compared with manual annotation. Similarly, there were no statistically significant differences between CNN-generated tumour dimensions across three scanning platforms. Furthermore, we have identified that this CNN can be used to calculate the ‘digital Breslow thickness’, which is a strong independent prognostic predictor of overall survival and melanoma-specific survival, across three test sets with follow-up data (hazard ratio 1.26, 95% confidence interval 1.19–1.34,P< 0.001). We have also shown that the ‘nodularity index’ determined by the tumour shape, independently of size, is predictive of survival; the rounder the tumour the worse the outcome (hazard ratio 0.71, 95% confidence interval 0.60–0.83,P< 0.001). We have developed and performed initial evaluation of a CN

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Improving the generalizability of convolutional neural network-based segmentation on CMR images

arXiv

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arXiv 2019年

作者： Chen, Chen Bai, Wenjia Davies, Rhodri H. Bhuva, Anish N. Manisty, Charlotte Moon, James C. Aung, Nay Lee, Aaron M. Sanghvi, Mihir M. Fung, Kenneth Paiva, Jose Miguel Petersen, Steffen E. Lukaschuk, Elena Piechnik, Stefan K. Neubauer, Stefan Rueckert, Daniel Biomedical Image Analysis Group Department of Computing Imperial College London London United Kingdom Data Science Institute Imperial College London London United Kingdom Department of Medicine Imperial College London London United Kingdom Institutes for Cardiovascular Science University College London London United Kingdom Department of Cardiovascular Imaging Barts Heart Centre St Bartholomew's Hospital London United Kingdom NIHR Biomedical Research Centre at Barts Queen Mary University of London London United Kingdom NIHR BRC Oxford Division of Cardiovascular Medicine Radcliffe Department of Medicine University of Oxford Oxford United Kingdom

Convolutional neural network (CNN) based segmentation methods provide an efficient and automated way for clinicians to assess the structure and function of the heart in cardiac MR images. While CNNs can generally perform the segmentation tasks with high accuracy when training and test images come from the same domain (e.g. same scanner or site), their performance often degrades dramatically on images from different scanners or clinical sites. We propose a simple yet effective way for improving the network generalization ability by carefully designing data normalization and augmentation strategies to accommodate common scenarios in multi-site, multi-scanner clinical imaging data sets. We demonstrate that a neural network trained on a single-site single-scanner dataset from the UK Biobank can be successfully applied to segmenting cardiac MR images across different sites and different scanners without substantial loss of accuracy. Specifically, the method was trained on a large set of 3,975 subjects from the UK Biobank. It was then directly tested on 600 different subjects from the UK Biobank for intra-domain testing and two other sets for cross-domain testing: the ACDC dataset (100 subjects, 1 site, 2 scanners) and the BSCMR-AS dataset (599 subjects, 6 sites, 9 scanners). The proposed method produces promising segmentation results on the UK Biobank test set which are comparable to previously reported values in the literature, while also performing well on cross-domain test sets, achieving a mean Dice metric of 0.90 for the left ventricle, 0.81 for the myocardium and 0.82 for the right ventricle on the ACDC dataset;and 0.89 for the left ventricle, 0.83 for the myocardium on the BSCMR-AS dataset. The proposed method offers a potential solution to improve CNN-based model generalizability for the cross-scanner and cross-site cardiac MR image segmentation task. Copyright © 2019, The Authors. All rights reserved.

关键词： Image segmentation

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First principles calculations of superconducting critical temperature of ThCr2Si2-type structure

arXiv

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arXiv 2019年

作者： Sinaga, Gewinner Senderanto Utimula, Keishu Nakano, Kousuke Hongo, Kenta Maezono, Ryo School of Materials Science JAIST Asahidai 1-1 Nomi Ishikawa923-1292 Japan Research Center for Advanced Computing Infrastructure JAIST Asahidai 1-1 Nomi Ishikawa923-1292 Japan Center for Materials Research by Information Integration Research and Services Division of Materials Data and Integrated System National Institute for Materials Science Tsukuba305-0047 Japan Computational Engineering Applications Unit RIKEN 2-1 Hirosawa Wako Saitama351-0198 Japan PRESTO JST Kawaguchi Saitama332-0012 Japan School of Information Science JAIST Asahidai 1-1 Nomi Ishikawa923-1292 Japan Computational Engineering Applications Unit RIKEN 2-1 Hirosawa Wako Saitama351-0198 Japan

High critical temperature (Tc) superconductor has a great potential in many industrial applications. However, discovering a compound having high Tc is still remaining a big challenge for experimental approach due to time-consuming and high cost. In this paper, we investigated the critical temperature (Tc) of several compounds of ThCr2Si2-type structure (space group I4/mmm) since some of them had already been investigated and trusted as the potential candidates for superconductivity. First principle calculation was performed to compute the critical temperature (Tc) based on allen-dynes equation modification of McMillian formula. In order to confirm our calculation scheme, we compared our result with compounds which had been experimentally determined obtained from NIMS database. The result showed a very good agreement with experimental data. Based on this scheme, finally, we found ThCu2Si2 and ThAu2Si2 which were most likely to exhibit a superconductivity around 3.88 K and 4.27. Since, these compounds have not been studied experimentally, then we futher investigated their electronic properties showing non-negative phonons which indicate the dynamical stability of the compounds at ambient condition. Band structure and DOS analysis showed metallic nature where the highest contributor come from d-orbital of rare-earth element and p-orbital of Si emerging a mixture of covalent, ionic and metallic bonds exists in both the compounds. Finally, we calculated the electron phonon coupling coefficient showing a quite large different (27%) leading to the increment of Tc, however, both of compounds possess three dimensional electron-like and hole-like multi-band fermi surface which is related to the phonon mediated which is still under electron-phonon mediated superconductivity. Copyright © 2019, The Authors. All rights reserved.

关键词： Temperature

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Making the most of data: Quantum monte carlo post-analysis revisited

arXiv

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arXiv 2019年

作者： Ichibha, Tom Hongo, Kenta Maezono, Ryo Thom, Alex J.W. School of Information Science JAIST 1-1 Asahidai Nomi Ishikawa923-1292 Japan Research Center for Advanced Computing Infrastructure JAIST 1-1 Asahidai Nomi Ishikawa923-1292 Japan Center for Materials Research by Information Integration Research and Services Division of Materials Data and Integrated System National Institute for Materials Science 1-2-1 Sengen Tsukuba305-0047 Japan PRESTO Japan Science and Technology Agency 4-1-8 Honcho Kawaguchi-shi Saitama322-0012 Japan Computational Engineering Applications Unit RIKEN 2-1 Hirosawa Wako Saitama351-0198 Japan Department of Chemistry University of Cambridge Lensfield Road CambridgeCB2 1EW United Kingdom

In quantum Monte Carlo (QMC) methods, energy estimators are calculated as the statistical average of the Markov chain sampling of energy estimator along with an associated statistical error. This error estimation is not straightforward and there are several choices of the error estimation methods. We evaluate the performance of three methods, Straatsma, an autoregressive model, and a blocking analysis based on von Neumann's ratio test for randomness, for the energy time-series given by Diffusion Monte Carlo, Full Configuration Interaction Quantum Monte Carlo and Coupled Cluster Monte Carlo methods. From these analyses we describe a hybrid analysis method which provides reliable error estimates for series of all lengths. Equally important is the estimation of the appropriate start point of the equilibrated phase, and two heuristic schemes are tested, establishing that MSER (mean squared error rule) gives reasonable and constant estimations independent of the length of time-series. Copyright © 2019, The Authors. All rights reserved.

关键词： Time series

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Brain based BMIgap as a new tool to correlate obesity and neural alterations – a multicohort study

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Neuroscience Applied 2022年 1卷

作者： A. Khuntia R. Paul O.A. Andreassen F. Degenhard R. Eils J. Erdmann C. Herrmann M. Hofmann-Apitius T. Kaufmann A.T. Kodamullil S. Mucha M.M. Nöthen M.L. Pedersen A. Quintero H. Schunkert A. Sharma H. Tost L.T. Westlye Y. Zhang J. Kambeitz N. Koutsouleris 1Department of Psychiatry and Psychotherapy- Ludwig-Maximilian-University Psychiatry Munich Germany 2Max Plank Institute of Psychiatry Psychiatry Munich Germany 3Department of Psychiatry and Psychotherapy- Ludwig-Maximilian-University- Munich- Germany Department of Psychiatry and Psychotherapy München Germany 4Norwegian Centre for Mental Disorders Research NORMENT Division of Mental Health and Addiction Oslo Norway 5Institute of Human Genetics- University of Bonn School of Medicine Bonn Germany 6Health Data Science Unit Heidelberg University Hospital- and BioQuant Heidelberg Germany 7Institute for Cardiogenetics University of Lübeck Lübeck Germany 8Heidelberg University Hospital- and BioQuant Health Data Science Unit Heidelberg Germany 9Fraunhofer Institute for Algorithms and Scientific Computing SCAI Bioinformatics Sankt Augustin Germany 10Fraunhofer Institute for Algorithms and Scientific Computing SCAI- Bioinformatics Sankt Augustin Germany 11Institute for Cardiogenetics- University of Lübeck- DZHK Cardiogenetics Lübeck Germany 12Institute of Human Genetics- University of Bonn Human Genetics Bonn Germany 13University of Oslo Department of Psychology Oslo Norway 14Deutsches Herzzentrum München- Technische Universität München Department of Cardiology Munich Germany 15Heidelberg University Hospital- and BioQuant Health Data Science Unit- Heidelberg Germany 16Central Institute of Mental Health Department of Psychiatry and Psychotherapy- Mannheim Germany 17University of Cologne Department of Psychiatry and Psychotherapy- Cologne Germany 18University of Turku Department of Psychiatry- Turku Finland 19Neuroscience and Sense Organs- University of Bari Aldo Moro Department of Basic Medical Science Bari Italy 20Fondazione IRCCS Ca’ Granda Ospedale Maggiore Policlinico Department of Neurosciences and Mental Health Milano Italy 21Institute of Mental Health- University of Birmingham Psychiatry Birmingham United Kingdom 22University of Birmingham School of Psyc

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Tau-PET signal in Alzheimer’s disease is related to immune activation and synaptic signaling measured with CSF proteomics

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Alzheimer's & Dementia 2023年第S17期19卷

作者： Roos M. Rikken Eleonora M. Vromen Emma M. Coomans Denise Visser Frederik Barkhof Ronald Boellaard Sandeep S.V. Golla Bart N.M. van Berckel Rik Ossenkoppele Anouk den Braber Everard G.B. Vijverberg Wiesje M. van der Flier Yolande A.L. Pijnenburg Charlotte E. Teunissen Pieter Jelle Visser Elsmarieke van de Giessen Betty M. Tijms Amsterdam Neuroscience Brain Imaging Amsterdam Netherlands Radiology & Nuclear Medicine Vrije Universiteit Amsterdam Amsterdam UMC location VUmc Amsterdam Netherlands Correspondece Alzheimer Center Amsterdam Neurology Vrije Universiteit Amsterdam Amsterdam UMC location VUmc Amsterdam Netherlands Amsterdam Neuroscience Neurodegeneration Amsterdam Netherlands Queen Square Institute of Neurology and Centre for Medical Image Computing University College London United Kingdom Clinical Memory Research Unit Lund University Lund Sweden Biological Psychiatry Vrije Universiteit Amsterdam Amsterdam Netherlands Epidemiology and Data Science Vrije Universiteit Amsterdam Amsterdam UMC location VUmc Amsterdam Netherlands Amsterdam Neuroscience Neurodegeneration Amsterdam Noord-Holland Netherlands Alzheimer Center Amsterdam Neurology Vrije Universiteit Amsterdam Amsterdam UMC location VUmc Amsterdam Noord-Holland Netherlands Alzheimer Center Amsterdam Neurology Amsterdam UMC Amsterdam Netherlands Neurochemistry laboratory Clinical Chemistry Amsterdam UMC location VUmc Amsterdam Netherlands Center for Alzheimer Research Karolinska Institutet Division of Clinical Geriatrics Department of Neurobiology Care Sciences and Society Stockholm Sweden Alzheimer Center Limburg School for Mental Health and Neuroscience (MHeNs) Maastricht University Maastricht Netherlands

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Harmonized-Multinational qEEG norms (HarMNqEEG)

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NEUROIMAGE 2022年 256卷 119190-119190页

作者： Li, Min Wang, Ying Lopez-Naranjo, Carlos Hu, Shiang Reyes, Ronaldo Cesar Garcia Paz-Linares, Deirel Areces-Gonzalez, Ariosky Hamid, Aini Ismafairus Abd Evans, Alan C. Savostyanov, Alexander N. Calzada-Reyes, Ana Villringer, Arno Tobon-Quintero, Carlos A. Garcia-Agustin, Daysi Yao, Dezhong Dong, Li Aubert-Vazquez, Eduardo Reza, Faruque Razzaq, Fuleah Abdul Omar, Hazim Abdullah, Jafri Malin Galler, Janina R. Ochoa-Gomez, John F. Prichep, Leslie S. Galan-Garcia, Lidice Morales-Chacon, Lilia Valdes-Sosa, Mitchell J. Trondle, Marius Zulkifly, Mohd Faizal Mohd Rahman, Muhammad Riddha Bin Abdul Milakhina, Natalya S. Langer, Nicolas Rudych, Pavel Koenig, Thomas Virues-Alba, Trinidad A. Lei, Xu Bringas-Vega, Maria L. Bosch-Bayard, Jorge F. Valdes-Sosa, Pedro Antonio [a]The Clinical Hospital of Chengdu Brain Science Institute MOE Key Lab for Neuroinformation School of Life Science and Technology University of Electronic Science and Technology of China Chengdu China [b]Cuban Center for Neurocience La Habana Cuba [c]McGill Centre for Integrative Neuroscience Ludmer Centre for Neuroinformatics and Mental Health Montreal Neurological Institute Canada [d]Department of Neurosciences School of Medical Sciences Universiti Sains Malaysia Universiti Sains Malaysia Health Campus Kota Bharu Kelantan 16150 Malaysia [e]Brain and Behaviour Cluster School of Medical Sciences Universiti Sains Malaysia Health Campus Kota Bharu Kelantan 16150 Malaysia [f]Hospital Universiti Sains Malaysia Universiti Sains Malaysia Health Campus Kota Bharu Kelantan 16150 Malaysia [g]Humanitarian Institute Novosibirsk State University Novosibirsk 630090 Russia [h]Laboratory of Psychophysiology of Individual Differences Federal State Budgetary Scientific Institution Scientific Research Institute of Neurosciences and Medicine Novosibirsk 630117 Russia [i]Laboratory of Psychological Genetics at the Institute of Cytology and Genetics Siberian Branch of the Russian Academy of Sciences Novosibirsk 630090 Russia [j]University of Pinar del Río “Hermanos Saiz Montes de Oca” Pinar del Río Cuba [k]Department of Neurology Max Planck Institute for Human Cognitive and Brain Sciences Leipzig Germany [l]Department of Cognitive Neurology University Hospital Leipzig Leipzig Germany [m]Center for Stroke Research Charité-Universitätsmedizin Berlin Berlin Germany [n]Grupo Neuropsicología y Conducta - GRUNECO Faculty of Medicine Universidad de Antioquia Colombia [o]Research Department Institución Prestadora de Servicios de Salud IPS Universitaria Colombia [p]The Cuban center aging longevity and health Havana Cuba [q]Research Unit of NeuroInformation Chinese Academy of Medical Sciences Chengdu 2019RU035 China [r]School of Electrical Engineering Zhengzhou University Zhengzhou 4500

This paper extends frequency domain quantitative electroencephalography (qEEG) methods pursuing higher sensitivity to detect Brain Developmental Disorders. Prior qEEG work lacked integration of cross-spectral information omitting important functional connectivity descriptors. Lack of geographical diversity precluded accounting for site-specific variance, increasing qEEG nuisance variance. We ameliorate these weaknesses. (i) Create lifespan Riemannian multinational qEEG norms for cross-spectral tensors. These norms result from the HarMNqEEG project fostered by the Global Brain Consortium. We calculate the norms with data from 9 countries, 12 devices, and 14 studies, including 1564 subjects. Instead of raw data, only anonymized metadata and EEG cross spectral tensors were shared. After visual and automatic quality control, developmental equations for the mean and standard deviation of qEEG traditional and Riemannian DPs were calculated using additive mixed-effects models. We demonstrate qEEG "batch effects " and provide methods to calculate harmonized z-scores. (ii) We also show that harmonized Riemannian norms produce z-scores with increased diagnostic accuracy predicting brain dysfunction produced by malnutrition in the first year of life and detecting COVID induced brain dysfunction. (iii) We offer open code and data to calculate different individual z-scores from the HarMNqEEG dataset. These results contribute to developing bias-free, low-cost neuroimaging technologies applicable in various health settings.

关键词： Quantitative EEG EEG cross-spectrum Riemannian geometry Batch effects Z-score Harmonization Malnutrition Covid induced brain dysfunction Developmental Brain Chart

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Understanding the artificial intelligence clinician and optimal treatment strategies for sepsis in intensive care

arXiv

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arXiv 2019年

作者： Komorowski, Matthieu Celi, Leo A. Badawi, Omar Gordon, Anthony C. Aldo Faisal, A. Department of Surgery and Cancer Imperial College London London United Kingdom Department of Bioengineering Imperial College London London United Kingdom Laboratory of Computational Physiology Harvard-MIT Division of Health Sciences & Technology CambridgeMA United States Beth Israel Deaconess Medical Center BostonMA United States Patient Care Analytic Philips Healthcare BaltimoreMD United States Department of Pharmacy Practice and Science University of Maryland School of Pharmacy BaltimoreMD United States Centre in AI for Healthcare Imperial College London London United Kingdom Department of Computing Imperial College London London United Kingdom MRC London Institute of Medical Sciences London United Kingdom Behaviour Analytics Lab Data Science Institute London United Kingdom

In this document, we explore in more detail our published work (Komorowski, Celi, Badawi, Gordon, & Faisal, 2018) for the benefit of the AI in Healthcare research community. In the above paper, we developed the AI Clinician system, which demonstrated how reinforcement learning could be used to make useful recommendations towards optimal treatment decisions from intensive care data. Since publication a number of authors have reviewed our work (e.g. Abbasi, 2018;Bos, Azoulay, & Martin-Loeches, 2019;Saria, 2018). Given the difference of our framework to previous work, the fact that we are bridging two very different academic communities (intensive care and machine learning) and that our work has impact on a number of other areas with more traditional computer-based approaches (biosignal processing and control, biomedical engineering), we are providing here additional details on our recent publication. We acknowledge the online comments by Jeter et al (https://***/abs/1902.03271). The sections of the present document are structured so as to address some of their questions. For clarity, we label figures from our main Nature Medicine publication as "M", figures from Jeter et al.'s arXiv paper as "J" and figures from our response here as "R". Copyright © 2019, The Authors. All rights reserved.

关键词： Publishing

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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

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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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The evolutionary history of 2,658 cancers (vol 578, pg 122, 2020)

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NATURE 2023年第7948期614卷 E42-E42页

作者： Gerstung, Moritz Jolly, Clemency Leshchiner, Ignaty Dentro, Stefan C. Gonzalez, Santiago Rosebrock, Daniel Mitchell, Thomas J. Rubanova, Yulia Anur, Pavana Yu, Kaixian Tarabichi, Maxime Deshwar, Amit Wintersinger, Jeff Kleinheinz, Kortine Vazquez-Garcia, Ignacio Haase, Kerstin Jerman, Lara Sengupta, Subhajit Macintyre, Geoff Malikic, Salem Donmez, Nilgun Livitz, Dimitri G. Cmero, Marek Demeulemeester, Jonas Schumacher, Steven Fan, Yu Yao, Xiaotong Lee, Juhee Schlesner, Matthias Boutros, Paul C. Bowtell, David D. Zhu, Hongtu Getz, Gad Imielinski, Marcin Beroukhim, Rameen Sahinalp, S. Cenk Ji, Yuan Peifer, Martin Markowetz, Florian Mustonen, Ville Yuan, Ke Wang, Wenyi Morris, Quaid D. Spellman, Paul T. Wedge, David C. Van Loo, Peter European Molecular Biology Laboratory European Bioinformatics Institute (EMBL-EBI) Cambridge UK European Molecular Biology Laboratory Genome Biology Unit Heidelberg Germany Wellcome Sanger Institute Cambridge UK Genome Biology Unit European Molecular Biology Laboratory (EMBL) Heidelberg Germany University of Ljubljana Ljubljana Slovenia The Francis Crick Institute London UK Big Data Institute University of Oxford Oxford UK Wellcome Sanger Institute Wellcome Genome Campus Hinxton UK Big Data Institute Li Ka Shing Centre University of Oxford Oxford UK University of Cambridge Cambridge UK Cambridge University Hospitals NHS Foundation Trust Cambridge UK Department of Applied Mathematics and Theoretical Physics Centre for Mathematical Sciences University of Cambridge Cambridge UK Department of Epidemiology and Biostatistics Memorial Sloan Kettering Cancer Center New York NY USA Department of Statistics Columbia University New York NY USA Department of Haematology University of Cambridge Cambridge UK University of Leuven Leuven Belgium Broad Institute of MIT and Harvard Cambridge MA USA Center for Cancer Research Massachusetts General Hospital Charlestown MA USA Department of Pathology Massachusetts General Hospital Boston MA USA Harvard Medical School Boston MA USA Center for Cancer Research Massachusetts General Hospital Boston MA USA Dana-Farber Cancer Institute Boston MA USA Department of Medical Oncology Dana-Farber Cancer Institute Boston MA USA Department of Cancer Biology Dana-Farber Cancer Institute Boston MA USA Oxford NIHR Biomedical Research Centre Oxford UK Oxford NIHR Biomedical Research Centre University of Oxford Oxford UK University of Toronto Toronto Ontario Canada Vector Institute Toronto Ontario Canada Vector Institute Toronto ON Canada Department of Computer Science University of Toronto Toronto ON Canada Ontario Institute for Cancer Research Toronto Ontario Canada University of California Los Angeles

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