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

Sven Koehler; Animesh Tandon; Tarique Hussain; Heiner Latus; Thomas Pickardt; Samir Sarikouch; Philipp Beerbaum; Gerald Greil; Sandy Engelhardt; Ivo Wolf

doi:10.1117/12.2550651

16 March 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?

Sven Koehler, Animesh Tandon, Tarique Hussain, Heiner Latus, Thomas Pickardt, Samir Sarikouch, Philipp Beerbaum, Gerald Greil, Sandy Engelhardt, Ivo Wolf

Author Affiliations +

Proceedings Volume 11315, Medical Imaging 2020: Image-Guided Procedures, Robotic Interventions, and Modeling; 113151K (2020) https://doi.org/10.1117/12.2550651
Event: SPIE Medical Imaging, 2020, Houston, Texas, United States

Abstract

Planning the optimal time of intervention for pulmonary valve replacement surgery in patients with the congenital heart disease Tetralogy of Fallot (TOF) is mainly based on ventricular volume and function according to current guidelines. Both of these two biomarkers are most reliably assessed by segmentation of 3D cardiac magnetic resonance (CMR) images. In several grand challenges in the last years, U-Net architectures have shown impressive results on the provided data. However, in clinical practice, data sets are more diverse considering individual pathologies and image properties derived from different scanner properties. Additionally, specific training data for complex rare diseases like TOF is scarce. For this work, 1) we assessed the accuracy gap when using a publicly available labelled data set (the Automatic Cardiac Diagnosis Challenge (ACDC) data set) for training and subsequent applying it to CMR data of TOF patients and vice versa and 2) whether we can achieve similar results when applying the model to a more heterogeneous data base. Multiple deep learning models were trained with four-fold cross validation. Afterwards they were evaluated on the respective unseen CMR images from the other collection. Our results confirm that current deep learning models can achieve excellent results (left ventricle dice of 0.951±0.003/0.941±0.0007 train/validation) within a single data collection. But once they are applied to other pathologies, it becomes apparent how much they overfit to the training pathologies (dice score drops between 0.072±0.001 for the left and 0.165±0.001 for the right ventricle).

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Sven Koehler, Animesh Tandon, Tarique Hussain, Heiner Latus, Thomas Pickardt, Samir Sarikouch, Philipp Beerbaum, Gerald Greil, Sandy Engelhardt, and Ivo Wolf "How well do U-Net-based segmentation trained on adult cardiac magnetic resonance imaging data generalize to rare congenital heart diseases for surgical planning?", Proc. SPIE 11315, Medical Imaging 2020: Image-Guided Procedures, Robotic Interventions, and Modeling, 113151K (16 March 2020); https://doi.org/10.1117/12.2550651

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