PT  - JOURNAL ARTICLE
AU  - Edouard A Hay
AU  - Raghuveer Parthasarathy
TI  - Performance of convolutional neural networks for identification of bacteria in 3D microscopy datasets
AID  - 10.1101/273318
DP  - 2018 Jan 01
TA  - bioRxiv
PG  - 273318
4099  - http://biorxiv.org/content/early/2018/02/28/273318.short
4100  - http://biorxiv.org/content/early/2018/02/28/273318.full
AB  - Three-dimensional microscopy is increasingly prevalent in biology due to the development of techniques such as multiphoton, spinning disk confocal, and light sheet fluorescence microscopies. These methods enable unprecedented studies of life at the microscale, but bring with them larger and more complex datasets. New image processing techniques are therefore called for to analyze the resulting images in an accurate and efficient manner. Convolutional neural networks are becoming the standard for classification of objects within images due to their accuracy and generalizability compared to traditional techniques. Their application to data derived from 3D imaging, however, is relatively new and has mostly been in areas of magnetic resonance imaging and computer tomography. It remains unclear, for images of discrete cells in variable backgrounds as are commonly encountered in fluorescence microscopy, whether convolutional neural networks provide sufficient performance to warrant their adoption, especially given the challenges of human comprehension of their classification criteria and their requirements of large training datasets. We therefore applied a 3D convolutional neural network to distinguish bacteria and non-bacterial objects in 3D light sheet fluorescence microscopy images of larval zebrafish intestines. We find that the neural network is as accurate as human experts, outperforms random forest and support vector machine classifiers, and generalizes well to a different bacterial species. We also discuss network design considerations, and describe the dependence of accuracy on dataset size and data augmentation. We provide source code and descriptions of our analysis pipeline to facilitate adoption of convolutional neural network analysis for three-dimensional microscopy data.