LAMA

LAMA automates the analysis and annotation of morphological abnormalities in mouse embryos using 3D volumetric imaging data to enable high-throughput developmental phenotyping.

Key Features:

• Automated image analysis: Identifies embryo dysmorphology from 3D volumetric imaging modalities including micro computed tomography (micro-CT), high-resolution episcopic microscopy (HREM), and optical projection tomography (OPT).
• Image registration-based pipeline: Aligns 3D imaging data with a reference atlas via image registration to ensure accurate localization and comparison of anatomical structures.
• Pre-processing and segmentation: Performs essential pre-processing steps and segmentation on 3D micro-CT data to enable detailed morphological assessments.
• Statistical analysis and gene function annotation: Includes statistical analysis workflows and gene function annotation to relate observed phenotypes to genetic perturbations.
• Integration with anatomical atlases: Utilizes a population-average anatomical atlas at embryonic day 14.5 (E14.5) for precise, granular annotation of dysmorphologies.
• Integration with high-throughput phenotyping pipelines: Designed to integrate with large-scale pipelines such as the International Mouse Phenotyping Consortium (IMPC) for high-throughput embryo phenotyping.

Scientific Applications:

• High-throughput embryonic phenotyping: Enables automated analysis of large volumes of 3D imaging data for large-scale phenotyping projects.
• Developmental biology investigations: Supports detection and annotation of morphological abnormalities at E14.5 to study embryogenesis and organogenesis.
• Genetic studies and gene function analysis: Facilitates statistical linkage of morphological phenotypes to genetic perturbations and gene function annotation.
• Drug screening: Allows assessment of morphological effects of chemical perturbations on embryo development using 3D imaging data.
• Multi-dimensional phenotypic data integration: Supports integration of morphological data with other phenotypic datasets for comprehensive phenotype characterization.

Methodology:

Computational steps explicitly include image registration to a population-average E14.5 atlas, pre-processing of 3D micro-CT data, segmentation, and statistical analysis with gene function annotation.