Comparison between threshold-based and deep learning-based bone segmentation on whole-body CT images - Archive ouverte HAL Access content directly
Conference Papers Year :

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

(1, 2, 3) , (1, 4) , (1, 2) , (2) , (1, 4) , (4) , (4) , (1) , (1, 4) , (1) , (2)
1
2
3
4

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.
Not file

Dates and versions

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

Identifiers

Cite

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⟩
33 View
0 Download

Altmetric

Share

Gmail Facebook Twitter LinkedIn More