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A Deep Learning Approach for the Automatic Identification of the Left Atrium Within CT Scans

[+] Author Affiliations
Alex Deakyne, Erik Gaasedelen, Paul A. Iaizzo

University of Minnesota, Minneapolis, MN

Paper No. DMD2019-3282, pp. V001T01A005; 2 pages
  • 2019 Design of Medical Devices Conference
  • 2019 Design of Medical Devices Conference
  • Minneapolis, Minnesota, USA, April 15–18, 2019
  • ISBN: 978-0-7918-4103-7
  • Copyright © 2019 by ASME


Recent advancements in deep learning have led to the possibility of increased performance in computer vision tools. A major development has been the usage of Convolutional Neural Networks (CNN) for automatically detecting features within a given image. Architectures such as YOLO1 have obtained incredibly high performances for the real-time detection of every-day objects within images. However to date, there have been few reports of deep learning applied to detect anatomical features within CT scans; especially those within the cardiovascular space. We propose here an automatic anatomical feature detection pipeline for identifying the features of the left atrium using a CNN. Slices of CT scans were fed into a single neural network which predicted the four bounding box coordinates that encapsulate the left atrium. The network can be optimized end-to-end and generate predictions at great speed, achieving a validation smooth L1 loss of 11.95 when predicting the left atrial bounding boxes.

Copyright © 2019 by ASME



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