Deep learning-based Wilms tumor segmentation to create 3D models for surgical planning: Implementation in the clinical workflow

Space: StayCurrentMD Author: M.A.D. Buser, N.T. de Groot, D.C. Simons, A.S. Littooij, M.H.W.A. Wijnen, C.P. van de Ven, M.M. van den Heuvel-Eibrink, M. Fitski, A.F.W. van der Steeg Published:

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M.A.D. Buser, N.T. de Groot, D.C. Simons, A.S. Littooij, M.H.W.A. Wijnen, C.P. van de Ven, M.M. van den Heuvel-Eibrink, M. Fitski, A.F.W. van der Steeg

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Creating 3D models based on pre-operative MRI of patients with a Wilms tumor (WT) can aid surgical planning. However, creating these models requires manual delineation (segmentation) of the MRI imaging. Deep learning can automate this, but most validations of these segmentation methods are retrospective. This article prospective evaluation of a WT segmentation method in a clinical workflow aimed at creating 3D models for surgical planning.

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