QU-BraTS: MICCAI BraTS 2020 challenge on quantifying uncertainty in brain tumor segmentation -- analysis of ranking metrics and benchmarking results

Deep learning (DL) models have provided the state-of-the-art performance in a wide variety of medical imaging benchmarking challenges, including the Brain Tumor Segmentation (BraTS) challenges. However, the task of focal pathology multi-compartment segmentation (e.g., tumor and lesion sub-regions) is particularly challenging, and potential errors hinder the translation of DL models into clinical workflows. Quantifying the reliability of DL model predictions in the form of uncertainties, could enable clinical review of the most uncertain regions, thereby building trust and paving the way towards clinical translation. Recently, a number of uncertainty estimation methods have been introduced for DL medical image segmentation tasks. Developing metrics to evaluate and compare the performance of uncertainty measures will assist the end-user in making more informed decisions. In this study, we explore and evaluate a metric developed during the BraTS 2019-2020 task on uncertainty quantification (QU-BraTS), and designed to assess and rank uncertainty estimates for brain tumor multi-compartment segmentation. This metric (1) rewards uncertainty estimates that produce high confidence in correct assertions, and those that assign low confidence levels at incorrect assertions, and (2) penalizes uncertainty measures that lead to a higher percentages of under-confident correct assertions. We further benchmark the segmentation uncertainties generated by 14 independent participating teams of QUBraTS 2020, all of which also participated in the main BraTS segmentation task. Overall, our findings confirm the importance and complementary value that uncertainty estimates provide to segmentation algorithms, and hence highlight the need for uncertainty quantification in medical image analyses. Finally, in favor of transparency and reproducibility our evaluation code is made publicly available at https://github.com/RagMeh11/QU-BraTSResearch reported in this publication was partly supported by the Informatics Technology for Cancer Research (ITCR) program of the National Cancer Institute (NCI) of the National Institutes of Health (NIH), under award numbers NIH/NCI/ITCR:U01CA242871 and NIH/NCI/ITCR:U24CA189523. It was also partly supported by the National Institute of Neurological Disorders and Stroke (NINDS) of the NIH, under award number NIH/NINDS:R01NS042645.Document signat per 92 autors/autores: Raghav Mehta1 , Angelos Filos2 , Ujjwal Baid3,4,5 , Chiharu Sako3,4 , Richard McKinley6 , Michael Rebsamen6 , Katrin D¨atwyler6,53, Raphael Meier54, Piotr Radojewski6 , Gowtham Krishnan Murugesan7 , Sahil Nalawade7 , Chandan Ganesh7 , Ben Wagner7 , Fang F. Yu7 , Baowei Fei8 , Ananth J. Madhuranthakam7,9 , Joseph A. Maldjian7,9 , Laura Daza10, Catalina Gómez10, Pablo Arbeláez10, Chengliang Dai11, Shuo Wang11, Hadrien Raynaud11, Yuanhan Mo11, Elsa Angelini12, Yike Guo11, Wenjia Bai11,13, Subhashis Banerjee14,15,16, Linmin Pei17, Murat AK17, Sarahi Rosas-González18, Illyess Zemmoura18,52, Clovis Tauber18 , Minh H. Vu19, Tufve Nyholm19, Tommy L¨ofstedt20, Laura Mora Ballestar21, Veronica Vilaplana21, Hugh McHugh22,23, Gonzalo Maso Talou24, Alan Wang22,24, Jay Patel25,26, Ken Chang25,26, Katharina Hoebel25,26, Mishka Gidwani25, Nishanth Arun25, Sharut Gupta25 , Mehak Aggarwal25, Praveer Singh25, Elizabeth R. Gerstner25, Jayashree Kalpathy-Cramer25 , Nicolas Boutry27, Alexis Huard27, Lasitha Vidyaratne28, Md Monibor Rahman28, Khan M. Iftekharuddin28, Joseph Chazalon29, Elodie Puybareau29, Guillaume Tochon29, Jun Ma30 , Mariano Cabezas31, Xavier Llado31, Arnau Oliver31, Liliana Valencia31, Sergi Valverde31 , Mehdi Amian32, Mohammadreza Soltaninejad33, Andriy Myronenko34, Ali Hatamizadeh34 , Xue Feng35, Quan Dou35, Nicholas Tustison36, Craig Meyer35,36, Nisarg A. Shah37, Sanjay Talbar38, Marc-Andr Weber39, Abhishek Mahajan48, Andras Jakab47, Roland Wiest6,46 Hassan M. Fathallah-Shaykh45, Arash Nazeri40, Mikhail Milchenko140,44, Daniel Marcus40,44 , Aikaterini Kotrotsou43, Rivka Colen43, John Freymann41,42, Justin Kirby41,42, Christos Davatzikos3,4 , Bjoern Menze49,50, Spyridon Bakas∗3,4,5 , Yarin Gal∗2 , Tal Arbel∗1,51 // 1Centre for Intelligent Machines (CIM), McGill University, Montreal, QC, Canada, 2Oxford Applied and Theoretical Machine Learning (OATML) Group, University of Oxford, Oxford, England, 3Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Philadelphia, PA, USA, 4Department of Radiology, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA, USA, 5Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA, 6Support Center for Advanced Neuroimaging (SCAN), University Institute of Diagnostic and Interventional Neuroradiology, University of Bern, Inselspital, Bern University Hospital, Bern, Switzerland, 7Department of Radiology, University of Texas Southwestern Medical Center, Dallas, TX, USA, 8Department of Bioengineering, University of Texas at Dallas, Texas, USA, 9Advanced Imaging Research Center, University of Texas Southwestern Medical Center, Dallas, TX, USA, 10Universidad de los Andes, Bogotá, Colombia, 11Data Science Institute, Imperial College London, London, UK, 12NIHR Imperial BRC, ITMAT Data Science Group, Imperial College London, London, UK, 13Department of Brain Sciences, Imperial College London, London, UK, 14Machine Intelligence Unit, Indian Statistical Institute, Kolkata, India, 15Department of CSE, University of Calcutta, Kolkata, India, 16 Division of Visual Information and Interaction (Vi2), Department of Information Technology, Uppsala University, Uppsala, Sweden, 17Department of Diagnostic Radiology, The University of Pittsburgh Medical Center, Pittsburgh, PA, USA, 18UMR U1253 iBrain, Université de Tours, Inserm, Tours, France, 19Department of Radiation Sciences, Ume˚a University, Ume˚a, Sweden, 20Department of Computing Science, Ume˚a University, Ume˚a, Sweden, 21Signal Theory and Communications Department, Universitat Politècnica de Catalunya, BarcelonaTech, Barcelona, Spain, 22Faculty of Medical and Health Sciences, University of Auckland, Auckland, New Zealand, 23Radiology Department, Auckland City Hospital, Auckland, New Zealand, 24Auckland Bioengineering Institute, University of Auckland, New Zealand, 25Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Boston, MA, USA, 26Massachusetts Institute of Technology, Cambridge, MA, USA, 27EPITA Research and Development Laboratory (LRDE), France, 28Vision Lab, Electrical and Computer Engineering, Old Dominion University, Norfolk, VA 23529, USA, 29EPITA Research and Development Laboratory (LRDE), Le Kremlin-Bicˆetre, France, 30School of Science, Nanjing University of Science and Technology, 31Research Institute of Computer Vision and Robotics, University of Girona, Spain, 32Department of Electrical and Computer Engineering, University of Tehran, Iran, 33School of Computer Science, University of Nottingham, UK, 34NVIDIA, Santa Clara, CA, US, 35Biomedical Engineering, University of Virginia, Charlottesville, USA, 36Radiology and Medical Imaging, University of Virginia, Charlottesville, USA, 37Department of Electrical Engineering, Indian Institute of Technology - Jodhpur, Jodhpur, India, 38SGGS ©2021 Mehta et al.. License: CC-BY 4.0. arXiv:2112.10074v1 [eess.IV] 19 Dec 2021 Mehta et al. Institute of Engineering and Technology, Nanded, India, 39Institute of Diagnostic and Interventional Radiology, Pediatric Radiology and Neuroradiology, University Medical Center, 40Department of Radiology, Washington University, St. Louis, MO, USA, 41Leidos Biomedical Research, Inc, Frederick National Laboratory for Cancer Research, Frederick, MD, USA, 42Cancer Imaging Program, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA, 43Department of Diagnostic Radiology, University of Texas MD Anderson Cancer Center, Houston, TX, USA, 44Neuroimaging Informatics and Analysis Center, Washington University, St. Louis, MO, USA, 45Department of Neurology, The University of Alabama at Birmingham, Birmingham, AL, USA, 46Institute for Surgical Technology and Biomechanics, University of Bern, Bern, Switzerland, 47Center for MR-Research, University Children’s Hospital Zurich, Zurich, Switzerland, 48Tata Memorial Centre, Homi Bhabha National Institute, Mumbai, India, 49Department of Quantitative Biomedicine, University of Zurich, Zurich, Switzerland, 50Department of Informatics, Technical University of Munich, Munich, Germany, 51MILA - Quebec Artificial Intelligence Institute, Montreal, QC, Canada, 52Neurosurgery department, CHRU de Tours, Tours, France, 53 Human Performance Lab, Schulthess Clinic, Zurich, Switzerland, 54 armasuisse S+T, Thun, Switzerland.Preprin

児童虐待対応における家族支援に関する小学校養護教諭の役割認識

【目的】本研究の目的は，養護教諭の児童虐待対応における家族支援に対する役割認識と今後学校における児童虐 待対応の家族支援の在り方について検討することである． 【方法】A 県内の公立小学校に勤務する11名の養護教諭を対象に2011年6月～8月にインタビュー調査を行い，質的 帰納的分析を行った． 【結果】養護教諭の児童虐待対応における家族支援の役割認識は，【防止教育の充実を図る】【相談しやすい環境を 整える】【校内外で連携して継続的な支援をする】【早期発見と対応】【保護者の負担を軽減する】【保護者との信頼 関係を築く】の6つのカテゴリーで整理された．小学校に勤務する養護教諭は虐待の予防から早期発見と保護者の 負担が軽減するような子育て支援を含む継続的な対応という役割があると認識していることが明らかになった． 【結論】今後，養護教諭の児童虐待対応における家族支援を進めていくためには，養護教諭の学校等における適切 な児童虐待防止の取組をサポートしてもらえる地域や社会の支援体制の構築や研修体制の充実が喫緊の課題であ る．今回明らかになった結果を踏まえて，調査対象を拡大する等，さらなる研究の蓄積をする必要がある．Purpose 　This study aimed to investigate how Yogo teachers perceive their roles in providing support to families in child abuse cases and what future forms of family support should be provided in cases of child abuse. Method 　Interviews were conducted with 11 Yogo teachers employed at public elementary schools in Prefecture A from June to August 2011, and a qualitative inductive analysis of the interview data was conducted. Results 　Roles perceived by Yogo teachers for providing support to families in child abuse cases were categorized into the following six categories: working to enhance preventive education. creating an environment conducive to consultation. out-ofschool connections and continuous support. early detection of abuse and addressing it. reducing the burden on guardians. and building trust relationships with guardians. Yogo teachers perceived their roles to include prevention, early detection, and ongoing work to address of child abuse, such as providing child-rearing support to reduce the parenting burden on guardians. Conclusion 　Pressing topics for the future include improving training programs and constructing community support and social support systems for helping Yogo teachers take appropriate preventive measures against child abuse.　In light of these study results, further accumulation of research results through expansion of the surveyed population is needed

Brain extraction on MRI scans in presence of diffuse glioma: Multi-institutional performance evaluation of deep learning methods and robust modality-agnostic training

Brain extraction, or skull-stripping, is an essential pre-processing step in neuro-imaging that has a direct impact on the quality of all subsequent processing and analyses steps. It is also a key requirement in multi-institutional collaborations to comply with privacy-preserving regulations. Existing automated methods, including Deep Learning (DL) based methods that have obtained state-of-the-art results in recent years, have primarily targeted brain extraction without considering pathologically-affected brains. Accordingly, they perform sub-optimally when applied on magnetic resonance imaging (MRI) brain scans with apparent pathologies such as brain tumors. Furthermore, existing methods focus on using only T1-weighted MRI scans, even though multi-parametric MRI (mpMRI) scans are routinely acquired for patients with suspected brain tumors. In this study, we present a comprehensive performance evaluation of recent deep learning architectures for brain extraction, training models on mpMRI scans of pathologically-affected brains, with a particular focus on seeking a practically-applicable, low computational footprint approach, generalizable across multiple institutions, further facilitating collaborations. We identified a large retrospective multi-institutional dataset of n=3340 mpMRI brain tumor scans, with manually-inspected and approved gold-standard segmentations, acquired during standard clinical practice under varying acquisition protocols, both from private institutional data and public (TCIA) collections. To facilitate optimal utilization of rich mpMRI data, we further introduce and evaluate a novel ‘‘modality-agnostic training’’ technique that can be applied using any available modality, without need for model retraining. Our results indicate that the modality-agnostic approach1 obtains accurate results, providing a generic and practical tool for brain extraction on scans with brain tumors

Federated learning enables big data for rare cancer boundary detection.

Although machine learning (ML) has shown promise across disciplines, out-of-sample generalizability is concerning. This is currently addressed by sharing multi-site data, but such centralization is challenging/infeasible to scale due to various limitations. Federated ML (FL) provides an alternative paradigm for accurate and generalizable ML, by only sharing numerical model updates. Here we present the largest FL study to-date, involving data from 71 sites across 6 continents, to generate an automatic tumor boundary detector for the rare disease of glioblastoma, reporting the largest such dataset in the literature (n = 6, 314). We demonstrate a 33% delineation improvement for the surgically targetable tumor, and 23% for the complete tumor extent, over a publicly trained model. We anticipate our study to: 1) enable more healthcare studies informed by large diverse data, ensuring meaningful results for rare diseases and underrepresented populations, 2) facilitate further analyses for glioblastoma by releasing our consensus model, and 3) demonstrate the FL effectiveness at such scale and task-complexity as a paradigm shift for multi-site collaborations, alleviating the need for data-sharing

Author Correction: Federated learning enables big data for rare cancer boundary detection.

10.1038/s41467-023-36188-7NATURE COMMUNICATIONS14

Federated Learning Enables Big Data for Rare Cancer Boundary Detection

Although machine learning (ML) has shown promise across disciplines, out-of-sample generalizability is concerning. This is currently addressed by sharing multi-site data, but such centralization is challenging/infeasible to scale due to various limitations. Federated ML (FL) provides an alternative paradigm for accurate and generalizable ML, by only sharing numerical model updates. Here we present the largest FL study to-date, involving data from 71 sites across 6 continents, to generate an automatic tumor boundary detector for the rare disease of glioblastoma, reporting the largest such dataset in the literature (n = 6, 314). We demonstrate a 33% delineation improvement for the surgically targetable tumor, and 23% for the complete tumor extent, over a publicly trained model. We anticipate our study to: 1) enable more healthcare studies informed by large diverse data, ensuring meaningful results for rare diseases and underrepresented populations, 2) facilitate further analyses for glioblastoma by releasing our consensus model, and 3) demonstrate the FL effectiveness at such scale and task-complexity as a paradigm shift for multi-site collaborations, alleviating the need for data-sharing

MRI studies in two cases of hypertensive encephalopathy

Magnetic resonance imaging (MRI) findings were analyzed in two patients with hypertensive encephalopathy. MRI demonstrated focal cortical and subcortical lesions of hyperintense T 2 signal and hypointense Tl signal lesions with diffuse brain swelling. Focal lesions were hardly explained by involvements of major arterial supplies. There were no neurological focal signs suggesting dysfunctions in the abnormal areas of MRI. These MRI studies further support the hypothesis that hypertensive encephalopathy is induced by vasogenic edema during breakthrough of cerebral autoregulation. Prompt diagnosis and reduction of blood pressure are key points for improving the clinical condition. MRI better defines the cerebral involvements in detail and would help proper diagnosis and therapeutic decision

The federated tumor segmentation (FeTS) tool: an open-source solution to further solid tumor research

Objective. De-centralized data analysis becomes an increasingly preferred option in the healthcare domain, as it alleviates the need for sharing primary patient data across collaborating institutions. This highlights the need for consistent harmonized data curation, pre-processing, and identification of regions of interest based on uniform criteria. Approach. Towards this end, this manuscript describes the Federated Tumor Segmentation (FeTS) tool, in terms of software architecture and functionality. Main results. The primary aim of the FeTS tool is to facilitate this harmonized processing and the generation of gold standard reference labels for tumor sub-compartments on brain magnetic resonance imaging, and further enable federated training of a tumor sub-compartment delineation model across numerous sites distributed across the globe, without the need to share patient data. Significance. Building upon existing open-source tools such as the Insight Toolkit and Qt, the FeTS tool is designed to enable training deep learning models targeting tumor delineation in either centralized or federated settings. The target audience of the FeTS tool is primarily the computational researcher interested in developing federated learning models, and interested in joining a global federation towards this effort. The tool is open sourced at https://github.com/FETS-AI/Front-End