Abstract
We train a convolutional neural network (CNN) to classify the sex of mice from x-ray images in order to develop a tool that can be used not only for quality control of high throughput image data, but also to identify sex-related phenotype alterations. The method achieved 98% accuracy, recall of .98 and precision of .98 and identified the chest and pelvis as the areas relevant for sex classification. We identified four knockout lines (Duoxa2, Tmem189, Dusp3 and Il10rb) potentially affected by phenotype changes related to sex.
This study demonstrates that CNNs can be trained for the purpose of quality control of images and can aid the discovery of novel genotype-phenotype associations. In addition to facilitating quality control, the method presented (1) allows the creation of a tool that will help phenotypers flag images of mice that should be inspected in more detail, (2) has highlighted areas of the mouse that are of particular interest in phenotype changes related to sex, and (3) has the potential to identify genes that may be causing sex related phenotype changes and/or are involved in sexual dimorphism.
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Acknowledgements
Our thanks to Professor Jesús Ruberte of UAB-Barcelona, and Dr David Lafont of the Wellcome Trust Sanger Institute for insightful discussions about the physiological relevance of the CAMs. This work was supported by the United States National Institutes of Health (NIH) Grant U54 HG006370.
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Babalola, K., Mashhadi, H.H., Muñoz-Fuentes, V., Mason, J., Meehan, T., On Behalf of the International Mouse Phenotyping Consortium. (2020). Investigating Sex Related Phenotype Changes in Knockout Mice by Applying Deep Learning to X-Ray Images. In: Papież, B., Namburete, A., Yaqub, M., Noble, J. (eds) Medical Image Understanding and Analysis. MIUA 2020. Communications in Computer and Information Science, vol 1248. Springer, Cham. https://doi.org/10.1007/978-3-030-52791-4_28
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DOI: https://doi.org/10.1007/978-3-030-52791-4_28
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