Comparison between threshold-based and deep learning-based bone segmentation on whole-body CT images

Noémie Moreau; Caroline Rousseau; Constance Fourcade; Gianmarco Santini; Ludovic Ferrer; Marie Lacombe; Camille Guillerminet; Pascal Jezequel; Mario Campone; Nicolas Normand; Mathieu Rubeaux

doi:10.1117/12.2580892

15 February 2021 Comparison between threshold-based and deep learning-based bone segmentation on whole-body CT images

Noémie Moreau, Caroline Rousseau, Constance Fourcade, Gianmarco Santini, Ludovic Ferrer, Marie Lacombe, Camille Guillerminet, Pascal Jezequel, Mario Campone, Nicolas Normand, Mathieu Rubeaux

Author Affiliations +

Proceedings Volume 11597, Medical Imaging 2021: Computer-Aided Diagnosis; 115972U (2021) https://doi.org/10.1117/12.2580892
Event: SPIE Medical Imaging, 2021, Online Only

Conference Poster

Abstract

Objectives: Bone segmentation can help bone disease diagnosis or post treatment assessment but manual segmentation is a time consuming and tedious task in clinical practice. In this work, three automatic methods to segment bone structures on whole body CT images were compared. Methods: A threshold-based approach with morphological operations and two deep learning methods using a 3D U-Net with different losses, one with a cross entropy/Dice loss and the second with a Hausdorff Distance/Dice loss, were developed. Ground truth bone segmentations were generated by manually correcting the results obtained with the threshold based method. The automatic bone segmentations were evaluated using a Dice score and Hausdorff distance. Visual evaluation was also performed by a medical expert. Results: Dice scores of 0.953, 0.986 and 0.978 were achieved for the Threshold-based method and the two deep learning methods, respectively. Visual evaluation showed that the deep learning method with a Hausdorff Distance/Dice loss performed the best.

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Noémie Moreau, Caroline Rousseau, Constance Fourcade, Gianmarco Santini, Ludovic Ferrer, Marie Lacombe, Camille Guillerminet, Pascal Jezequel, Mario Campone, Nicolas Normand, and Mathieu Rubeaux "Comparison between threshold-based and deep learning-based bone segmentation on whole-body CT images", Proc. SPIE 11597, Medical Imaging 2021: Computer-Aided Diagnosis, 115972U (15 February 2021); https://doi.org/10.1117/12.2580892

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