Biology subjects

Maga, A. M.

Publications and source records attributed to Maga, A. M..

17 recordsLinked to original sources

bioRxiv2026

To slide or not to slide, that is the question: evaluating dense semilandmarks and sliding in 3D geometric morphometrics with real and simulated data

Dense semilandmarks describe 3D surfaces with hundreds to thousands of points, and sliding them by bending energy or Procrustes distance is a near-universal default. Three questions remain open: does dense sampling add shape beyond fixed landmarks, how many points are needed, and does sliding help or harm? Real specimens cannot answer them: the true correspondence is unknown. We tested two workflows, ALPACA (single-template registration) and DeCAL (landmark-anchored correspondence), on 496 mouse skulls at 250-1,000 points, with and without sliding, scored by surface reconstruction. We repeated it on 500 synthetic skulls with exact correspondence, measuring each point's distance to its true homologue. Dense semilandmarks lowered error for almost every specimen; the fixed landmarks added little but supplied anchoring the semilandmarks could not, and the anchored method was more accurate. The benefit saturated near 250 points for ALPACA but kept improving to 1,000 for DeCAL. Procrustes-distance sliding harmed every configuration; bending-energy sliding helped only a poor, landmark-free correspondence, vanishing once anatomical anchors spanned the form. Match the sliding decision to the correspondence in hand: relax a poor one, leave a good one alone, never slide toward the mean. Known-correspondence specimens offer a general test of landmarking and sliding against ground truth.

evolutionary biology↗

bioRxiv2026

A new reference-invariant consensus template generation method in ALPACA

O_LIAutomated landmarking transfers anatomical landmarks from a reference specimen onto many targets, greatly increasing analytical throughput. However, this procedure needs to be bootstrap using an initial sample. An arbitrary or atypical choice imprints a reference-of-origin bias that propagates through the pseudo-landmarks, the resulting morphospace, and the downstream template selection, a risk that is difficult to avoid for large datasets whose variation is not yet understood. C_LIO_LIWe replace the fixed reference with an iterative consensus atlas, warped over a few iterations toward the Procrustes mean shape of all similarity-aligned specimens. We evaluated it on a 62-strain Mus musculus skull panel by running both the original fixed-reference pipeline and the new consensus pipeline 62 times each, using every specimen in turn as the bootstrap. We compared atlas convergence, inter-atlas similarity, morphospace reproducibility, reference-choice variance of pairwise Procrustes distances, downstream k-means selection stability, and leave-one-out out-of-sample fit, and tested generalisation on great-ape datasets of differing sampling balance. C_LIO_LIThe consensus atlas converged within a few iterations and was far less sensitive to the starting specimen than the fixed reference. It produced more reproducible morphospaces (mean RV 0.960 versus 0.944), reduced the reference-of-origin variance of pairwise distances by a median of about 60%, drew downstream template selections from a smaller and more consistent pool of specimens, and fit held-out specimens more closely in all 62 strains. On the great-ape data the atlases agreed closely in surface geometry, but the downstream morphospace became reference-dependent when the sample was taxonomically imbalanced, and a smaller balanced subset outperformed the larger imbalanced one. C_LIO_LIIterative consensus atlas building removes a persistent bias from automated landmarking and yields reference-invariant, reproducible results, with sampling balance mattering more than absolute sample size. Because the atlas stabilises quickly, it can be built from a small balanced subset while the remaining specimens are simply landmarked against it, a practical route to scaling reference-invariant landmarking. The method is implemented in ALPACA within SlicerMorph, with a mock library enabling headless use on HPC. C_LI

evolutionary biology↗

bioRxiv2025

Trustworthy detection of exencephaly in high-throughput micro-CT embryo screens with focal-loss transformers

1.Lethal and sub-viable knockout mouse lines require whole-embryo 3D imaging to connect genotype to phenotype (Dickinson et al., 2016; Cacheiro et al., 2022). There are often far fewer samples of in-class (e.g., homozygous knockouts) than wildtype or normative samples. Such extreme subject-level imbalance degrades both statistical anatomy and deep learning, often yielding saliency maps that highlight noise rather than lesion-specific signal (Adebayo et al., 2018; Buda et al., 2018; Johnson & Khoshgoftaar, 2019). We therefore asked whether focal loss (Lin et al., 2017) in combination with model-capacity control and seed ensembling can stabilize explanations without compromising classification accuracy. Exencephaly is an neural tube defect characterized by incomplete closure of the cephalic neural tube and dorsally exposed,disorganized neural tissue (Greene & Copp, 2014; Noden & de Lahunta, 1985). In our late-gestation screen, approximately 10% of embryos had exencephaly, creating severe class imbalance and underscoring the need for interpretable automation. As a test case for imbalance-aware, interpretable phenotyping, we analyzed 253 diceCT scans of E15.5 embryos (24 with exencephaly). A self-supervised transformer was fine-tuned with three regimes: cross-entropy (CE-Large), focal-loss equal-capacity (FL-Large) and focal-loss reduced-capacity (FL-Small). Five random seeds per regime yielded 15 models. Integrated Gradients saliency was quantified, and explanation quality was measured by saliency entropy (sparsity), cross-seed Dice/Jaccard similarity (reproducibility), and expert visual inspection. All 15 models achieved near perfect phenotype recognition on held-out data with 0.996 {+/-} 0.002 mean accuracy with some seeds/regimens reached 1.000. Focal loss reduced saliency entropy by up to 1.5 bits and doubled cross-seed Dice overlap, concentrating attribution on the malformed cranial vault. Ensemble-averaged heat-maps show that both focal-loss regimes concentrate attribution on the malformed cranial vault while suppressing spurious body-wide signals. Focal loss, modest capacity, and seed ensembling within a modified M3T transformer yielded sparse, reproducible, anatomically focused attribution while preserving perfect sensitivity. This supports trustworthy high-throughput phenotyping in severely imbalanced embryo screens. The workflow relies only on standard atlas registration and image pre-processing, requires no voxel-level annotations, and is readily adaptable to other structural malformations and developmental stages.

developmental biology↗

bioRxiv2025

Anatomy-aware, label-informed approach improves image registration for challenging datasets

Image registration-based volumetric morphometrics have emerged as a valuable method for identifying subtle morphological differences in neuroimaging and other biomedical images. However, accurate registration out-of-the-box remains challenging when overt morphological phenotypes--such as those observed in developmental and comparative studies--are present in a dataset. A new label-informed image registration function developed in the ANTsX ecosystem provides an easy to use, generalizable solution for anatomy-aware registration of a wide diversity of morphological variation. In this approach, segmentations (i.e., labels) provide a priori regional correspondences that guide the registration. These labels can be generated by any method-manually, using semi-automated tools, or through deep learning-based approaches-and allow morphological experts to define regions of correspondence based on biological concepts of homology (e.g., tissue origin, gene expression patterns). Here we demonstrate the utility of this label-informed image registration approach for improving the registration knockout mouse embryos which fail to register to a wildtype (normative) template image by traditional registration methods. E15.5 Gli2-/- mouse embryos show a severe scoliosis and radical topological rearrangement of the internal organs. Compared to traditional, intensity-only registration, the new label-informed image registration improved the correspondence of knockout subjects to the canonical template image, which resulted in increased power and sensitivity of downstream statistical analyses. All in all, label-informed image registration provides a flexible and customizable method to allow image registration in datasets for which registration-based morphometrics were previously unfeasible, unlocking new potential applications of registration-based morphometrics in developmental, comparative, and evolutionary studies.

developmental biology↗

bioRxiv2025

SlicerMorph Photogrammetry: An Open-Source Photogrammetry Workflow for Reconstructing 3D Models

1. ContextAccurate three-dimensional (3D) models of skeletal and other biological specimens are crucial for ecological and evolutionary research and teaching. Here, we present a streamlined, open-source workflow for 3D photogrammetry that allows researchers to construct 3D models using the 3D Slicer and the SlicerMorph ecosystem. 2. Objectives and MethodsWe present an updated photogrammetry pipeline within the 3D Slicer ecosystem that combines advanced image masking via Segment Anything Model (SAM) with the open-source ODM reconstruction engine. Our approach systematically reduces artifacts in delicate cranial regions by automating background removal. We validate this pipeline against micro-CT references using multiple rodent skull specimens, quantifying accuracy through mean distance, root mean square error (RMSE), Hausdorff distance, and Chamfer distance. 3. ResultsOur method consistently reduces average surface error by 10-15% compared to a previous open-source photogrammetry workflow. This gain is particularly pronounced around thin or fenestrated anatomical structures such as zygomatic arches, where the previous workflow created the most noise. Although occasional increases in Hausdorff distance reflect localized photography gaps, improved mean distance and RMSE underscore improved overall fidelity. 4. Main ConclusionsThe 3D Slicer Photogrammetry extension provides a robust, fully open-source solution for generating high-quality 3D reconstructions from photographs. The streamlined pipeline lowers technical barriers to collecting reliable 3D data and fosters reproducible research across diverse biological collections.

evolutionary biology↗

bioRxiv2024

Streamlining Asymmetry Quantification in Fetal Mouse Imaging: A Semi-Automated Pipeline Supported by Expert Guidance

Asymmetry is a key feature of numerous developmental disorders and in phenotypic screens is often used as a readout for environmental or genetic perturbations to normal development. A better understanding of the genetic basis of asymmetry and its relationship to disease susceptibility will help unravel the complex genetic and environmental factors and their interactions that increase risk in a range of developmental disorders. Large-scale imaging datasets offer opportunities to work with sample sizes needed to detect and quantify differences in morphology beyond severe deformities while also posing challenges to manual phenotyping protocols. In this work, we introduce a semi-automated open-source workflow to quantify abnormal asymmetry of craniofacial structures that integrates expert anatomical knowledge. We apply this workflow to explore the role of genes contributing to abnormal asymmetry by deep phenotyping 3D fetal microCT images from knockout strains acquired as part of the Knockout Mouse Phenotyping Program (KOMP2). Four knockout strains: Ccdc186, Acvr2a, Nhlh1, and Fam20c were identified with highly significant asymmetry in craniofacial regions, making them good candidates for further analysis into their potential roles in asymmetry and developmental disorders. Summary StatementWe introduce an open-source, semi-automated pipeline to detect abnormally asymmetric phenotypes in 3D scans of fetal mice to explore the relationship between facial asymmetry, perturbed development, and developmental instability.

developmental biology↗

bioRxiv2024

Morphological simulation tests the limits on phenotype discovery in 3D image analysis

In the past few decades, advances in 3D imaging have created new opportunities for reverse genetic screens. Rapidly growing datasets of 3D images of genetic knockouts require high-throughput, automated computational approaches for identifying and characterizing new phenotypes. However, exploratory, discovery-oriented image analysis pipelines used to discover these phenotypes can be difficult to validate because, by their nature, the expected outcome is not known a priori. Introducing known morphological variation through simulation can help distinguish between real phenotypic differences and random variation; elucidate the effects of sample size; and test the sensitivity and reproducibility of morphometric analyses. Here we present a novel approach for 3D morphological simulation that uses open-source, open-access tools available in 3D Slicer, SlicerMorph, and Advanced Normalization Tools in R (ANTsR). While we focus on diffusible-iodine contrast-enhanced micro-CT (diceCT) images, this approach can be used on any volumetric image. We then use our simulated datasets to test whether tensor-based morphometry (TBM) can recover our introduced differences; to test how effect size and sample size affect detectability; and to determine the reproducibility of our results. In our approach to morphological simulation, we first generate a simulated deformation based on a reference image and then propagate this deformation to subjects using inverse transforms obtained from the registration of subjects to the reference. This produces a new dataset with a shifted population mean while retaining individual variability because each sample deforms more or less based on how different or similar it is from the reference. TBM is a widely-used technique that statistically compares local volume differences associated with local deformations. Our results showed that TBM recovered our introduced morphological differences, but that detectability was dependent on the effect size, the sample size, and the region of interest (ROI) included in the analysis. Detectability of subtle phenotypes can be improved both by increasing the sample size and by limiting analyses to specific body regions. However, it is not always feasible to increase sample sizes in screens of essential genes. Therefore, methodical use of ROIs is a promising way to increase the power of TBM to detect subtle phenotypes. Generating known morphological variation through simulation has broad applicability in developmental, evolutionary, and biomedical morphometrics and is a useful way to distinguish between a failure to detect morphological difference and a true lack of morphological difference. Morphological simulation can also be applied to AI-based supervised learning to augment datasets and overcome dataset limitations.

zoology↗

bioRxiv2024

Leveraging Descriptor Learning and Functional Map-based Shape Matching for Automatic Landmark Acquisition

Geometric morphometrics is widely employed across the biological sciences for the quantification of morphological traits. However, the scalability of these methods to large datasets is hampered by the requisite placement of landmarks, which can be laborious and time consuming if done manually. Additionally, the selected landmarks embody a particular hypothesis regarding the critical geometry pertinent to the biological inquiry at hand. Modifying this hypothesis lacks flexibility, necessitating the acquisition of an entirely new set of landmarks on the entire dataset to reflect any theoretical adjustments. In our research, we investigate the precision and accuracy of landmarks derived from the comprehensive set of functional correspondences acquired through the functional map framework of geometry processing. We use a deep functional map network to learn shape descriptors that effectively yield functional map-based and point-to-point correspondences between the specimens in our dataset. We then interrogate these maps to identify corresponding landmarks given manually placed landmarks from the entire dataset. We assess our method by automating the landmarking process on a dataset comprising mandibles from various rodent species, comparing its efficacy against MALPACA, a cutting-edge technique for automatic landmark placement. Compared to MALPACA, our model is notably faster and maintains competitive accuracy. The Root Mean Square Error (RMSE) analysis reveals that while MALPACA generally exhibits the lowest RMSE, our models perform comparably, especially with smaller training datasets, suggesting strong generalizability. Visual evaluations confirm the precision of our landmark placements, with deviations remaining within an acceptable range. These findings underscore the potential of unsupervised learning models in anatomical landmark placement, providing a viable and efficient alternative to traditional methods.

bioinformatics↗

bioRxiv2023

An open-source photogrammetry workflow for reconstructing 3D models

Acquiring accurate 3D biological models efficiently and economically is important for morphological data collection and analysis in organismal biology. In recent years, structure-from-motion (SFM) photogrammetry has become increasingly popular in biological research due to its flexibility and being relatively low cost. SFM photogrammetry registers 2D images for reconstructing camera positions as the basis for 3D modeling and texturing. However, most studies of organismal biology still rely on commercial software to reconstruct the 3D model from photographs, which impedes the adoption of this workflow in our field due the blocking issues such as cost and affordability. Also, prior investigations in photogrammetry did not sufficiently assess the geometric accuracy of the models reconstructed. Consequently, this study has two goals. First, we present an affordable and highly flexible structure-from-motion photogrammetry pipeline based on the open-source package OpenDroneMap (ODM) and its user interface WebODM. Second, we assess the geometric accuracy of the photogrammetric models acquired from the ODM pipeline by comparing them to the models acquired via microCT scanning, the de facto method to image skeleton. Our sample comprises fifteen Aplodontia rufa (mountain beaver) skulls. Using models derived from microCT scans of the samples as reference, our results show that the geometry of the models derived from ODM is sufficiently accurate for gross metric and morphometric analysis as the measurement errors are usually around or below 2%, and morphometric analysis captures consistent patterns of shape variations in both modalities. However, subtle but distinct differences between the photogrammetric and microCT-derived 3D models can affect the landmark placement, which in return affect the downstream shape analysis, especially when the variance within a sample is relatively small. At the minimum, we strongly advise not combining 3D models derived from these two modalities for geometric morphometric analysis. Our findings can be indictive of similar issues in other SFM photogrammetry tools since the underlying pipelines are similar. We recommend that users run a pilot test of geometric accuracy before using photogrammetric models for morphometric analysis. For the research community, we provide detailed guidance on using our pipeline for building 3D models from photographs.

evolutionary biology↗

bioRxiv2022

DEEP LEARNING ENABLED MULTI-ORGAN SEGMENTATION OF MOUSE EMBRYOS

The International Mouse Phenotyping Consortium (IMPC) has generated a large repository of 3D imaging data from mouse embryos, providing a rich resource for investigating phenotype/genotype interactions. While the data is freely available, the computing resources and human effort required to segment these images for analysis of individual structures can create a significant hurdle for research. In this paper, we present an open source, deep learning-enabled tool, Mouse Embryo Multi-Organ Segmentation (MEMOS), that estimates a segmentation of 50 anatomical structures with a support for manually reviewing, editing, and analyzing the estimated segmentation in a single application. MEMOS is implemented as an extension on the 3D Slicer platform and is designed to be accessible to researchers without coding experience. We validate the performance of MEMOS-generated segmentations through comparison to state-of-the-art atlas-based segmentation and quantification of previously reported anatomical abnormalities in a CBX4 knockout strain. SUMMARY STATEMENTWe present a new open source, deep learning-enabled tool, Mouse Embryo Multi-Organ Segmentation (MEMOS), to estimate the segmentation of 50 anatomical structures from microCT scans of embryonic mice.

developmental biology↗

bioRxiv2022

Examining craniofacial variation among crispant and mutant zebrafish models of human skeletal diseases

Genetic diseases affecting the skeletal system present with a wide range of symptoms that make diagnosis and treatment difficult. Genome-wide association and sequencing studies have identified genes linked to human skeletal diseases. Gene editing of zebrafish models allows researchers to further examine the link between genotype and phenotype, with the long-term goal of improving diagnosis and treatment. While current automated tools enable rapid and in-depth phenotyping of the axial skeleton, characterizing the effects of mutations on the craniofacial skeleton has been more challenging. The objective of this study was to evaluate a semi-automated screening tool can be used to quantify craniofacial variations in zebrafish models using four genes that have been associated with human skeletal diseases (meox1, plod2, sost, and wnt16) as test cases. We used traditional landmarks to ground truth our dataset and pseudolandmarks to quantify variation across the 3D cranial skeleton between the groups (somatic crispant, germline mutant, and control fish). The proposed pipeline identified variation between the crispant or mutant fish and control fish for four genes. Variation in phenotypes parallel human craniofacial symptoms for two of the four genes tested. This study demonstrates the potential as well as the limitations of our pipeline as a screening tool to examine multi-dimensional phenotypes associated with the zebrafish craniofacial skeleton.

genetics↗

bioRxiv2022

Automated Landmarking via Multiple Templates

Geometric morphometrics based on landmark data has been increasingly used in biomedical and biological research for quantifying complex phenotypes. However, manual landmarking can be laborious and subject to intra and interobserver errors. This has motivated the development of automated landmarking methods. We have recently introduced ALPACA (Automated Landmarking through Point Cloud Alignment and Correspondence), a fast method to automatically annotate landmarks via the use of a landmark template as a part of the SlicerMorph toolkit. Yet, using a single template may not consistently perform well for large study samples, especially when the sample consists of specimens with highly variable morphology, as it is common in evolutionary studies. In this study, we introduce a variation on our ALPACA pipeline that supports multiple specimen templates, which we call MALPACA. We show that MALPACA outperforms ALPACA consistently by testing on two different datasets. We also introduce a method of choosing the templates that can be used in conjunction with MALPACA, when no prior information is available. This K-means method uses an approximation of the total morphological variation in the dataset to suggest samples within the population to be used as landmark templates. While we advise investigators to pay careful attention to the template selection process in any of the template-based automated landmarking approaches, our analyses show that the introduced K-means based method of templates selection is better than randomly choosing the templates. In summary, MALPACA can accommodate larger morphological disparity commonly found in evolutionary studies with performance comparable to human observer.

evolutionary biology↗

bioRxiv2021

Computational anatomy and geometric shape analysis enables analysis of complex craniofacial phenotypes in zebrafish mutants

Due to the complexity of fish skulls, previous attempts to classify craniofacial phenotypes have relied on qualitative features or 2D landmarks. In this work we aim to identify and quantify differences in 3D craniofacial phenotypes in adult zebrafish mutants. We first estimate a synthetic normative zebrafish template using microCT scans from a sample pool of wildtype animals using the Advanced Normalization Tools (ANTs). We apply a computational anatomy (CA) approach to quantify the phenotype of zebrafish with disruptions in bmp1a, a gene implicated in later skeletal development and whose human ortholog when disrupted is associated with Osteogenesis Imperfecta. Compared to controls, the bmp1a fish have larger otoliths and exhibit shape differences concentrated around the operculum, anterior frontal, and posterior parietal bones. Moreover, bmp1a fish differ in the degree of asymmetry. Our CA approach offers a potential pipeline for high throughput screening of complex fish craniofacial phenotypes, especially those of zebrafish which are an important model system for testing genome to phenome relationships in the study of development, evolution, and human diseases. Summary statementA computational anatomy approach offers a potential pipeline for high throughput screening of complex zebrafish craniofacial phenotypes, an important model system for the study of development, evolution, and human diseases.

developmental biology↗

bioRxiv2021

Hierarchy-guided Neural Networks for Species Classification

O_LISpecies classification is an important task that is the foundation of industrial, commercial, ecological, and scientific applications involving the study of species distributions, dynamics, and evolution. C_LIO_LIWhile conventional approaches for this task use off-the-shelf machine learning (ML) methods such as existing Convolutional Neural Network (ConvNet) architectures, there is an opportunity to inform the ConvNet architecture using our knowledge of biological hierarchies among taxonomic classes. C_LIO_LIIn this work, we propose a new approach for species classification termed Hierarchy-Guided Neural Network (HGNN), which infuses hierarchical taxonomic information into the neural networks training to guide the structure and relationships among the extracted features. We perform extensive experiments on an illustrative use-case of classifying fish species to demonstrate that HGNN outperforms conventional ConvNet models in terms of classification accuracy, especially under scarce training data conditions. C_LIO_LIWe also observe that HGNN shows better resilience to adversarial occlusions, when some of the most informative patch regions of the image are intentionally blocked and their effect on classification accuracy is studied. C_LI

evolutionary biology↗

bioRxiv2020

SlicerMorph: An open and extensible platform to retrieve, visualize and analyze 3D morphology

Large scale digitization projects such as #ScanAllFishes and oVert are generating high-resolution microCT scans of vertebrates by the thousands. Data from these projects are shared with the community using aggregate 3D specimen repositories like MorphoSource through various open licenses. MorphoSource currently hosts tens of thousands of 3D scans of eukaryotes. Along with the data from similarly scoped projects such as 10kPhenomes, DigiMorph and many others, soon hundreds of thousands of specimens that represent biodiversity of extinct and extant organisms will be conveniently available to researchers. We anticipate an explosion of quantitative research in organismal biology with the convergence of available data and the methodologies to analyze them. Though the data are available, the road from a series of images to analysis is fraught with challenges for most biologists. It involves tedious tasks of data format conversions, preserving spatial scale of the data accurately, 3D visualization and segmentations, acquiring measurements and annotations. When scientists use commercial software with proprietary formats, a roadblock for data exchange, collaboration, and reproducibility is erected that hurts the efforts of the scientific community to broaden participation in research. Another relevant concern is that ultimate derivative data from individual research projects (e.g., 3D models of segmentation) are shared in formats that do not preserve the correct spatial scale of the data. In this paper, we present our effort to tackle challenges biologists face when conducting 3D specimen-based research. We developed SlicerMorph as an extension of 3D Slicer, a biomedical visualization and analysis ecosystem with extensive visualization and segmentation capabilities built on proven python-scriptable open-source libraries such as Visualization Toolkit and Insight Toolkit. In addition to the core functionalities of Slicer, SlicerMorph provides users with modules to conveniently retrieve open-access 3D models or import users own 3D volumes, to annotate 3D curve and patch-based landmarks, generate canonical templates, conduct geometric morphometric analyses of 3D organismal form using both landmark-driven and landmark-free approaches, and create 3D animations from their results. We highlight how these individual modules can be tied together to establish complete workflow(s) from image sequence to morphospace. Our software development efforts were supplemented with short courses and workshops that cover the fundamentals of 3D imaging and morphometric analyses as it applies to study of organismal form and shape in evolutionary biology, and extensive links to the existing tutorials are provided as supplemental material. Our goal is to establish a community of organismal biologists centered around Slicer and SlicerMorph to facilitate easy exchange of data and results and collaborations using 3D specimens. Our proposition to our colleagues is that using a common open platform supported by a large user and developer community ensures the longevity and sustainability of the tools beyond the initial development effort.

evolutionary biology↗

bioRxiv2020

ALPACA: a fast and accurate approach for automated landmarking of three-dimensional biological structures

Landmark-based geometric morphometrics has emerged as an essential discipline for the quantitative analysis of size and shape in ecology and evolution. With the ever-increasing density of digitized landmarks, the possible development of a fully automated method of landmark placement has attracted considerable attention. Despite the recent progress in image registration techniques, which could provide a pathway to automation, three-dimensional morphometric data is still mainly gathered by trained experts. For the most part, the large infrastructure requirements necessary to perform image-based registration, together with its system-specificity and its overall speed have prevented wide dissemination. Here, we propose and implement a general and lightweight point cloud-based approach to automatically collect highdimensional landmark data in 3D surfaces (Automated Landmarking through Point cloud Alignment and Correspondence Analysis). Our framework possesses several advantages compared with image-based approaches. First, it presents comparable landmarking accuracy, despite relying on a single, random reference specimen and much sparser sampling of the structures surface. Second, it is performant such that it can be efficiently run on consumer-grade personal computers. Finally, it is general and can be applied to any biological structure of interest, regardless of whether anatomical atlases are available. Our validation procedures indicate that the method is capable of recovering multivariate patterns of morphological variation that are largely indistinguishable from those obtained by manual digitization, indicating that the use of an automated landmarking approach should not result in different conclusions regarding the nature of multivariate patterns of morphological variation. The proposed point cloud-based approach has the potential to increase the scale and reproducibility of morphometrics research. To allow ALPACA to be used out-of-the-box by users with no prior programming experience, we implemented it as a module as part of the SlicerMorph project. SlicerMorph is an extension that enables geometric morphometrics data collection and 3D specimen analysis within the open-source 3D Slicer biomedical visualization ecosystem. We expect that convenient access to this platform will make ALPACA broadly applicable within ecology and evolution.

evolutionary biology↗

bioRxiv2019

Cranial Bone Growth in Isolated Sagittal Craniosynostosis Compared to Normal Growth in the First Six Months of Age

BackgroundSagittal craniosynostosis (SCS), the most common type of premature perinatal cranial suture fusion, results in abnormal head shape that requires extensive surgery to correct. It is important to find objective and repeatable measures of severity and surgical outcome to examine the effect of timing and technique on different SCS surgeries. The purpose of this study was to develop statistical models of infant (0-6 months old) skull growth in both normative and SCS subjects (prior to surgery). Our goal was to apply these models to the assessment of differences between these two groups in overall post-natal growth patterns and sutural growth rates as a first step to develop methods for predictive models of surgical outcome. Methods and FindingsWe identified 81 patients with isolated, non-syndromic SCS from Seattle Childrens Craniofacial Center patient database who had a pre-operative CT exam before the age of six months. As a control group, we identified 117 CT exams without any craniofacial abnormalities or bone fractures in the same age group. We first created population-level templates from the CT images of the SCS and normal groups. All CT images from both groups, as well as the canonical templates of both cohorts were annotated with anatomical landmarks, which were used in a growth model that predicted the locations of these landmarks at a given age based on each population. Using the template images and the landmark positions predicted by the growth models, we created 3D meshes for each week of age up to six months for both populations. To analyze the growth patterns at the suture sites, we annotated both templates with additional semi-landmarks equally spaced along the metopic, coronal, sagittal and lambdoidal cranial sutures. By transferring these semi-landmarks to meshes produced from the growth model, we measured the displacement of the bone borders and suture closure rates. We found that the growth at the metopic and coronal sutures were more rapid in the SCS cohort compared to the normal cohort. The antero-posterior displacement of the semi-landmarks indicated a more rapid growth in the sagittal plane in the SCS model compared to the normal model as well. ConclusionsStatistical templates and geometric morphometrics are promising tools for understanding the growth patterns in normal and synostotic populations and to produce objective and reproducible measurements of severity and outcome. Our study is the first of its kind to quantify the bone growth for the first six months of life in both normal and sagittal synostosis patients.

physiology↗