Sexual Dimorphism in the Vertebral Column

Determining sex from skeletal remains is important in forensic and archaeological settings. Though using the pelvis to determine sex is ideal, often remains are fragmentary or incomplete, requiring sex to be estimated from other skeletal elements. Many individual bones have been studied to evaluate sexual dimorphism and the extent to which they can be used to determine sex of an unknown individual. However, sexual dimorphism in the vertebral column has only been examined to a limited extent. The purpose of this study is to examine the extent of sexual dimorphism throughout the entire vertebral column and, if present, to establish a method by which sex can be determined from any given vertebra, even if the exact vertebral number is not known. A total of 16 different measurements were taken on the vertebrae from a sample of 119 individuals from the William M. Bass Skeletal Collection. Given the small representation of African American individuals in the collection, only individuals of European descent were considered in this study. Since possible effects of aging were to be considered, equal numbers of males and females were randomly selected and matched for age groups. First MANOVA analyses were performed on each vertebrae and vertebral grouping, i.e. cervical C3-C7, thoracic, lumbar, and vertebral column C3-L5, to determine if each was significant for sex for each measurement taken. A stepwise analysis and then discriminant function analysis was performed to select the most sexually dimorphic measurements for each vertebra or vertebral grouping and equations were developed to allow sex to be determined from an unknown individual for each vertebra, or if the vertebral number is not known, from the vertebral grouping

Virtual morphometric method using seven cervical vertebrae for sex estimation on the Turkish population

Sex estimation from skeletal remains is crucial for the estimation of the biological profile of an individual. Although the most commonly used bones for means of sex estimation are the pelvis and the skull, research has shown that acceptable accuracy rates might be achieved by using other skeletal elements such as vertebrae. This study aims to contribute to the development of sex estimation standards from a Turkish population through the examination of CT scans from the seven cervical vertebrae. A total of 294 individuals were included in this study. The CT scans were obtained from patients attending the Bakirkoy Training and Research Hospital (Turkey) and the data was collected retrospectively by virtually taking measurements from each cervical vertebrae. The full database was divided into a training set (N = 210) and a validation set (N = 84) to test the fit of the models. Observer error was assessed through technical error of measurement and sex differences were explored using parametric and non-parametric approaches. Logistic regression was applied in order to explore different combinations of vertebral parameters. The results showed low intra- and inter-observer errors. All parameters presented statistically significant differences between the sexes and a total of 15 univariate and multivariate models were generated producing accuracies ranging from a minimum of 83.30% to a maximum of 91.40% for a model including three parameters collected from four vertebrae. This study presents a virtual method using cervical vertebrae for sex estimation on the Turkish population providing error rates comparable to other metric studies conducted on the postcranial skeleton. The presented results contribute not only to the development of population-specific standards but also to the generation of virtual methods that can be tested, validated, and further examined in future forensic cases

Fully automatic cervical vertebrae segmentation framework for X-ray images

This is the author accepted manuscript. The final version is available from Elsevier via the DOI in this record.The cervical spine is a highly flexible anatomy and therefore vulnerable to injuries. Unfortunately, a large number of injuries in lateral cervical X-ray images remain undiagnosed due to human errors. Computer-aided injury detection has the potential to reduce the risk of misdiagnosis. Towards building an automatic injury detection system, in this paper, we propose a deep learning-based fully automatic framework for segmentation of cervical vertebrae in X-ray images. The framework first localizes the spinal region in the image using a deep fully convolutional neural network. Then vertebra centers are localized using a novel deep probabilistic spatial regression network. Finally, a novel shape-aware deep segmentation network is used to segment the vertebrae in the image. The framework can take an X-ray image and produce a vertebrae segmentation result without any manual intervention. Each block of the fully automatic framework has been trained on a set of 124 X-ray images and tested on another 172 images, all collected from real-life hospital emergency rooms. A Dice similarity coefficient of 0.84 and a shape error of 1.69 mm have been achieved

Cervical Vertebral Maturation Stage as a Growth Predictor

The purposes of this study were to establish the reproducibility of skeletal age assessment as determined by the stage of cervical vertebral maturation (CVM) and to assess the ability of the CVM method to predict timing of peak mandibular growth velocity (PMdGV). The longitudinal records of 104 females (age 8 to 14 inclusive) were used to determine skeletal age (as assessed by the CVM) and mandibular length. Reproducibility of skeletal age estimates was tested by comparing five sets of first and second determinations done 2 months apart for 20 subjects chosen from the total sample before and after the principal operator calibration. The reproducibility of skeletal age assessments done prior to calibration was unacceptable. The reproducibility improved to acceptable limits following calibration. Improved definitions, the addition of an extra stage and the development of a Sequential Conditional Flow Chart rendered the modified CVM method, introduced in this study, even more reproducible. The kappa for 20 double assessments of the timing of PMdGV was 59% (not acceptable) but of the 55 subjects for whom two determinations of timing of PMdGV coincided, only 61% were at cervical vertebral stage 3 thus lending some measure of uncertainty to the use of the cervical vertebral maturation method for predicting timing of PMdGV

Cervical Vertebral Maturation Stage as a Growth Predictor

I would like to express my sincere appreciation to the members of my thesis committee: Dr Stuart Hunter, Dr Antonios Mamandras, Dr Lesley Short, Dr David Banting, Dr John Murray and Dr Brian Tompson. In particular, thank you to my thesis supervisor, Dr Stuart Hunter, for your help and support during my thesis project. You are an inspiration and a role model to me. A special thank to Dr Antonios Mamandras and to Dr Lesley Short for their help and support through the whole program. You made my experience at Western unforgettable. For all the support provided to me in completing this thesis and this program, thank you Barb Merner, Joanne Pfaff, Leesa Couper, Evelyn Larios, PJ Blake, Justina Hovarth, Jackie Geneau and Cynthia Mugimba. You made my experience at Western memorable. I want to say thanks to all of my co-residents: Nadia, Mitch, Dolly, Ali, Mark, Mike, Mariela, Manisha, Neville, Dana, Phil and Julia. Thank you for your comradery, support and humour. To my classmates Ziad and Julia, the past three years have been a true learning experience. Thank you for being such good classmates. I will never forget all the special moments we shared together. Most importantly, I dedicate this thesis to my entire family. To my husband Jalal, thank you for your love, patience and support. Thank you for believing in me and for supporting me in every step of the way. I couldn’t have done it without you. To Mom and Dad, thank you for believing in me every day of my life. Your love and support over the last three years allowed me to follow my dream. I am eternally grateful. To my adorable daughter Mayali, I know we will be able to make up for lost time together but know you are my raison d’être and the sunshine in my life

Sexual dimorphism from vertebrae: its potential use for sex estimation in an identified Portuguese sample.

In archaeological and medicolegal contexts, sex estimation is a crucial parameter for personal identification. However, it can be a complex task if the skeletal remains are damaged or fragmented. For this reason, it is important to establish reliable methodologies and techniques using alternative sexually dimorphic anatomical regions other than pelvic and skull, such as vertebrae. The purpose of the current study was to evaluate the level of sexual dimorphism of first, second and seventh cervical and twelfth thoracic vertebrae from the Coimbra Identified Skeletal Collection of the University of Coimbra (Portugal) and to develop logistic regression equations for sex estimation based on metric data from these vertebrae. The sample comprised 73 individuals (38 males and 35 females) with a mean age of 50.10 ± 18.34 years. Eleven multivariate logistic regression equations were developed with accuracy rates between 80.0% and 92.5%. The first cervical vertebra demonstrated to be useful for sex diagnosis when more sexually dimorphic anatomical regions (i.e., pelvis and skull) are not available or suitable for analysis.pre-print325 K

A Framework of Vertebra Segmentation Using the Active Shape Model-Based Approach

We propose a medical image segmentation approach based on the Active Shape Model theory. We apply this method for cervical vertebra detection. The main advantage of this approach is the application of a statistical model created after a training stage. Thus, the knowledge and interaction of the domain expert intervene in this approach. Our application allows the use of two different models, that is, a global one (with several vertebrae) and a local one (with a single vertebra). Two modes of segmentation are also proposed: manual and semiautomatic. For the manual mode, only two points are selected by the user on a given image. The first point needs to be close to the lower anterior corner of the last vertebra and the second near the upper anterior corner of the first vertebra. These two points are required to initialize the segmentation process. We propose to use the Harris corner detector combined with three successive filters to carry out the semiautomatic process. The results obtained on a large set of X-ray images are very promising