Lumbar intervertebral disc segmentation for computer modeling and simulation

R Matos; P R Fernandes; N Matela; A P G Castro

doi:10.1016/j.cmpb.2023.107337

Lumbar intervertebral disc segmentation for computer modeling and simulation

Comput Methods Programs Biomed. 2023 Mar:230:107337. doi: 10.1016/j.cmpb.2023.107337. Epub 2023 Jan 5.

Authors

R Matos¹, P R Fernandes², N Matela³, A P G Castro⁴

Affiliations

¹ Faculdade de Ciências da Universidade de Lisboa, 1749-016 Lisbon, Portugal.
² IDMEC, Instituto Superior Técnico, Universidade de Lisboa, 1049-001 Lisbon, Portugal.
³ Faculdade de Ciências da Universidade de Lisboa, 1749-016 Lisbon, Portugal; IBEB, Faculdade de Ciências da Universidade de Lisboa, 1749-016 Lisbon, Portugal.
⁴ IDMEC, Instituto Superior Técnico, Universidade de Lisboa, 1049-001 Lisbon, Portugal; ESTSetúbal, Instituto Politécnico de Setúbal, 2910-761 Setúbal, Portugal. Electronic address: andre.castro@tecnico.ulisboa.pt.

PMID: 36634387
DOI: 10.1016/j.cmpb.2023.107337

Abstract

Background and objective: The present work had as its main objective the development of a method for localizing and automatically segmenting lumbar intervertebral discs (IVD) in 3D from magnetic resonance imaging (MRI), with the goal of supporting the generation of finite element (FE) models from actual lumbar spine anatomy, by providing accurate and personalized information on the shape of the patient's IVD. The extension of the method to allow performing separate segmentations of the IVD's two main structures - annulus fibrosus (AF) and nucleus pulposus (NP) - as well as automatically detecting degenerated IVD where this distinction is no longer possible was also an objective of the work.

Methods: The method presented here evolves from 2D segmentations in the sagittal profile using Gabor filters towards 3D segmentations. It works by detecting the spine curves and intensity regions corresponding to IVD. As so, the 2D method from Zhu et al. (2013) was partially implemented, modified and adapted to 3D use, and then tested with eight spines from two separated online datasets. The 3D adaptation was achieved by using vertebral body segmentation masks to approximate the shape of the vertebrae and to adjust the spine curves accordingly.

Results: The method showed average values of 85%, 83% and 96% for the Dice coefficient, sensitivity and specificity, respectively. The method correctly identified 65 of 68 (96%) IVD as either healthy or degenerated. The method's Dice coefficient is within the range of existing 3D IVD segmentation methods in the literature (81-92%). The method took on average 6-7 s to perform a full 3D segmentation, which is well within the range of the existing methods (2 s - 19 min).

Conclusions: The developed method can be used to generate accurate 3D models of the IVD based on MRI, with AF/NP distinction and detection of marked degeneration by comparing each IVD with the remaining spine levels. Further work shall improve the method towards distinguishing between specific levels of degeneration for clinically oriented FE modeling.

Keywords: Degenerative disc disease; Gabor filter; Image segmentation; Intervertebral disc; Magnetic resonance imaging.

MeSH terms

Computer Simulation
Computers
Humans
Intervertebral Disc Degeneration* / diagnostic imaging
Intervertebral Disc* / diagnostic imaging
Intervertebral Disc* / pathology
Lumbar Vertebrae / diagnostic imaging
Magnetic Resonance Imaging / methods