1
|
Mehta R, Filos A, Baid U, Sako C, McKinley R, Rebsamen M, Dätwyler K, Meier R, Radojewski P, Murugesan GK, Nalawade S, Ganesh C, Wagner B, Yu FF, Fei B, Madhuranthakam AJ, Maldjian JA, Daza L, Gómez C, Arbeláez P, Dai C, Wang S, Reynaud H, Mo Y, Angelini E, Guo Y, Bai W, Banerjee S, Pei L, AK M, Rosas-González S, Zemmoura I, Tauber C, Vu MH, Nyholm T, Löfstedt T, Ballestar LM, Vilaplana V, McHugh H, Maso Talou G, Wang A, Patel J, Chang K, Hoebel K, Gidwani M, Arun N, Gupta S, Aggarwal M, Singh P, Gerstner ER, Kalpathy-Cramer J, Boutry N, Huard A, Vidyaratne L, Rahman MM, Iftekharuddin KM, Chazalon J, Puybareau E, Tochon G, Ma J, Cabezas M, Llado X, Oliver A, Valencia L, Valverde S, Amian M, Soltaninejad M, Myronenko A, Hatamizadeh A, Feng X, Dou Q, Tustison N, Meyer C, Shah NA, Talbar S, Weber MA, Mahajan A, Jakab A, Wiest R, Fathallah-Shaykh HM, Nazeri A, Milchenko1 M, Marcus D, Kotrotsou A, Colen R, Freymann J, Kirby J, Davatzikos C, Menze B, Bakas S, Gal Y, Arbel T. QU-BraTS: MICCAI BraTS 2020 Challenge on Quantifying Uncertainty in Brain Tumor Segmentation - Analysis of Ranking Scores and Benchmarking Results. J Mach Learn Biomed Imaging 2022; 2022:https://www.melba-journal.org/papers/2022:026.html. [PMID: 36998700 PMCID: PMC10060060] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Subscribe] [Scholar Register] [Indexed: 04/01/2023]
Abstract
Deep learning (DL) models have provided state-of-the-art performance in various 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 translating 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 toward clinical translation. Several uncertainty estimation methods have recently been introduced for DL medical image segmentation tasks. Developing scores 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 score developed during the BraTS 2019 and BraTS 2020 task on uncertainty quantification (QU-BraTS) and designed to assess and rank uncertainty estimates for brain tumor multi-compartment segmentation. This score (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 percentage of under-confident correct assertions. We further benchmark the segmentation uncertainties generated by 14 independent participating teams of QU-BraTS 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, highlighting 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-BraTS.
Collapse
Affiliation(s)
- Raghav Mehta
- Centre for Intelligent Machines (CIM), McGill University, Montreal, QC, Canada
| | - Angelos Filos
- Oxford Applied and Theoretical Machine Learning (OATML) Group, University of Oxford, Oxford, England
| | - Ujjwal Baid
- Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Philadelphia, PA, USA
- Department of Radiology, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA, USA
- Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Chiharu Sako
- Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Philadelphia, PA, USA
- Department of Radiology, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA, USA
| | - Richard McKinley
- Support Center for Advanced Neuroimaging (SCAN), University Institute of Diagnostic and Interventional Neuroradiology, University of Bern, Inselspital, Bern University Hospital, Bern, Switzerland
| | - Michael Rebsamen
- Support Center for Advanced Neuroimaging (SCAN), University Institute of Diagnostic and Interventional Neuroradiology, University of Bern, Inselspital, Bern University Hospital, Bern, Switzerland
| | - Katrin Dätwyler
- Support Center for Advanced Neuroimaging (SCAN), University Institute of Diagnostic and Interventional Neuroradiology, University of Bern, Inselspital, Bern University Hospital, Bern, Switzerland
- Human Performance Lab, Schulthess Clinic, Zurich, Switzerland
| | | | - Piotr Radojewski
- Support Center for Advanced Neuroimaging (SCAN), University Institute of Diagnostic and Interventional Neuroradiology, University of Bern, Inselspital, Bern University Hospital, Bern, Switzerland
| | | | - Sahil Nalawade
- Department of Radiology, University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - Chandan Ganesh
- Department of Radiology, University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - Ben Wagner
- Department of Radiology, University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - Fang F. Yu
- Department of Radiology, University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - Baowei Fei
- Department of Bioengineering, University of Texas at Dallas, Texas, USA
| | - Ananth J. Madhuranthakam
- Department of Radiology, University of Texas Southwestern Medical Center, Dallas, TX, USA
- Advanced Imaging Research Center, University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - Joseph A. Maldjian
- Department of Radiology, University of Texas Southwestern Medical Center, Dallas, TX, USA
- Advanced Imaging Research Center, University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - Laura Daza
- Universidad de los Andes, Bogotá, Colombia
| | | | | | - Chengliang Dai
- Data Science Institute, Imperial College London, London, UK
| | - Shuo Wang
- Data Science Institute, Imperial College London, London, UK
| | | | - Yuanhan Mo
- Data Science Institute, Imperial College London, London, UK
| | - Elsa Angelini
- NIHR Imperial BRC, ITMAT Data Science Group, Imperial College London, London, UK
| | - Yike Guo
- Data Science Institute, Imperial College London, London, UK
| | - Wenjia Bai
- Data Science Institute, Imperial College London, London, UK
- Department of Brain Sciences, Imperial College London, London, UK
| | - Subhashis Banerjee
- Machine Intelligence Unit, Indian Statistical Institute, Kolkata, India
- Department of CSE, University of Calcutta, Kolkata, India
- Division of Visual Information and Interaction (Vi2), Department of Information Technology, Uppsala University, Uppsala, Sweden
| | - Linmin Pei
- Department of Diagnostic Radiology, The University of Pittsburgh Medical Center, Pittsburgh, PA, USA
| | - Murat AK
- Department of Diagnostic Radiology, The University of Pittsburgh Medical Center, Pittsburgh, PA, USA
| | | | - Ilyess Zemmoura
- UMR U1253 iBrain, Université de Tours, Inserm, Tours, France
- Neurosurgery department, CHRU de Tours, Tours, France
| | - Clovis Tauber
- UMR U1253 iBrain, Université de Tours, Inserm, Tours, France
| | - Minh H. Vu
- Department of Radiation Sciences, Umeå University, Umeå, Sweden
| | - Tufve Nyholm
- Department of Radiation Sciences, Umeå University, Umeå, Sweden
| | - Tommy Löfstedt
- Department of Computing Science, Umeå University, Umeå, Sweden
| | - Laura Mora Ballestar
- Signal Theory and Communications Department, Universitat Politècnica de Catalunya, BarcelonaTech, Barcelona, Spain
| | - Veronica Vilaplana
- Signal Theory and Communications Department, Universitat Politècnica de Catalunya, BarcelonaTech, Barcelona, Spain
| | - Hugh McHugh
- Faculty of Medical and Health Sciences, University of Auckland, Auckland, New Zealand
- Radiology Department, Auckland City Hospital, Auckland, New Zealand
| | | | - Alan Wang
- Faculty of Medical and Health Sciences, University of Auckland, Auckland, New Zealand
- Auckland Bioengineering Institute, University of Auckland, New Zealand
| | - Jay Patel
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Boston, MA, USA
- Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Ken Chang
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Boston, MA, USA
- Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Katharina Hoebel
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Boston, MA, USA
- Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Mishka Gidwani
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Boston, MA, USA
| | - Nishanth Arun
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Boston, MA, USA
| | - Sharut Gupta
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Boston, MA, USA
| | - Mehak Aggarwal
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Boston, MA, USA
| | - Praveer Singh
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Boston, MA, USA
| | - Elizabeth R. Gerstner
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Boston, MA, USA
| | - Jayashree Kalpathy-Cramer
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Boston, MA, USA
| | - Nicolas Boutry
- EPITA Research and Development Laboratory (LRDE), France
| | - Alexis Huard
- EPITA Research and Development Laboratory (LRDE), France
| | - Lasitha Vidyaratne
- Vision Lab, Electrical and Computer Engineering, Old Dominion University, Norfolk, VA 23529, USA
| | - Md Monibor Rahman
- Vision Lab, Electrical and Computer Engineering, Old Dominion University, Norfolk, VA 23529, USA
| | - Khan M. Iftekharuddin
- Vision Lab, Electrical and Computer Engineering, Old Dominion University, Norfolk, VA 23529, USA
| | - Joseph Chazalon
- EPITA Research and Development Laboratory (LRDE), Le Kremlin-Biĉetre, France
| | - Elodie Puybareau
- EPITA Research and Development Laboratory (LRDE), Le Kremlin-Biĉetre, France
| | - Guillaume Tochon
- EPITA Research and Development Laboratory (LRDE), Le Kremlin-Biĉetre, France
| | - Jun Ma
- School of Science, Nanjing University of Science and Technology
| | - Mariano Cabezas
- Research Institute of Computer Vision and Robotics, University of Girona, Spain
| | - Xavier Llado
- Research Institute of Computer Vision and Robotics, University of Girona, Spain
| | - Arnau Oliver
- Research Institute of Computer Vision and Robotics, University of Girona, Spain
| | - Liliana Valencia
- Research Institute of Computer Vision and Robotics, University of Girona, Spain
| | - Sergi Valverde
- Research Institute of Computer Vision and Robotics, University of Girona, Spain
| | - Mehdi Amian
- Department of Electrical and Computer Engineering, University of Tehran, Iran
| | | | | | | | - Xue Feng
- Biomedical Engineering, University of Virginia, Charlottesville, USA
| | - Quan Dou
- Biomedical Engineering, University of Virginia, Charlottesville, USA
| | - Nicholas Tustison
- Radiology and Medical Imaging, University of Virginia, Charlottesville, USA
| | - Craig Meyer
- Biomedical Engineering, University of Virginia, Charlottesville, USA
- Radiology and Medical Imaging, University of Virginia, Charlottesville, USA
| | - Nisarg A. Shah
- Department of Electrical Engineering, Indian Institute of Technology - Jodhpur, Jodhpur, India
| | - Sanjay Talbar
- SGGS Institute of Engineering and Technology, Nanded, India
| | - Marc-André Weber
- Institute of Diagnostic and Interventional Radiology, Pediatric Radiology and Neuroradiology, University Medical Center Rostock, Rostock, Germany
| | - Abhishek Mahajan
- Tata Memorial Centre, Homi Bhabha National Institute, Mumbai, India
| | - Andras Jakab
- Center for MR-Research, University Children’s Hospital Zurich, Zurich, Switzerland
| | - Roland Wiest
- Support Center for Advanced Neuroimaging (SCAN), University Institute of Diagnostic and Interventional Neuroradiology, University of Bern, Inselspital, Bern University Hospital, Bern, Switzerland
- Institute for Surgical Technology and Biomechanics, University of Bern, Bern, Switzerland
| | | | - Arash Nazeri
- Department of Radiology, Washington University, St. Louis, MO, USA
| | - Mikhail Milchenko1
- Department of Radiology, Washington University, St. Louis, MO, USA
- Neuroimaging Informatics and Analysis Center, Washington University, St. Louis, MO, USA
| | - Daniel Marcus
- Department of Radiology, Washington University, St. Louis, MO, USA
- Neuroimaging Informatics and Analysis Center, Washington University, St. Louis, MO, USA
| | - Aikaterini Kotrotsou
- Department of Diagnostic Radiology, University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Rivka Colen
- Department of Diagnostic Radiology, University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - John Freymann
- Leidos Biomedical Research, Inc, Frederick National Laboratory for Cancer Research, Frederick, MD, USA
- Cancer Imaging Program, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Justin Kirby
- Leidos Biomedical Research, Inc, Frederick National Laboratory for Cancer Research, Frederick, MD, USA
- Cancer Imaging Program, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Christos Davatzikos
- Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Philadelphia, PA, USA
- Department of Radiology, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA, USA
| | - Bjoern Menze
- Department of Quantitative Biomedicine, University of Zurich, Zurich, Switzerland
- Department of Informatics, Technical University of Munich, Munich, Germany
| | - Spyridon Bakas
- Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Philadelphia, PA, USA
- Department of Radiology, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA, USA
- Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Yarin Gal
- Oxford Applied and Theoretical Machine Learning (OATML) Group, University of Oxford, Oxford, England
| | - Tal Arbel
- Centre for Intelligent Machines (CIM), McGill University, Montreal, QC, Canada
- MILA - Quebec Artificial Intelligence Institute, Montreal, QC, Canada
| |
Collapse
|
2
|
Yogananda CGB, Shah BR, Nalawade SS, Murugesan GK, Yu FF, Pinho MC, Wagner BC, Mickey B, Patel TR, Fei B, Madhuranthakam AJ, Maldjian JA. MRI-Based Deep-Learning Method for Determining Glioma MGMT Promoter Methylation Status. AJNR Am J Neuroradiol 2021; 42:845-852. [PMID: 33664111 PMCID: PMC8115363 DOI: 10.3174/ajnr.a7029] [Citation(s) in RCA: 31] [Impact Index Per Article: 10.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/24/2020] [Accepted: 11/21/2020] [Indexed: 01/14/2023]
Abstract
BACKGROUND AND PURPOSE O6-Methylguanine-DNA methyltransferase (MGMT) promoter methylation confers an improved prognosis and treatment response in gliomas. We developed a deep learning network for determining MGMT promoter methylation status using T2 weighted Images (T2WI) only. MATERIALS AND METHODS Brain MR imaging and corresponding genomic information were obtained for 247 subjects from The Cancer Imaging Archive and The Cancer Genome Atlas. One hundred sixty-three subjects had a methylated MGMT promoter. A T2WI-only network (MGMT-net) was developed to determine MGMT promoter methylation status and simultaneous single-label tumor segmentation. The network was trained using 3D-dense-UNets. Three-fold cross-validation was performed to generalize the performance of the networks. Dice scores were computed to determine tumor-segmentation accuracy. RESULTS The MGMT-net demonstrated a mean cross-validation accuracy of 94.73% across the 3 folds (95.12%, 93.98%, and 95.12%, [SD, 0.66%]) in predicting MGMT methylation status with a sensitivity and specificity of 96.31% [SD, 0.04%] and 91.66% [SD, 2.06%], respectively, and a mean area under the curve of 0.93 [SD, 0.01]. The whole tumor-segmentation mean Dice score was 0.82 [SD, 0.008]. CONCLUSIONS We demonstrate high classification accuracy in predicting MGMT promoter methylation status using only T2WI. Our network surpasses the sensitivity, specificity, and accuracy of histologic and molecular methods. This result represents an important milestone toward using MR imaging to predict prognosis and treatment response.
Collapse
Affiliation(s)
- C G B Yogananda
- From the Advanced Neuroscience Imaging Research Lab (C.G.B.Y., B.R.S., S.S.N., G.K.M., F.F.Y., M.C.P., B.C.W., A.J.M., J.A.M.), Department of Radiology, University of Texas Southwestern Medical Center, Dallas, Texas
| | - B R Shah
- From the Advanced Neuroscience Imaging Research Lab (C.G.B.Y., B.R.S., S.S.N., G.K.M., F.F.Y., M.C.P., B.C.W., A.J.M., J.A.M.), Department of Radiology, University of Texas Southwestern Medical Center, Dallas, Texas
| | - S S Nalawade
- From the Advanced Neuroscience Imaging Research Lab (C.G.B.Y., B.R.S., S.S.N., G.K.M., F.F.Y., M.C.P., B.C.W., A.J.M., J.A.M.), Department of Radiology, University of Texas Southwestern Medical Center, Dallas, Texas
| | - G K Murugesan
- From the Advanced Neuroscience Imaging Research Lab (C.G.B.Y., B.R.S., S.S.N., G.K.M., F.F.Y., M.C.P., B.C.W., A.J.M., J.A.M.), Department of Radiology, University of Texas Southwestern Medical Center, Dallas, Texas
| | - F F Yu
- From the Advanced Neuroscience Imaging Research Lab (C.G.B.Y., B.R.S., S.S.N., G.K.M., F.F.Y., M.C.P., B.C.W., A.J.M., J.A.M.), Department of Radiology, University of Texas Southwestern Medical Center, Dallas, Texas
| | - M C Pinho
- From the Advanced Neuroscience Imaging Research Lab (C.G.B.Y., B.R.S., S.S.N., G.K.M., F.F.Y., M.C.P., B.C.W., A.J.M., J.A.M.), Department of Radiology, University of Texas Southwestern Medical Center, Dallas, Texas
| | - B C Wagner
- From the Advanced Neuroscience Imaging Research Lab (C.G.B.Y., B.R.S., S.S.N., G.K.M., F.F.Y., M.C.P., B.C.W., A.J.M., J.A.M.), Department of Radiology, University of Texas Southwestern Medical Center, Dallas, Texas
| | - B Mickey
- Department of Neurological Surgery (B.M., T.R.P.), University of Texas Southwestern Medical Center, Dallas, Texas
| | - T R Patel
- Department of Neurological Surgery (B.M., T.R.P.), University of Texas Southwestern Medical Center, Dallas, Texas
| | - B Fei
- Department of Bioengineering (B.F.), University of Texas at Dallas, Richardson, Texas
| | - A J Madhuranthakam
- From the Advanced Neuroscience Imaging Research Lab (C.G.B.Y., B.R.S., S.S.N., G.K.M., F.F.Y., M.C.P., B.C.W., A.J.M., J.A.M.), Department of Radiology, University of Texas Southwestern Medical Center, Dallas, Texas
| | - J A Maldjian
- From the Advanced Neuroscience Imaging Research Lab (C.G.B.Y., B.R.S., S.S.N., G.K.M., F.F.Y., M.C.P., B.C.W., A.J.M., J.A.M.), Department of Radiology, University of Texas Southwestern Medical Center, Dallas, Texas
| |
Collapse
|