Automatic segmentation of facial soft tissue in MRI data based on non-rigid normalization in application to soft tissue thickness measurement

2020 ◽  
Vol 56 ◽  
pp. 101698 ◽  
Author(s):  
Iryna Gorbenko ◽  
Krzysztof Mikołajczyk ◽  
Magdalena Jasionowska ◽  
Jerzy Narloch ◽  
Krzysztof Kałużyński
Keyword(s):  
Soft Tissue ◽  
Automatic Segmentation ◽  
Thickness Measurement ◽  
Tissue Thickness ◽  
Facial Soft Tissue ◽  
Soft Tissue Thickness

A dense approach for computation of facial soft tissue thickness data

2021 ◽  
pp. 200460
Author(s):  
Diana Toneva ◽  
Silviya Nikolova ◽  
Stanislav Harizanov ◽  
Dora Zlatareva ◽  
Vassil Hadjidekov
Keyword(s):  
Soft Tissue ◽  
Tissue Thickness ◽  
Facial Soft Tissue ◽  
Soft Tissue Thickness

Midsagittal facial soft tissue thickness norms in an adult Mediterranean population

2019 ◽  
Vol 294 ◽  
pp. 217.e1-217.e7 ◽  
Author(s):  
Fouad Ayoub ◽  
Maria Saadeh ◽  
Georges Rouhana ◽  
Ramzi Haddad
Keyword(s):  
Soft Tissue ◽  
Mediterranean Population ◽  
Tissue Thickness ◽  
Facial Soft Tissue ◽  
Soft Tissue Thickness

Testing regression and mean model approaches to facial soft-tissue thickness estimation

2020 ◽  
pp. 002580242097701
Author(s):  
Tobias MR Houlton ◽  
Nicolene Jooste ◽  
Maryna Steyn
Keyword(s):  
Soft Tissue ◽  
Regression Model ◽  
South African ◽  
Goodness Of Fit ◽  
Regression Equations ◽  
Tissue Thickness ◽  
Facial Soft Tissue ◽  
Soft Tissue Thickness ◽  
Subject Specific ◽  
Model Approach

Average facial soft-tissue thickness (FSTT) databanks are continuously developed and applied within craniofacial identification. This study considered and tested a subject-specific regression model alternative for estimating the FSTT values for oral midline landmarks using skeletal projection measurements. Measurements were taken from cone-beam computed tomography scans of 100 South African individuals (60 male, 40 female; Mage = 35 years). Regression equations incorporating sex categories were generated. This significantly improved the goodness-of-fit ( r2-value). Validation tests compared the constructed regression models with mean FSTT data collected from this study, existing South African FSTT data, a universal total weighted mean approach with pooled demographic data and collection techniques and a regression model approach that uses bizygomatic width and maximum cranial breadth dimensions. The generated regression equations demonstrated individualised results, presenting a total mean inaccuracy (TMI) of 1.53 mm using dental projection measurements and 1.55 mm using cemento-enamel junction projection measurements. These slightly outperformed most tested mean models (TMI ranged from 1.42 to 4.43 mm), and substantially outperformed the pre-existing regression model approach (TMI = 5.12 mm). The newly devised regressions offer a subject-specific solution to FSTT estimation within a South African population. A continued development in sample size and validation testing may help substantiate its application within craniofacial identification.

scholarly journals Variation of Facial Soft Tissue Thickness in Nepalese Adult Orthodontic Subjects

2018 ◽  
Vol 8 (2) ◽  
pp. 22-28
Author(s):  
Ravi Kumar Mahto ◽  
Dashrath Kafle ◽  
Pankaj Kumar Singh ◽  
Sonika Khanal ◽  
Siddhartha Khanal
Keyword(s):  
Soft Tissue ◽  
Measurement Data ◽  
Tissue Thickness ◽  
Facial Soft Tissue ◽  
Male And Female ◽  
Skeletal Class ◽  
Skeletal Malocclusion ◽  
Soft Tissue Thickness ◽  
Significant Difference ◽  
Female Subjects

Introduction: Variations in facial soft tissue thickness have been established previously by studies conducted in different population. Hence, it is essential to obtain facial soft tissue thickness measurement data specific to a population and develop individual standards. The objective of this research is to obtain facial soft tissue thickness data of Nepalese adult male and female subjects seeking orthodontic treatment with different sagittal skeletal malocclusion and evaluate variations in facial soft tissue thickness. Materials & Method: Facial soft tissue thicknesses was measured manually on ninety pretreatment lateral cephalogram at eleven points (Glabella, Nasion, Rhinion, Subnasale, Labrale superius, Stomion, Labrale inferius, Labiomentale, Pogonion,Gnathion and Menton). One-way Analysis of variances [one-way ANOVA] followed by Least significant difference (LSD) post hoc test was used to determine difference in facial soft tissue thickness measurements among three sagittal skeletal group for both sexes. In addition, Student’s t-test was used to find difference in facial soft tissue thickness between the male and female subjects in each skeletal Class. Result: Statistically significant differences were found at points Rhinion, Subnasale, Labrale superius, Stomion and Gnathion in males and at Subnasale, Labrale superius, Stomion and Labrale inferius in females while comparing facial soft tissue thickness among three sagittal skeletal classes. Also, it was observed that mean facial soft tissue thickness was greater for males as compared to female subjects with significant differences at Subnasale, Labrale superius, and Labrale inferius in each skeletal Class. Conclusion: Facial soft tissue thickness varies considerably among different population group, sex and sagittal relationship of jaws.

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