How well do U-Net-based segmentation trained on adult cardiac magnetic resonance imaging data generalize to rare congenital heart diseases for surgical planning?

MLA

Animesh Tandon, et al. “How Well Do U-Net-Based Segmentation Trained on Adult Cardiac Magnetic Resonance Imaging Data Generalize to Rare Congenital Heart Diseases for Surgical Planning?” Medical Imaging 2020: Image-Guided Procedures, Robotic Interventions, and Modeling, Mar. 2020. EBSCOhost, https://doi.org/10.1117/12.2550651.

APA

Animesh Tandon, Gerald F. Greil, Philipp Beerbaum, Sandy Engelhardt, Thomas Pickardt, Sven Koehler, Samir Sarikouch, Tarique Hussain, Ivo Wolf, & Heiner Latus. (2020). How well do U-Net-based segmentation trained on adult cardiac magnetic resonance imaging data generalize to rare congenital heart diseases for surgical planning? Medical Imaging 2020: Image-Guided Procedures, Robotic Interventions, and Modeling. https://doi.org/10.1117/12.2550651

Chicago

Animesh Tandon, Gerald F. Greil, Philipp Beerbaum, Sandy Engelhardt, Thomas Pickardt, Sven Koehler, Samir Sarikouch, Tarique Hussain, Ivo Wolf, and Heiner Latus. 2020. “How Well Do U-Net-Based Segmentation Trained on Adult Cardiac Magnetic Resonance Imaging Data Generalize to Rare Congenital Heart Diseases for Surgical Planning?” Medical Imaging 2020: Image-Guided Procedures, Robotic Interventions, and Modeling, March. doi:10.1117/12.2550651.