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ABSTRACT The International Mouse Phenotyping Consortium (IMPC) has generated a large repository of three-dimensional (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. This article has an associated First Person interview with the first author of the paper. 

Manually collecting landmarks for quantifying complex morphological phenotypes can be laborious and subject to intra and interobserver errors. However, most automated landmarking methods for efficiency and consistency fall short of landmarking highly variable samples due to the bias introduced by the use of a single template. We introduce a fast and open source automated landmarking pipeline (MALPACA) that utilizes multiple templates for accommodating large-scale variations. We also introduce a K-means method of choosing the templates that can be used in conjunction with MALPACA, when no prior information for selecting templates is available. Our results confirm that MALPACA significantly outperforms single-template methods in landmarking both single and multi-species samples. K-means based template selection can also avoid choosing the worst set of templates when compared to random template selection. We further offer an example of post-hoc quality check for each individual template for further refinement. In summary, MALPACA is an efficient and reproducible method that can accommodate large morphological variability, such as those commonly found in evolutionary studies. To support the research community, we have developed open-source and user-friendly software tools for performing K-means multi-templates selection and MALPACA. 

ABSTRACT Due to the complexity of fish skulls, previous attempts to classify craniofacial phenotypes have relied on qualitative features or sparce 2D landmarks. In this work we aim to identify previously unknown 3D craniofacial phenotypes with a semiautomated pipeline in adult zebrafish mutants. We first estimate a synthetic ‘normative’ zebrafish template using MicroCT scans from a sample pool of wild-type 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, larger normalized centroid sizes, 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 shape to discover novel phenotypes for which traditional landmarks are too sparce to detect. The current pipeline successfully identifies areas of variation in zebrafish mutants, which are an important model system for testing genome to phenome relationships in the study of development, evolution, and human diseases. This article has an associated First Person interview with the first author of the paper. 

Abstract ObjectivesIncreased 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 methodsThree 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. ResultsThe 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. ConclusionsEach 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. 

Abstract 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, andwnt16) 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.