@inproceedings{b1e129fd7ce94704b99394418552da99,
title = "How well do U-Net-based segmentation trained on adult cardiac magnetic resonance imaging data generalise to rare congenital heart diseases for surgical planning?",
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).",
keywords = "Cardiac magnet resonance imaging (CMR), Deep learning, Generalisation, Machine learning for surgical applications, Semantic segmentation, Tetralogy of Fallot, U-Net",
author = "Sven Koehler and Animesh Tandon and Tarique Hussain and Heiner Latus and Thomas Pickardt and Samir Sarikouch and Philipp Beerbaum and Gerald Greil and Sandy Engelhardt and Ivo Wolf",
note = "Funding Information: The Titan Xp GPU card used for this research was donated by the NVIDIA Corporation. This work was supported by the Competence Network for Congenital Heart Defects, which has received funding from the Federal Ministry of Education and Research, grant number 01GI0601 (until 2014), and the DZHK (German Centre for Cardiovascular Research; as of 2015). Publisher Copyright: {\textcopyright} 2020 SPIE.; Medical Imaging 2020: Image-Guided Procedures, Robotic Interventions, and Modeling ; Conference date: 16-02-2020 Through 19-02-2020",
year = "2020",
doi = "10.1117/12.2550651",
language = "English (US)",
series = "Proceedings of SPIE - The International Society for Optical Engineering",
publisher = "SPIE",
editor = "Baowei Fei and Linte, {Cristian A.}",
booktitle = "Medical Imaging 2020",
}