Deep learning for the detection of landmarks in head CT images and automatic quality assessment

Hrishikesh Deshpande; Axel Saalbach; Tim Harder; Stewart Young; Thomas Buelow

doi:10.1117/12.2581810

15 February 2021 Deep learning for the detection of landmarks in head CT images and automatic quality assessment

Hrishikesh Deshpande, Axel Saalbach, Tim Harder, Stewart Young, Thomas Buelow

Proceedings Volume 11596, Medical Imaging 2021: Image Processing; 115960N (2021) https://doi.org/10.1117/12.2581810
Event: SPIE Medical Imaging, 2021, Online Only

Abstract

In order to alleviate the risk of radiation induced cataract in patients undergoing head CT examinations, the guidelines published by the American Association of Physicists in Medicine (AAPM) link the optimal scan angle to particular anatomic landmarks in the skull. In this paper, we investigated the use of a foveal fully-convolutional neural network (F-Net) for the segmentation-based detection of three head CT landmarks, with the final objective of an automatic scan quality control. Three individual networks were trained using ground-truth (GT) from three different readers to investigate the detection accuracy compared to each reader. The experiments were performed using 119 head CT scans and the three-fold cross-validation set up. For the evaluation, two performance measures were employed: the Euclidean distance between the detected landmarks and GT, and the distance of the detected landmarks to the plane generated from the GT landmark positions. For three readers, the median values of the Euclidean and point-to-plane distance obtained using F-Net were in the range of 1.3 - 2.8 mm and 0.3 - 0.8 mm, respectively. The presented method outperformed a previously published approach using image registration and achieved results comparable to the inter-observer variability between three readers. Further improvements were achieved by training a similar network which combined GT information from all readers.

Conference Presentation

Citation Download Citation

Hrishikesh Deshpande, Axel Saalbach, Tim Harder, Stewart Young, and Thomas Buelow "Deep learning for the detection of landmarks in head CT images and automatic quality assessment", Proc. SPIE 11596, Medical Imaging 2021: Image Processing, 115960N (15 February 2021); https://doi.org/10.1117/12.2581810

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