Scanner Independent Deep Learning-Based Segmentation Framework Applied to Mouse Embryos
| Autor: | Jonathan Mamou, Jeffrey A. Ketterling, Daniel H. Turnbull, Orlando Aristizabal, Hannah Goldman, Yao Wang, Tongda Xu, Ziming Qiu |
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| Rok vydání: | 2020 |
| Předmět: |
0301 basic medicine
Scanner 030219 obstetrics & reproductive medicine business.industry Computer science Deep learning ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION 030105 genetics & heredity 03 medical and health sciences 0302 clinical medicine Computer vision Segmentation Artificial intelligence business Brain Ventricle |
| Zdroj: | 2020 IEEE International Ultrasonics Symposium (IUS). |
| Popis: | We have applied a deep learning framework, trained on mouse embryo images acquired with a 40 MHz annular array, to volumetric data acquired with a VisualSonics Vevo 3100 commercial scanner using a 40-MHz linear array. The deep learning framework was robust enough to accurately segment out the body and the brain ventricle from the 3D data generated by the commercial scanner. These results show that there is no need to retrain the algorithm with hundreds of new manually segmented datasets. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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