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Background: Different lateral and postero-lateral cranial approaches to the petroclival region and to the mid-upper brain stem have been described so far, some of which require extensive osseous demolition and possible damage of neurovascular structures. Neuronavigational systems are now extensively available for preoperative planning and intraoperative navigation to assist the surgeons in choosing the optimally invasive approach for each pathology. Herein, we describe a detailed navigation-augmented morphometric analysis to bring insight into the usefulness of an anterior petrosectomy (AP) to handle lesions in the petroclival region. Methods: Eight cadaveric, silicone injected heads were used. A total of 14 approaches (AP, n = 8; retrosigmoid, RS, n = 6) using a standard microsurgical dissection technique were performed. All specimens had preoperative CT and MRI scans, as well as a post-dissection CT. The neuronavigational system was used for distance measurements, craniotomy sizes and surgical corridor volumes, for each approach. Results: The distance from the skull surface to the petrous apex was significantly shorter in the AP approach when compared with the RS (46.0 ± 1.9 mm versus 71.3 ± 1.8 mm, respectively, p < 0.001). Although the craniotomy size was not different, the volume of the surgical corridor was significantly larger with the AP approach (21.31 ± 1.91 cm3 vs. 13.39 ± 1.8 cm3). The AP approach increased the length of the basilar artery exposure from 6.9 ± 1.5 mm (obtained with a standard subtemporal approach) to 22.1 ± 1.7 mm (p < 0.0001). Conclusions: The surgical corridor to the petroclival region achieved by virtue of an AP was significantly larger and featured shorter working distances, resulting in a higher degree of surgical freedom. Although significant individual anatomical variations of fundamental neurovascular and bony structures were found, these difficulties were overcome by careful pre- and intraoperative use of neuronavigation.

Mindboggle (http://mindboggle.info) is an open source brain morphometry platform that takes in preprocessed T1-weighted MRI data and outputs volume, surface, and tabular data containing label, feature, and shape information for further analysis. In this article, we document the software and demonstrate its use in studies of shape variation in healthy and diseased humans. The number of different shape measures and the size of the populations make this the largest and most detailed shape analysis of human brains ever conducted. Brain image morphometry shows great potential for providing much-needed biological markers for diagnosing, tracking, and predicting progression of mental health disorders. Very few software algorithms provide more than measures of volume and cortical thickness, while more subtle shape measures may provide more sensitive and specific biomarkers. Mindboggle computes a variety of (primarily surface-based) shapes: area, volume, thickness, curvature, depth, Laplace-Beltrami spectra, Zernike moments, etc. We evaluate Mindboggle's algorithms using the largest set of manually labeled, publicly available brain images in the world and compare them against state-of-the-art algorithms where they exist. All data, code, and results of these evaluations are publicly available.

Vertebrate models provide indispensable paradigms to study development and disease. Their analysis requires a quantitative morphometric study of the body, organs and tissues. This is often impeded by pigmentation and sample size. X-ray micro-computed tomography (micro-CT) allows high-resolution volumetric tissue analysis, largely independent of sample size and transparency to visual light. Importantly, micro-CT data are inherently quantitative. We report a complete pipeline of high-throughput 3D data acquisition and image analysis, including tissue preparation and contrast enhancement for micro-CT imaging down to cellular resolution, automated data processing and organ or tissue segmentation that is applicable to comparative 3D morphometrics of small vertebrates. Applied to medaka fish, we first create an annotated anatomical atlas of the entire body, including inner organs as a quantitative morphological description of an adult individual. This atlas serves as a reference model for comparative studies. Using isogenic medaka strains we show that comparative 3D morphometrics of individuals permits identification of quantitative strain-specific traits. Thus, our pipeline enables high resolution morphological analysis as a basis for genotype-phenotype association studies of complex genetic traits in vertebrates.

Landmark digitization is essential in geometric morphometrics. It enables the quantification of biological shapes, such as facial structures. Traditional landmarking, which identifies specific anatomical points, can be complemented by semilandmarks when precise locations are challenging to define. However, manual placement of numerous landmarks is time-consuming and prone to human error, leading to inconsistencies across studies. To address this, we introduce FaceDig, an AI-powered tool designed to automate landmark placement with human-level precision, focusing on anatomically sound facial points. FaceDig is open-source and integrates seamlessly with analytical platforms like R and Python. It was trained using one of the largest and most ethnically diverse face dataset, applying a landmark configuration optimized for 2D enface photographs. Our results demonstrate that FaceDig provides reliable landmark coordinates, comparable to those placed manually by experts. The tool’s output is compatible with the widely-used TpsDig2 software, which facilitates adoption and ensures consistency across studies. Users are advised to work with standardized facial images and visually inspect the results for potential corrections. Despite the growing preference for 3D morphometrics, 2D facial photographs remain valuable due to their cultural and practical significance. Future enhancements to FaceDig will include support for profile views, further expanding its utility. By offering a standardized approach to landmark placement, FaceDig promotes reproducibility in facial morphology research and provides a robust alternative to existing 2D tools.

Accurate characterization of morphological variation is crucial for generating reliable results and conclusions concerning changes and differences in form. Despite the prevalence of landmark-based geometric morphometric (GM) data in the scientific literature, a formal treatment of whether sampled landmarks adequately capture shape variation has remained elusive. Here, I introduce LaSEC (Landmark Sampling Evaluation Curve), a computational tool to assess the fidelity of morphological characterization by landmarks. This task is achieved by calculating how subsampled data converge to the pattern of shape variation in the full dataset as landmark sampling is increased incrementally. While the number of landmarks needed for adequate shape variation is dependent on individual datasets, LaSEC helps the user (1) identify under- and oversampling of landmarks; (2) assess robustness of morphological characterization; and (3) determine the number of landmarks that can be removed without compromising shape information. In practice, this knowledge could reduce time and cost associated with data collection, maintain statistical power in certain analyses, and enable the incorporation of incomplete, but important, specimens to the dataset. Results based on simulated shape data also reveal general properties of landmark data, including statistical consistency where sampling additional landmarks has the tendency to asymptotically improve the accuracy of morphological characterization. As landmark-based GM data become more widely adopted, LaSEC provides a systematic approach to evaluate and refine the collection of shape data--a goal paramount for accumulation and analysis of accurate morphological information.

PurposeS1 alar iliac (S1AI) trajectory has gained popularity as a salvage technique for revision surgeries and failed constructs in the lumbopelvic region. This study aims to investigate the morphometry of this new trajectory based on 3D models. The possible role of gender, ethnicity and view angle (surgeon’s vs. radiologist’s) was investigated.MethodsComputed tomography-driven virtual 3D models of spinopelvic region were created applying Materialize MIMICS software, and assessed for coronal and sagittal radiographic versus surgeon’s view angles, and morphometry of the screw trajectory. Independent-samples t test was used to analyze the results. P value was set at <  = 0.05. The Statistical Package for the Social Sciences Software (SPSS version 24.0) was used for the statistical analysis.ResultsA total of 164 3D models were simulated with a total 328 screws inserted satisfactorily within the S1AI trajectory. S1AI instrumentation was feasible in 96.48%. The mean radiological coronal angle was 50.619' ± 8.590' and the mean coronal angle for surgeons’ perspective was 10.263' ± 5.860'. The mean radiological and surgeon’s perspective sagittal angles were found to be 44.532' ± 6.424' and 31.164' ± 5.455', respectively. A statistically significant difference was found between anatomical and surgeon’s perspective trajectories. Neither the pelvic laterality nor the gender influence the screw angles, length and diameter in radiological versus surgeon’s view angles.ConclusionPreoperative 3D modeling would be an invaluable adjunct to increase the accuracy of S1AI screw placement. Surgeon’s perspective of the trajectory differs from standard CT sections and should be considered in preoperative planning.

Background The management of multilevel cervical spondylosis with concomitant foraminal stenosis and instability remains challenging. Anterior-only, posterior-only, and circumferential procedures each present disadvantages regarding decompression adequacy, implant burden, and complication risk. We developed an integrated posterior system combining facet joint fusion and semi-open-door laminoplasty to enable decompression and stabilization through a single approach. Methods Thirty cervical CT scans from healthy adults (C2-C7) were reconstructed in Mimics. Key morphometric parameters relevant to device design were assessed, including lateral mass height and facet dimensions, minimum lamina height, lamina safety length, interfacet gap height, facet inclination, and spinous process screw height. A concept construct comprising a lateral mass plate, interfacet fusion cage, and laminoplasty plate was dimensioned from these data and virtually implanted to evaluate anatomical compatibility. Bilateral and sex-related differences were analyzed (two-tailed α = 0.05) with prespecified assumptions for parametric testing and multiple-comparison control. Results Key morphometric parameters (e.g., lateral mass height: 11.5–13.3 mm; lamina safety length: 25.3–28.6 mm) supported the dimensional design of the system components. Statistical analysis showed no significant differences related to sex or side (all p  > 0.05). Crucially, Mimics-guided virtual implantation demonstrated successful positioning of all components without cortical breach, validating the anatomical compatibility of the construct. Conclusion CT-based morphometry and Mimics-guided virtual implantation demonstrate that the proposed facet-fusion-integrated posterior cervical semi–open-door system is anatomically feasible and dimensionally compatible with subaxial cervical anatomy. However, as this study is limited to imaging data and virtual simulation without biomechanical or clinical validation, the system should currently be regarded as a proof-of-concept design rather than a clinically established technique.

Background and Objectives: The landmark for neurosurgical approaches to access brain lesion is the pterion. The aim of the present study is to classify and examine the prevalence of all types of pterion variations and perform morphometric measurements from previously defined anthropological landmarks. Materials and methods: One-hundred and twenty-four Thai dried skulls were investigated. Classification and morphometric measurement of the pterion was performed. Machine learning models were also used to interpret the morphometric findings with respect to sex and age estimation. Results: Spheno-parietal type was the most common type (62.1%), followed by epipteric (11.7%), fronto-temporal (5.2%) and stellate (1.2%). Complete synostosis of the pterion suture was present in 18.5% and was only present in males. While most morphometric measurements were similar between males and females, the distances from the pterion center to the mastoid process and to the external occipital protuberance were longer in males. Random forest algorithm could predict sex with 80.7% accuracy (root mean square error = 0.38) when the pterion morphometric data were provided. Correlational analysis indicated that the distances from the pterion center to the anterior aspect of the frontozygomatic suture and to the zygomatic angle were positively correlated with age, which may serve as basis for age estimation in the future. Conclusions: Further studies are needed to explore the use of machine learning in anatomical studies and morphometry-based sex and age estimation. Thorough understanding of the anatomy of the pterion is clinically useful when planning pterional craniotomy, particularly when the position of the pterion may change with age.

Key messageAnatomical traits and features of Amphipterygium adstringens wood and hydraulic properties, both linked to sex, explain the performance of the species in its ecological niche.Androic and gynoic dioecious species respond structurally and functionally to the environmental conditions where they live. How are adaptive advantages manifested by each sex, from the anatomical water transport system to gain hydraulic efficiency? How does the species use the ecological niche by each sex in a dry competitive environment? Amphipterygium adstringens is an endemic, dioecious tree in Mexico. We analyzed the morphometric anatomical features of wood samples from trees to differentiate the functionality of the xylematic system by sex. Physiological indices such as relative hydraulic conductivity, vessel grouping, and vulnerability were estimated. A discriminant analysis was carried out to differentiate hydraulic efficiency by sex. The ecological niches of both sexes were defined using Ripley’s bivariate function. The sizes of the vessels and fibers were significantly higher in the gynoecious wood samples than in the androecious wood samples. Rays in the androecious wood samples were larger than those in the gynoecious wood samples. There was a significant difference between sexes in all the indices estimated. The discriminant analysis showed that gynoecious trees have a better functional response, are able to better adapt to drought, and have higher water transport security. The spatial correlation pattern between adults of both sexes showed independence. Hydraulic efficiency and security are the key features of gynoecious tree survival, while vulnerability to cavitation is a risk factor for androecious trees. The performance of A. adstringens based on wood anatomical traits and hydraulic properties revealed an advantage for gynoecious trees, and susceptibility was linked to androecious trees.

Geometric morphometrics is the statistical analysis of landmark-based shape variation and its covariation with other variables. Over the past two decades, the gold standard of landmark data acquisition has been manual detection by a single observer. This approach has proven accurate and reliable in small-scale investigations. However, big data initiatives are increasingly common in biology and morphometrics. This requires fast, automated, and standardized data collection. We combine techniques from image registration, geometric morphometrics, and deep learning to automate and optimize anatomical landmark detection. We test our method on high-resolution, micro-computed tomography images of adult mouse skulls. To ensure generalizability, we use a morphologically diverse sample and implement fundamentally different deformable registration algorithms. Compared to landmarks derived from conventional image registration workflows, our optimized landmark data show up to a 39.1% reduction in average coordinate error and a 36.7% reduction in total distribution error. In addition, our landmark optimization produces estimates of the sample mean shape and variance–covariance structure that are statistically indistinguishable from expert manual estimates. For biological imaging datasets and morphometric research questions, our approach can eliminate the time and subjectivity of manual landmark detection whilst retaining the biological integrity of these expert annotations.