LAMA: Automated image analysis for developmental phenotyping of mouse embryos
| Autor: | Ramón Casero, Sara Wells, Neil R. Horner, Sara Johnson, Henrik Westerberg, Steve D.M. Brown, Lydia Teboul, James M. Brown, Ann-Marie Mallon, Shanmugasundaram Venkataraman |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: |
0303 health sciences
education.field_of_study Computer science business.industry Population ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION Image registration Imaging modalities 03 medical and health sciences 0302 clinical medicine Post coitus Computer vision Artificial intelligence business education 030217 neurology & neurosurgery 030304 developmental biology |
| DOI: | 10.1101/2020.05.04.075853 |
| Popis: | Advanced 3D imaging modalities such as micro computed tomography (micro-CT), high resolution episcopic microscopy (HREM), and optical projection tomography (OPT) have been readily incorporated into high-throughput phenotyping pipelines, such as the International Mouse Phenotyping Consortium (IMPC). Such modalities generate large volumes of raw data that cannot be immediately harnessed without significant resources of manpower and expertise. Thus, rapid automated analysis and annotation is critical to ensure that 3D imaging data is able to be integrated with other multi-dimensional phenotyping data. To this end, we present an automated computational mouse phenotyping pipeline called LAMA, based on image registration, which requires minimal technical expertise and human input to use. Designed predominantly for developmental biologists, our software performs image pre-processing, registration, statistical and gene function annotation, and segmentation of 3D micro-CT data. We address several limitations of current methods and create an easy to use, fast solution application for mouse embryo phenotyping. We also present a highly granular, novel anatomical E14.5 (14.5 days post coitus) atlas of a population average that integrates with our pipeline to allow a range of dysmorphologies to be automatically annotated as well as results from the validation of the pipeline. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|