Automatic computation of bone defective volume from tomographic images

M Ezzahmouly; A Essakhi; A El Ouahli; H El Byad; M Ed-Dhahraouy; S Hakim; E Gourri; A ELmoutaouakkil; Z Hatim

doi:10.1016/j.heliyon.2022.e09594

Automatic computation of bone defective volume from tomographic images

Heliyon. 2022 May 30;8(6):e09594. doi: 10.1016/j.heliyon.2022.e09594. eCollection 2022 Jun.

Authors

M Ezzahmouly^{1

2}, A Essakhi³, A El Ouahli², H El Byad^{1

2}, M Ed-Dhahraouy¹, S Hakim¹, E Gourri², A ELmoutaouakkil¹, Z Hatim²

Affiliations

¹ Research Laboratory in Optimization, Emerging Systems, Networks and Imaging, LAROSERI, Computer Science Department, Faculty of Sciences, University Chouaib Doukkali, El Jadida, Morocco.
² Energy, Materials and Environment Team, Chemistry Department, Faculty of Sciences, University Chouaib Doukkali, El Jadida, Morocco.
³ Laboratory of Renewable Energy and Systems Dynamics, Faculty of Sciences Ain Chok, Casablanca, Morocco.

Abstract

One of the most difficult aims of modern biomaterial science is predicting the shape and volume of a bone defect and adjusting the implementation of a bone substitute. Prior to implantation, practitioners must carefully identify the architecture and volume of the defective bone to be filled. This information is often accessed via imaging techniques. The defective bone is frequently confused with its surroundings and the image background. The use of conventional segmentation for the selection and isolation of the cavity to be filled proves to be difficult. In this work, a defect in a dead bone is created and then imaged with the microtomography technique (343 cuts generated). The goal is to separate the defect's shape and volume from both the bone and the background image. An adaptive morphological operation technique was employed to complete these tasks. The proposed method allows for exact segmentation and calculation of the volume of the cavity to be filled. Using several calculated phantoms, the approach is subjectively and quantitatively evaluated: Compared to the high error value of the conventional method, the error value of the proposed one has no bearing on the overall data. The method's accuracy was also confirmed by comparing the calculated volume of the bone defect (0.91 cm³) and the volume of prepared calcium phosphate cement paste necessary for its filling (0.87 cm³). To challenge the method even further, another direct application on a mandibular bone is realized with an advanced number of cuts (1236 cuts). The result of this application proved that the proposed algorithm overcomes the performance of the classical approaches of segmentation with a gain of 2 min on average. A comparison study between the proposed method and other classical segmentation approaches is also presented. The effectiveness of the method is proved by the various reports and metrics generated. The automated procedure can be beneficial in implantology for realizing and guiding surgical acts, as well as in computer-aided scaffolding techniques.

Keywords: Biomaterial cement; Bone defect; Microtomography; Morphological operation; Segmentation.