Interinstitutional Portability of a Deep Learning Brain MRI Lesion Segmentation Algorithm.

To assess how well a brain MRI lesion segmentation algorithm trained at one institution performed at another institution, and to assess the effect of multi-institutional training datasets for mitigating performance loss. In this retrospective study, a three-dimensional U-Net for brain MRI abnormality segmentation was trained on data from 293 patients from one institution (IN1) (median age, 54 years; 165 women; patients treated between 2008 and 2018) and tested on data from 51 patients from a second institution (IN2) (median age, 46 years; 27 women; patients treated between 2003 and 2019). The model was then trained on additional data from various sources: The U-Net accurately segmented brain MRI lesions across various pathologic conditions. Performance was lower when tested at an external institution (median Dice score, 0.70 [IN2] vs 0.76 [IN1]). Addition of 483 training cases of a single pathologic condition, including from IN2, did not raise performance (median Dice score, 0.72; For brain MRI lesion segmentation, adding a modest amount of relevant training data from an external institution to a previously trained model supported successful application of the model to this external institution.

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2021 by the Radiological Society of North America, Inc.

Déclaration de conflit d'intérêts

Disclosures of conflicts of interest: A.M.R. Institution received American Society of Neuroradiology (ASNR) trainee grant and Carestream Health/Radiological Society of North America (RSNA) research scholar grant; former member of Radiology: Artificial Intelligence trainee editorial board. T.J.G. No relevant relationships. P.N. Formerly employed by Perceus and PrinterPress (urologic devices and orthopedic devices); grants/grants pending from Perceus for a urology device; patent from Perceus for a urology device; stock/stock options in PrinterPress (orthopedic devices). M.T.D. No relevant relationships. D.A.W. Consultancy fee from Galileo CDS for work not related to this publication; stock/stock options in Galileo CDS received for work not related to this publication. E.C. No relevant relationships. J.B.C. No relevant relationships. L.P.S. No relevant relationships. J.D.R. Institution received ASNR neuroradiology research grant in AI; former member of Radiology: Artificial Intelligence trainee editorial board. C.P.H. Medical imaging consultant for GE Healthcare; research travel expenses from Siemens Healthineers; member of data monitoring and safety boards for Insightec and UniQure; former member of Radiology editorial board.