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Abstract
Image segmentation has a significant role in the virtual planning, execution, and evaluation of craniomaxillofacial (CMF) surgical procedures. This systematic review aims to evaluate and compare the image segmentation programs frequently used in the field of CMF surgery. A precise search strategy was employed to recognise suitable studies across several databases, using specific inclusion criteria and keywords. Various image segmentation programs that use different techniques, including thresholding, edge-based methods, region-based methods and machine learning-based methods, were investigated. Results were screened through Preferred Reporting Items for Systematic Reviews and Meta-Analyses. A total of 94 reports on the use of virtual surgical planning from January 1, 2014, to June 1, 2023, were obtained. The identified image segmentation programs were analysed, including factors such as program features, strengths, limitations, supported image modalities, and clinical applications. A qualified assessment of these programs was conducted on the basis of parameters such as segmentation accuracy, processing speed, robustness, user-friendliness and integration capabilities. The review also addresses challenges faced by current segmentation programs and outlines future directions for advancement, including the standardised validation metrics and the integration of artificial intelligence. Surgical procedures were assigned into seven categories for analysis: cranial reconstructions, facial rejuvenation, orthognathic surgery, trauma repair, tumour resection, cleft lip and palate and patient specific implant. Amongst the software that could be used for bone segmentation in CMF region, eight software programs are most frequently used. Results showed that the Materialise suite was the most widespread tool for bone segmentation programs, with a prevalence of 50%, followed by the 3D slicer. This review underlines the principal significance of image segmentation in CMF surgery and offers valuable insights for clinicians and researchers to make informed decisions regarding the selection and utilisation of image segmentation programs.
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