Automatic computation of bone defective volume from tomographic images

Area of Science:

• Biomaterial Science
• Medical Imaging
• Computer-Aided Surgery

Background:

• Accurate bone defect volume prediction is crucial for effective bone substitute implantation.
• Conventional image segmentation methods struggle to isolate bone defects from surrounding tissues and background noise.
• Microtomography imaging generates detailed data but requires precise segmentation for volumetric analysis.

Purpose of the Study:

• To develop and evaluate an adaptive morphological operation technique for precise bone defect segmentation.
• To accurately calculate the volume of bone defects for improved biomaterial implementation.
• To demonstrate the method's efficacy in both artificial and real bone defect scenarios.

Main Methods:

• Created a bone defect in dead bone, imaged using microtomography (343 cuts).
• Employed an adaptive morphological operation technique for defect segmentation and volume calculation.
• Validated the method using calculated phantoms and a direct application on mandibular bone (1236 cuts).

Main Results:

• The proposed method achieved exact segmentation and volume calculation of bone defects.
• Demonstrated significantly lower error rates compared to conventional segmentation methods.
• Accurate volume calculation (bone defect: 0.91 cm³, cement paste: 0.87 cm³) and time savings (average 2 min gain) were observed.

Conclusions:

• The adaptive morphological operation technique offers precise bone defect segmentation and volumetric analysis.
• This automated method enhances accuracy and efficiency in biomaterial science and implantology.
• The technique is beneficial for guiding surgical acts and in computer-aided scaffolding applications.