CardiSort: a convolutional neural network for cross vendor automated sorting of cardiac MR images

• URL: http://arxiv.org/abs/2109.08479v1
• Date: Fri, 17 Sep 2021 11:42:39 GMT
• Title: CardiSort: a convolutional neural network for cross vendor automated sorting of cardiac MR images
• Authors: Ruth P Lim, Stefan Kachel, Adriana DM Villa, Leighton Kearney, Nuno Bettencourt, Alistair A Young, Amedeo Chiribiri, Cian M Scannell
• Abstract summary: A two-head convolutional neural network ('CardiSort') was trained to classify 35 sequences by imaging sequence and plane. High sequence and plane accuracies were observed for single vendor training (SVT) and multi-vendor training (MVT) There was high accuracy for common sequences and conventional cardiac planes.
• Score: 2.0791118244420757
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Objectives: To develop an image-based automatic deep learning method to classify cardiac MR images by sequence type and imaging plane for improved clinical post-processing efficiency. Methods: Multi-vendor cardiac MRI studies were retrospectively collected from 4 centres and 3 vendors. A two-head convolutional neural network ('CardiSort') was trained to classify 35 sequences by imaging sequence (n=17) and plane (n=10). Single vendor training (SVT) on single centre images (n=234 patients) and multi-vendor training (MVT) with multicentre images (n = 479 patients, 3 centres) was performed. Model accuracy was compared to manual ground truth labels by an expert radiologist on a hold-out test set for both SVT and MVT. External validation of MVT (MVTexternal) was performed on data from 3 previously unseen magnet systems from 2 vendors (n=80 patients). Results: High sequence and plane accuracies were observed for SVT (85.2% and 93.2% respectively), and MVT (96.5% and 98.1% respectively) on the hold-out test set. MVTexternal yielded sequence accuracy of 92.7% and plane accuracy of 93.0%. There was high accuracy for common sequences and conventional cardiac planes. Poor accuracy was observed for underrepresented classes and sequences where there was greater variability in acquisition parameters across centres, such as perfusion imaging. Conclusions: A deep learning network was developed on multivendor data to classify MRI studies into component sequences and planes, with external validation. With refinement, it has potential to improve workflow by enabling automated sequence selection, an important first step in completely automated post-processing pipelines.

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