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Spaide T, Jiang J, Patil J, Anegondi N, Steffen V, Kawczynski MG, Newton EM, Rabe C, Gao SS, Lee AY, Holz FG, Sadda S, Schmitz-Valckenberg S, Ferrara D. Geographic Atrophy Segmentation Using Multimodal Deep Learning. Transl Vis Sci Technol 2023; 12:10. [PMID: 37428131 DOI: 10.1167/tvst.12.7.10] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/11/2023] Open
Abstract
Purpose To examine deep learning (DL)-based methods for accurate segmentation of geographic atrophy (GA) lesions using fundus autofluorescence (FAF) and near-infrared (NIR) images. Methods This retrospective analysis utilized imaging data from study eyes of patients enrolled in Proxima A and B (NCT02479386; NCT02399072) natural history studies of GA. Two multimodal DL networks (UNet and YNet) were used to automatically segment GA lesions on FAF; segmentation accuracy was compared with annotations by experienced graders. The training data set comprised 940 image pairs (FAF and NIR) from 183 patients in Proxima B; the test data set comprised 497 image pairs from 154 patients in Proxima A. Dice coefficient scores, Bland-Altman plots, and Pearson correlation coefficient (r) were used to assess performance. Results On the test set, Dice scores for the DL network to grader comparison ranged from 0.89 to 0.92 for screening visit; Dice score between graders was 0.94. GA lesion area correlations (r) for YNet versus grader, UNet versus grader, and between graders were 0.981, 0.959, and 0.995, respectively. Longitudinal GA lesion area enlargement correlations (r) for screening to 12 months (n = 53) were lower (0.741, 0.622, and 0.890, respectively) compared with the cross-sectional results at screening. Longitudinal correlations (r) from screening to 6 months (n = 77) were even lower (0.294, 0.248, and 0.686, respectively). Conclusions Multimodal DL networks to segment GA lesions can produce accurate results comparable with expert graders. Translational Relevance DL-based tools may support efficient and individualized assessment of patients with GA in clinical research and practice.
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Affiliation(s)
- Theodore Spaide
- Roche Personalized Healthcare, Genentech, Inc., South San Francisco, CA, USA
| | - Jiaxiang Jiang
- Clinical Imaging Group, Genentech, Inc., South San Francisco, CA, USA
- Department of Electrical and Computer Engineering, University of California, Santa Barbara, Santa Barbara, CA, USA
| | - Jasmine Patil
- Clinical Imaging Group, Genentech, Inc., South San Francisco, CA, USA
| | - Neha Anegondi
- Roche Personalized Healthcare, Genentech, Inc., South San Francisco, CA, USA
- Clinical Imaging Group, Genentech, Inc., South San Francisco, CA, USA
| | - Verena Steffen
- Roche Personalized Healthcare, Genentech, Inc., South San Francisco, CA, USA
- Biostatistics, Genentech, Inc., South San Francisco, CA, USA
| | | | - Elizabeth M Newton
- Roche Personalized Healthcare, Genentech, Inc., South San Francisco, CA, USA
| | - Christina Rabe
- Roche Personalized Healthcare, Genentech, Inc., South San Francisco, CA, USA
- Biostatistics, Genentech, Inc., South San Francisco, CA, USA
| | - Simon S Gao
- Roche Personalized Healthcare, Genentech, Inc., South San Francisco, CA, USA
- Clinical Imaging Group, Genentech, Inc., South San Francisco, CA, USA
| | - Aaron Y Lee
- Department of Ophthalmology, University of Washington, School of Medicine, Seattle, WA, USA
| | - Frank G Holz
- Department of Ophthalmology and GRADE Reading Center, University of Bonn, Bonn, Germany
| | - SriniVas Sadda
- Doheny Eye Institute, Los Angeles, CA, USA
- Department of Ophthalmology, David Geffen School of Medicine at University of California, Los Angeles, Los Angeles, CA, USA
| | - Steffen Schmitz-Valckenberg
- Department of Ophthalmology and GRADE Reading Center, University of Bonn, Bonn, Germany
- John A. Moran Eye Center, University of Utah, Salt Lake City, UT, USA
| | - Daniela Ferrara
- Roche Personalized Healthcare, Genentech, Inc., South San Francisco, CA, USA
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Anegondi N, Gao SS, Steffen V, Spaide RF, Sadda SR, Holz FG, Rabe C, Honigberg L, Newton EM, Cluceru J, Kawczynski MG, Bengtsson T, Ferrara D, Yang Q. Deep Learning to Predict Geographic Atrophy Area and Growth Rate from Multimodal Imaging. Ophthalmol Retina 2023; 7:243-252. [PMID: 36038116 DOI: 10.1016/j.oret.2022.08.018] [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] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2022] [Revised: 08/04/2022] [Accepted: 08/22/2022] [Indexed: 11/16/2022]
Abstract
OBJECTIVE To develop deep learning models for annualized geographic atrophy (GA) growth rate prediction using fundus autofluorescence (FAF) images and spectral-domain OCT volumes from baseline visits, which can be used for prognostic covariate adjustment to increase power of clinical trials. DESIGN This retrospective analysis estimated GA growth rate as the slope of a linear fit on all available measurements of lesion area over a 2-year period. Three multitask deep learning models-FAF-only, OCT-only, and multimodal (FAF and OCT)-were developed to predict concurrent GA area and annualized growth rate. PARTICIPANTS Patients were from prospective and observational lampalizumab clinical trials. METHODS The 3 models were trained on the development data set, tested on the holdout set, and further evaluated on the independent test sets. Baseline FAF images and OCT volumes from study eyes of patients with bilateral GA (NCT02247479; NCT02247531; and NCT02479386) were split into development (1279 patients/eyes) and holdout (443 patients/eyes) sets. Baseline FAF images from study eyes of NCT01229215 (106 patients/eyes) and NCT02399072 (169 patients/eyes) were used as independent test sets. MAIN OUTCOME MEASURES Model performance was evaluated using squared Pearson correlation coefficient (r2) between observed and predicted lesion areas/growth rates. Confidence intervals were calculated by bootstrap resampling (B = 10 000). RESULTS On the holdout data set, r2 (95% confidence interval) of the FAF-only, OCT-only, and multimodal models for GA lesion area prediction was 0.96 (0.95-0.97), 0.91 (0.87-0.95), and 0.94 (0.92-0.96), respectively, and for GA growth rate prediction was 0.48 (0.41-0.55), 0.36 (0.29-0.43), and 0.47 (0.40-0.54), respectively. On the 2 independent test sets, r2 of the FAF-only model for GA lesion area was 0.98 (0.97-0.99) and 0.95 (0.93-0.96), and for GA growth rate was 0.65 (0.52-0.75) and 0.47 (0.34-0.60). CONCLUSIONS We show the feasibility of using baseline FAF images and OCT volumes to predict individual GA area and growth rates using a multitask deep learning approach. The deep learning-based growth rate predictions could be used for covariate adjustment to increase power of clinical trials. FINANCIAL DISCLOSURE(S) Proprietary or commercial disclosure may be found after the references.
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Affiliation(s)
- Neha Anegondi
- Clinical Imaging Group, Genentech, Inc., South San Francisco, California; Roche Ophthalmology Personalized Healthcare, Genentech, Inc., South San Francisco, California
| | - Simon S Gao
- Clinical Imaging Group, Genentech, Inc., South San Francisco, California; Roche Ophthalmology Personalized Healthcare, Genentech, Inc., South San Francisco, California
| | - Verena Steffen
- Roche Ophthalmology Personalized Healthcare, Genentech, Inc., South San Francisco, California; Biostatistics, Genentech, Inc., South San Francisco, California
| | - Richard F Spaide
- Vitreous Retina Macula Consultants of New York, New York, New York
| | - SriniVas R Sadda
- Doheny Eye Institute, Los Angeles, California; Department of Ophthalmology, David Geffen School of Medicine at University of California, Los Angeles, Los Angeles, California
| | - Frank G Holz
- Department of Ophthalmology and GRADE Reading Center, University of Bonn, Bonn, Germany
| | - Christina Rabe
- Roche Ophthalmology Personalized Healthcare, Genentech, Inc., South San Francisco, California; Biostatistics, Genentech, Inc., South San Francisco, California
| | - Lee Honigberg
- Roche Ophthalmology Personalized Healthcare, Genentech, Inc., South San Francisco, California; Biomarker Development, Genentech, Inc., South San Francisco, California
| | - Elizabeth M Newton
- Roche Ophthalmology Personalized Healthcare, Genentech, Inc., South San Francisco, California
| | - Julia Cluceru
- Clinical Imaging Group, Genentech, Inc., South San Francisco, California; Roche Ophthalmology Personalized Healthcare, Genentech, Inc., South San Francisco, California
| | - Michael G Kawczynski
- Roche Ophthalmology Personalized Healthcare, Genentech, Inc., South San Francisco, California; Data Science Imaging, Genentech, Inc., South San Francisco, California
| | - Thomas Bengtsson
- Roche Ophthalmology Personalized Healthcare, Genentech, Inc., South San Francisco, California; Data Science Imaging, Genentech, Inc., South San Francisco, California
| | - Daniela Ferrara
- Roche Ophthalmology Personalized Healthcare, Genentech, Inc., South San Francisco, California
| | - Qi Yang
- Roche Ophthalmology Personalized Healthcare, Genentech, Inc., South San Francisco, California; Data Science Imaging, Genentech, Inc., South San Francisco, California.
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6
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Ding Y, Sohn JH, Kawczynski MG, Trivedi H, Harnish R, Jenkins NW, Lituiev D, Copeland TP, Aboian MS, Mari Aparici C, Behr SC, Flavell RR, Huang SY, Zalocusky KA, Nardo L, Seo Y, Hawkins RA, Hernandez Pampaloni M, Hadley D, Franc BL. A Deep Learning Model to Predict a Diagnosis of Alzheimer Disease by Using 18F-FDG PET of the Brain. Radiology 2019; 290:456-464. [PMID: 30398430 PMCID: PMC6358051 DOI: 10.1148/radiol.2018180958] [Citation(s) in RCA: 242] [Impact Index Per Article: 48.4] [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: 04/23/2018] [Revised: 08/24/2018] [Accepted: 09/13/2018] [Indexed: 12/11/2022]
Abstract
Purpose To develop and validate a deep learning algorithm that predicts the final diagnosis of Alzheimer disease (AD), mild cognitive impairment, or neither at fluorine 18 (18F) fluorodeoxyglucose (FDG) PET of the brain and compare its performance to that of radiologic readers. Materials and Methods Prospective 18F-FDG PET brain images from the Alzheimer's Disease Neuroimaging Initiative (ADNI) (2109 imaging studies from 2005 to 2017, 1002 patients) and retrospective independent test set (40 imaging studies from 2006 to 2016, 40 patients) were collected. Final clinical diagnosis at follow-up was recorded. Convolutional neural network of InceptionV3 architecture was trained on 90% of ADNI data set and tested on the remaining 10%, as well as the independent test set, with performance compared to radiologic readers. Model was analyzed with sensitivity, specificity, receiver operating characteristic (ROC), saliency map, and t-distributed stochastic neighbor embedding. Results The algorithm achieved area under the ROC curve of 0.98 (95% confidence interval: 0.94, 1.00) when evaluated on predicting the final clinical diagnosis of AD in the independent test set (82% specificity at 100% sensitivity), an average of 75.8 months prior to the final diagnosis, which in ROC space outperformed reader performance (57% [four of seven] sensitivity, 91% [30 of 33] specificity; P < .05). Saliency map demonstrated attention to known areas of interest but with focus on the entire brain. Conclusion By using fluorine 18 fluorodeoxyglucose PET of the brain, a deep learning algorithm developed for early prediction of Alzheimer disease achieved 82% specificity at 100% sensitivity, an average of 75.8 months prior to the final diagnosis. © RSNA, 2018 Online supplemental material is available for this article. See also the editorial by Larvie in this issue.
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Affiliation(s)
- Yiming Ding
- From the Department of Radiology and Biomedical Imaging (Y.D.,
J.H.S., H.T., R.H., N.W.J., T.P.C., M.S.A., C.M.A., S.C.B., R.R.F., S.Y.H.,
Y.S., R.A.H., M.H.P., B.L.F.) and Institute for Computational Health Sciences
(J.H.S., M.G.K., H.T., D.L., K.A.Z., D.H.), University of California, San
Francisco, 550 Parnassus Ave, San Francisco, CA 94143; Department of Electrical
Engineering and Computer Sciences, University of California, Berkeley, Berkeley,
Calif (Y.D.); and Department of Radiology, University of California, Davis,
Sacramento, Calif (L.N.)
| | - Jae Ho Sohn
- From the Department of Radiology and Biomedical Imaging (Y.D.,
J.H.S., H.T., R.H., N.W.J., T.P.C., M.S.A., C.M.A., S.C.B., R.R.F., S.Y.H.,
Y.S., R.A.H., M.H.P., B.L.F.) and Institute for Computational Health Sciences
(J.H.S., M.G.K., H.T., D.L., K.A.Z., D.H.), University of California, San
Francisco, 550 Parnassus Ave, San Francisco, CA 94143; Department of Electrical
Engineering and Computer Sciences, University of California, Berkeley, Berkeley,
Calif (Y.D.); and Department of Radiology, University of California, Davis,
Sacramento, Calif (L.N.)
| | - Michael G. Kawczynski
- From the Department of Radiology and Biomedical Imaging (Y.D.,
J.H.S., H.T., R.H., N.W.J., T.P.C., M.S.A., C.M.A., S.C.B., R.R.F., S.Y.H.,
Y.S., R.A.H., M.H.P., B.L.F.) and Institute for Computational Health Sciences
(J.H.S., M.G.K., H.T., D.L., K.A.Z., D.H.), University of California, San
Francisco, 550 Parnassus Ave, San Francisco, CA 94143; Department of Electrical
Engineering and Computer Sciences, University of California, Berkeley, Berkeley,
Calif (Y.D.); and Department of Radiology, University of California, Davis,
Sacramento, Calif (L.N.)
| | - Hari Trivedi
- From the Department of Radiology and Biomedical Imaging (Y.D.,
J.H.S., H.T., R.H., N.W.J., T.P.C., M.S.A., C.M.A., S.C.B., R.R.F., S.Y.H.,
Y.S., R.A.H., M.H.P., B.L.F.) and Institute for Computational Health Sciences
(J.H.S., M.G.K., H.T., D.L., K.A.Z., D.H.), University of California, San
Francisco, 550 Parnassus Ave, San Francisco, CA 94143; Department of Electrical
Engineering and Computer Sciences, University of California, Berkeley, Berkeley,
Calif (Y.D.); and Department of Radiology, University of California, Davis,
Sacramento, Calif (L.N.)
| | - Roy Harnish
- From the Department of Radiology and Biomedical Imaging (Y.D.,
J.H.S., H.T., R.H., N.W.J., T.P.C., M.S.A., C.M.A., S.C.B., R.R.F., S.Y.H.,
Y.S., R.A.H., M.H.P., B.L.F.) and Institute for Computational Health Sciences
(J.H.S., M.G.K., H.T., D.L., K.A.Z., D.H.), University of California, San
Francisco, 550 Parnassus Ave, San Francisco, CA 94143; Department of Electrical
Engineering and Computer Sciences, University of California, Berkeley, Berkeley,
Calif (Y.D.); and Department of Radiology, University of California, Davis,
Sacramento, Calif (L.N.)
| | - Nathaniel W. Jenkins
- From the Department of Radiology and Biomedical Imaging (Y.D.,
J.H.S., H.T., R.H., N.W.J., T.P.C., M.S.A., C.M.A., S.C.B., R.R.F., S.Y.H.,
Y.S., R.A.H., M.H.P., B.L.F.) and Institute for Computational Health Sciences
(J.H.S., M.G.K., H.T., D.L., K.A.Z., D.H.), University of California, San
Francisco, 550 Parnassus Ave, San Francisco, CA 94143; Department of Electrical
Engineering and Computer Sciences, University of California, Berkeley, Berkeley,
Calif (Y.D.); and Department of Radiology, University of California, Davis,
Sacramento, Calif (L.N.)
| | - Dmytro Lituiev
- From the Department of Radiology and Biomedical Imaging (Y.D.,
J.H.S., H.T., R.H., N.W.J., T.P.C., M.S.A., C.M.A., S.C.B., R.R.F., S.Y.H.,
Y.S., R.A.H., M.H.P., B.L.F.) and Institute for Computational Health Sciences
(J.H.S., M.G.K., H.T., D.L., K.A.Z., D.H.), University of California, San
Francisco, 550 Parnassus Ave, San Francisco, CA 94143; Department of Electrical
Engineering and Computer Sciences, University of California, Berkeley, Berkeley,
Calif (Y.D.); and Department of Radiology, University of California, Davis,
Sacramento, Calif (L.N.)
| | - Timothy P. Copeland
- From the Department of Radiology and Biomedical Imaging (Y.D.,
J.H.S., H.T., R.H., N.W.J., T.P.C., M.S.A., C.M.A., S.C.B., R.R.F., S.Y.H.,
Y.S., R.A.H., M.H.P., B.L.F.) and Institute for Computational Health Sciences
(J.H.S., M.G.K., H.T., D.L., K.A.Z., D.H.), University of California, San
Francisco, 550 Parnassus Ave, San Francisco, CA 94143; Department of Electrical
Engineering and Computer Sciences, University of California, Berkeley, Berkeley,
Calif (Y.D.); and Department of Radiology, University of California, Davis,
Sacramento, Calif (L.N.)
| | - Mariam S. Aboian
- From the Department of Radiology and Biomedical Imaging (Y.D.,
J.H.S., H.T., R.H., N.W.J., T.P.C., M.S.A., C.M.A., S.C.B., R.R.F., S.Y.H.,
Y.S., R.A.H., M.H.P., B.L.F.) and Institute for Computational Health Sciences
(J.H.S., M.G.K., H.T., D.L., K.A.Z., D.H.), University of California, San
Francisco, 550 Parnassus Ave, San Francisco, CA 94143; Department of Electrical
Engineering and Computer Sciences, University of California, Berkeley, Berkeley,
Calif (Y.D.); and Department of Radiology, University of California, Davis,
Sacramento, Calif (L.N.)
| | - Carina Mari Aparici
- From the Department of Radiology and Biomedical Imaging (Y.D.,
J.H.S., H.T., R.H., N.W.J., T.P.C., M.S.A., C.M.A., S.C.B., R.R.F., S.Y.H.,
Y.S., R.A.H., M.H.P., B.L.F.) and Institute for Computational Health Sciences
(J.H.S., M.G.K., H.T., D.L., K.A.Z., D.H.), University of California, San
Francisco, 550 Parnassus Ave, San Francisco, CA 94143; Department of Electrical
Engineering and Computer Sciences, University of California, Berkeley, Berkeley,
Calif (Y.D.); and Department of Radiology, University of California, Davis,
Sacramento, Calif (L.N.)
| | - Spencer C. Behr
- From the Department of Radiology and Biomedical Imaging (Y.D.,
J.H.S., H.T., R.H., N.W.J., T.P.C., M.S.A., C.M.A., S.C.B., R.R.F., S.Y.H.,
Y.S., R.A.H., M.H.P., B.L.F.) and Institute for Computational Health Sciences
(J.H.S., M.G.K., H.T., D.L., K.A.Z., D.H.), University of California, San
Francisco, 550 Parnassus Ave, San Francisco, CA 94143; Department of Electrical
Engineering and Computer Sciences, University of California, Berkeley, Berkeley,
Calif (Y.D.); and Department of Radiology, University of California, Davis,
Sacramento, Calif (L.N.)
| | - Robert R. Flavell
- From the Department of Radiology and Biomedical Imaging (Y.D.,
J.H.S., H.T., R.H., N.W.J., T.P.C., M.S.A., C.M.A., S.C.B., R.R.F., S.Y.H.,
Y.S., R.A.H., M.H.P., B.L.F.) and Institute for Computational Health Sciences
(J.H.S., M.G.K., H.T., D.L., K.A.Z., D.H.), University of California, San
Francisco, 550 Parnassus Ave, San Francisco, CA 94143; Department of Electrical
Engineering and Computer Sciences, University of California, Berkeley, Berkeley,
Calif (Y.D.); and Department of Radiology, University of California, Davis,
Sacramento, Calif (L.N.)
| | - Shih-Ying Huang
- From the Department of Radiology and Biomedical Imaging (Y.D.,
J.H.S., H.T., R.H., N.W.J., T.P.C., M.S.A., C.M.A., S.C.B., R.R.F., S.Y.H.,
Y.S., R.A.H., M.H.P., B.L.F.) and Institute for Computational Health Sciences
(J.H.S., M.G.K., H.T., D.L., K.A.Z., D.H.), University of California, San
Francisco, 550 Parnassus Ave, San Francisco, CA 94143; Department of Electrical
Engineering and Computer Sciences, University of California, Berkeley, Berkeley,
Calif (Y.D.); and Department of Radiology, University of California, Davis,
Sacramento, Calif (L.N.)
| | - Kelly A. Zalocusky
- From the Department of Radiology and Biomedical Imaging (Y.D.,
J.H.S., H.T., R.H., N.W.J., T.P.C., M.S.A., C.M.A., S.C.B., R.R.F., S.Y.H.,
Y.S., R.A.H., M.H.P., B.L.F.) and Institute for Computational Health Sciences
(J.H.S., M.G.K., H.T., D.L., K.A.Z., D.H.), University of California, San
Francisco, 550 Parnassus Ave, San Francisco, CA 94143; Department of Electrical
Engineering and Computer Sciences, University of California, Berkeley, Berkeley,
Calif (Y.D.); and Department of Radiology, University of California, Davis,
Sacramento, Calif (L.N.)
| | - Lorenzo Nardo
- From the Department of Radiology and Biomedical Imaging (Y.D.,
J.H.S., H.T., R.H., N.W.J., T.P.C., M.S.A., C.M.A., S.C.B., R.R.F., S.Y.H.,
Y.S., R.A.H., M.H.P., B.L.F.) and Institute for Computational Health Sciences
(J.H.S., M.G.K., H.T., D.L., K.A.Z., D.H.), University of California, San
Francisco, 550 Parnassus Ave, San Francisco, CA 94143; Department of Electrical
Engineering and Computer Sciences, University of California, Berkeley, Berkeley,
Calif (Y.D.); and Department of Radiology, University of California, Davis,
Sacramento, Calif (L.N.)
| | - Youngho Seo
- From the Department of Radiology and Biomedical Imaging (Y.D.,
J.H.S., H.T., R.H., N.W.J., T.P.C., M.S.A., C.M.A., S.C.B., R.R.F., S.Y.H.,
Y.S., R.A.H., M.H.P., B.L.F.) and Institute for Computational Health Sciences
(J.H.S., M.G.K., H.T., D.L., K.A.Z., D.H.), University of California, San
Francisco, 550 Parnassus Ave, San Francisco, CA 94143; Department of Electrical
Engineering and Computer Sciences, University of California, Berkeley, Berkeley,
Calif (Y.D.); and Department of Radiology, University of California, Davis,
Sacramento, Calif (L.N.)
| | - Randall A. Hawkins
- From the Department of Radiology and Biomedical Imaging (Y.D.,
J.H.S., H.T., R.H., N.W.J., T.P.C., M.S.A., C.M.A., S.C.B., R.R.F., S.Y.H.,
Y.S., R.A.H., M.H.P., B.L.F.) and Institute for Computational Health Sciences
(J.H.S., M.G.K., H.T., D.L., K.A.Z., D.H.), University of California, San
Francisco, 550 Parnassus Ave, San Francisco, CA 94143; Department of Electrical
Engineering and Computer Sciences, University of California, Berkeley, Berkeley,
Calif (Y.D.); and Department of Radiology, University of California, Davis,
Sacramento, Calif (L.N.)
| | - Miguel Hernandez Pampaloni
- From the Department of Radiology and Biomedical Imaging (Y.D.,
J.H.S., H.T., R.H., N.W.J., T.P.C., M.S.A., C.M.A., S.C.B., R.R.F., S.Y.H.,
Y.S., R.A.H., M.H.P., B.L.F.) and Institute for Computational Health Sciences
(J.H.S., M.G.K., H.T., D.L., K.A.Z., D.H.), University of California, San
Francisco, 550 Parnassus Ave, San Francisco, CA 94143; Department of Electrical
Engineering and Computer Sciences, University of California, Berkeley, Berkeley,
Calif (Y.D.); and Department of Radiology, University of California, Davis,
Sacramento, Calif (L.N.)
| | - Dexter Hadley
- From the Department of Radiology and Biomedical Imaging (Y.D.,
J.H.S., H.T., R.H., N.W.J., T.P.C., M.S.A., C.M.A., S.C.B., R.R.F., S.Y.H.,
Y.S., R.A.H., M.H.P., B.L.F.) and Institute for Computational Health Sciences
(J.H.S., M.G.K., H.T., D.L., K.A.Z., D.H.), University of California, San
Francisco, 550 Parnassus Ave, San Francisco, CA 94143; Department of Electrical
Engineering and Computer Sciences, University of California, Berkeley, Berkeley,
Calif (Y.D.); and Department of Radiology, University of California, Davis,
Sacramento, Calif (L.N.)
| | - Benjamin L. Franc
- From the Department of Radiology and Biomedical Imaging (Y.D.,
J.H.S., H.T., R.H., N.W.J., T.P.C., M.S.A., C.M.A., S.C.B., R.R.F., S.Y.H.,
Y.S., R.A.H., M.H.P., B.L.F.) and Institute for Computational Health Sciences
(J.H.S., M.G.K., H.T., D.L., K.A.Z., D.H.), University of California, San
Francisco, 550 Parnassus Ave, San Francisco, CA 94143; Department of Electrical
Engineering and Computer Sciences, University of California, Berkeley, Berkeley,
Calif (Y.D.); and Department of Radiology, University of California, Davis,
Sacramento, Calif (L.N.)
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