Fine-tuned convolutional neural nets for cardiac MRI acquisition plane recognition

J. Margeta*, A. Criminisi, R. Cabrera Lozoya, D. C. Lee, N. Ayache

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

36 Scopus citations

Abstract

In this paper, we propose a convolutional neural network-based method to automatically retrieve missing or noisy cardiac acquisition plane information from magnetic resonance imaging and predict the five most common cardiac views. We fine-tune a convolutional neural network (CNN) initially trained on a large natural image recognition data-set (Imagenet ILSVRC2012) and transfer the learnt feature representations to cardiac view recognition. We contrast this approach with a previously introduced method using classification forests and an augmented set of image miniatures, with prediction using off the shelf CNN features, and with CNNs learnt from scratch. We validate this algorithm on two different cardiac studies with 200 patients and 15 healthy volunteers, respectively. We show that there is value in fine-tuning a model trained for natural images to transfer it to medical images. Our approach achieves an average F1 score of 97.66% and significantly improves the state-of-the-art of image-based cardiac view recognition. This is an important building block to organise and filter large collections of cardiac data prior to further analysis. It allows us to merge studies from multiple centres, to perform smarter image filtering, to select the most appropriate image processing algorithm, and to enhance visualisation of cardiac data-sets in content-based image retrieval.

Original languageEnglish (US)
Pages (from-to)339-349
Number of pages11
JournalComputer Methods in Biomechanics and Biomedical Engineering: Imaging and Visualization
Volume5
Issue number5
DOIs
StatePublished - Sep 3 2017

Keywords

  • applications of imaging and visualization
  • computer aided diagnosis
  • data processing and analysis
  • image processing and analysis
  • machine learning; cardiac magnetic resonance; convolutional neural networks
  • medical imaging and visualization

ASJC Scopus subject areas

  • Computational Mechanics
  • Biomedical Engineering
  • Radiology Nuclear Medicine and imaging
  • Computer Science Applications

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