Evaluation of a hybrid pipeline for automated segmentation of solid lesions based on mathematical algorithms and deep learning.

Journal: Scientific reports
Published Date:

Abstract

We evaluate the accuracy of an original hybrid segmentation pipeline, combining variational and deep learning methods, in the segmentation of CT scans of stented aortic aneurysms, abdominal organs and brain lesions. The hybrid pipeline is trained on 50 aortic CT scans and tested on 10. Additionally, we trained and tested the hybrid pipeline on publicly available datasets of CT scans of abdominal organs and MR scans of brain tumours. We tested the accuracy of the hybrid pipeline against a gold standard (manual segmentation) and compared its performance to that of a standard automated segmentation method with commonly used metrics, including the DICE and JACCARD and volumetric similarity (VS) coefficients, and the Hausdorff Distance (HD). Results. The hybrid pipeline produced very accurate segmentations of the aorta, with mean DICE, JACCARD and VS coefficients of: 0.909, 0.837 and 0.972 in thrombus segmentation and 0.937, 0.884 and 0.970 for stent and lumen segmentation. It consistently outperformed the standard automated method. Similar results were observed when the hybrid pipeline was trained and tested on publicly available datasets, with mean DICE scores of: 0.832 on brain tumour segmentation, and 0.894/0.841/0.853/0.847/0.941 on left kidney/right kidney/spleen/aorta/liver organ segmentation.

Authors

  • Liam Burrows
    Centre for Mathematical Imaging Techniques and Department of Mathematical Sciences, University of Liverpool, Liverpool, L69 7ZL, UK. liam.burrows@liv.ac.uk.
  • Ke Chen
    Department of Signal Processing, Tampere University of Technology, Finland.
  • Weihong Guo
    Department of Mathematics, Applied Mathematics and Statistics, Case Western Reserve University, Cleveland, OH, 44106, USA.
  • Martin Hossack
    Liverpool Vascular and Endovascular Service, Liverpool University Hospitals NHS Foundation Trust, Liverpool, UK.
  • Richard G McWilliams
    Department of Radiology, Liverpool University Hospitals, Liverpool, UK.
  • Francesco Torella
    Liverpool Vascular and Endovascular Service, Liverpool University Hospitals NHS Foundation Trust, Liverpool, UK.