To identify the optimal parameters, we performed training and testing over a range of variable parameters including epochs, steps per epoch and learning rate. Performance comparisons were conducted by tabulating the F1-Scores on the resultant test data, evaluated at an IoU of 0.7, using the method set down by Caicedo et al. [21]. Optimization was conducted for the colon tissue dataset and also for the cell line A dataset (discrete nuclear texture). First, a model for each network was trained using 5, 10, 20, 50, 100, 200, 400, 600, 800 and 1000 epochs, then each epoch’s model was tested for comparison. StarDist and Mask R-CNN networks were then trained using the optimal number of epochs with 20, 50, 100, 200, 300, 400 and 500 steps, and again tested. ANCIS was not evaluated for number of steps as the code did not provide the option to change steps, however ANCIS did provide separate training functions for both the region proposal and segmentation networks, and both were optimized for epochs. Lastly, each network was trained using learning rates of 1e−3, 1e−4 and 1e−5.

In order to determine the minimum number of required images in the dataset to achieve acceptable results, we varied the size of each dataset used for training. Using the tissue dataset, the training set size was varied from 10, 20, 40, 60 and 77 images; for the cell line dataset, we used 10, 20, 40 and 65 images. Models were retrained for each training set size, and accuracy testing was conducted for each network.

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