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Communication Dans Un Congrès Année : 2021

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

Résumé

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.

Domaines

Cancer
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Dates et versions

inserm-03540472 , version 1 (24-01-2022)

Identifiants

Citer

Noémie Moreau, Caroline Rousseau, Constance Fourcade, Gianmarco Santini, Ludovic Ferrer, et al.. Comparison between threshold-based and deep learning-based bone segmentation on whole-body CT images. Computer-Aided Diagnosis, Feb 2021, Online Only, United States. pp.100, ⟨10.1117/12.2580892⟩. ⟨inserm-03540472⟩
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