Figure 7.

Evaluation of the time needed to create annotations, train, and run segmentation models. (a) Preparation time measures hands on, expert time annotating images or creating CellProfiler pipelines. Manually annotating 100 training images with about 11,500 nuclei requires significantly longer times. (b) Machine learning models need to be trained while CellProfiler pipelines do not need additional processing. Neural network training was run on a single NVIDIA Titan X GPU. DeepCell trains an ensemble of five models, which was used in all evaluations. (c) CellProfiler pipelines and Random Forests are run on new images using CPU cores to measure the computational cost of segmenting a single image. Deep learning needs significantly more resources to accomplish the task, but can be accelerated using GPUs, which have thousands of computing cores that allow algorithms to run operations in parallel. This reduces significantly the elapsed time, making it practical and even faster than classical solutions. [Color figure can be viewed at wileyonlinelibrary.com]