This is an open access article under the terms of the Creat ive Commo ns Attri bution-NonCo mmercial License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited and is not used for commercial purposes.

This is an open access article under the terms of the Creat ive Commo ns Attri bution-NonCo mmercial License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited and is not used for commercial purposes.

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.

Hmx1 encodes a homeodomain transcription factor expressed in the developing lateral craniofacial mesenchyme, retina and sensory ganglia. Mutation or mis-regulation of Hmx1 underlies malformations of the eye and external ear in multiple species. Deletion or insertional duplication of an evolutionarily conserved region (ECR) downstream of Hmx1 has recently been described in rat and cow, respectively. Here, we demonstrate that the impact of Hmx1 loss is greater than previously appreciated, with a variety of lateral cranioskeletal defects, auriculofacial nerve deficits, and duplication of the caudal region of the external ear. Using a transgenic approach, we demonstrate that a 594 bp sequence encompassing the ECR recapitulates specific aspects of the endogenous Hmx1 lateral facial expression pattern. Moreover, we show that Hoxa2, Meis and Pbx proteins act cooperatively on the ECR, via a core 32 bp sequence, to regulate Hmx1 expression. These studies highlight the conserved role for Hmx1 in BA2-derived tissues and provide an entry point for improved understanding of the causes of the frequent lateral facial birth defects in humans.

Objectives Increased use of three‐dimensional (3D) imaging data has led to a need for methods capable of capturing rich shape descriptions. Semi‐landmarks have been demonstrated to increase shape information but placement in 3D can be time consuming, computationally expensive, or may introduce artifacts. This study implements and compares three strategies to more densely sample a 3D image surface. Materials and methods Three dense sampling strategies: patch, patch‐thin‐plate spline (TPS), and pseudo‐landmark sampling, are implemented to analyze skulls from three species of great apes. To evaluate the shape information added by each strategy, the semi or pseudo‐landmarks are used to estimate a transform between an individual and the population average template. The average mean root squared error between the transformed mesh and the template is used to quantify the success of the transform. Results The landmark sets generated by each method result in estimates of the template that on average were comparable or exceeded the accuracy of using manual landmarks alone. The patch method demonstrates the most sensitivity to noise and missing data, resulting in outliers with large deviations in the mean shape estimates. Patch‐TPS and pseudo‐landmarking provide more robust performance in the presence of noise and variability in the dataset. Conclusions Each landmarking strategy was capable of producing shape estimations of the population average templates that were generally comparable to manual landmarks alone while greatly increasing the density of the shape information. This study highlights the potential trade‐offs between correspondence of the semi‐landmark points, consistent point spacing, sample coverage, repeatability, and computational time.

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 structure’s 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.

Human facial asymmetry is due to a complex interaction of genetic and environmental factors. To identify genetic influences on facial asymmetry, we developed a method for automated scoring that summarizes local morphology features and their spatial distribution. A genome-wide association study using asymmetry scores from two local symmetry features was conducted and significant genetic associations were identified for one asymmetry feature, including genes thought to play a role in craniofacial disorders and development: NFATC1, SOX5, NBAS, and TCF7L1. These results provide evidence that normal variation in facial asymmetry may be impacted by common genetic variants and further motivate the development of automated summaries of complex phenotypes.

This paper introduces a new method to quantify and characterize shape changes during early facial development without the use of landmarks. Landmarks are traditionally used in morphometric analysis, but very few can be identified reliably across all stages of embryonic development. This method uses deformable registration to produce a dense vector field describing the point correspondences between two images. Low and mid-level features are extracted from the deformable vector field to find regions of organized differences that are biologically relevant. These methods are shown to detect regions of difference when evaluated on chick embryo images warped with small magnitude deformations in regions critical to midfacial development.