Using the optimal settings determined in the previous section, we conducted a more thorough testing and evaluation of each network’s performance on our STORM images. Test accuracy was analyzed using the F1-Score, Hausdorff distance and the false negative percentage, again using an IoU threshold of 0.7. The Hausdorff distance was calculated using the scikit-image version 0.17.2 python library. Both the Hausdorff distance and the F1-Score were averaged across all instances that achieved the requisite IoU threshold in each test dataset. The false negative percentage was determined as the total number of ground truth instances that did not achieve the requisite IoU score in the predictions of each test set divided by the total number of ground truth instances in that set. Similarly, the false positive percentage was calculated as the total number of predicted instances without a corresponding ground truth instance, divided by the total number of ground truth instances in the image.

The optimally trained colon tissue network models were used to evaluate both the colon tissue and prostate tissue test sets, as well as the 512 × 512 and 256 × 256 cell line test data. Additionally, the trained network models for cell line training sets A & B were both used to evaluate the cell line test set images. Only the network models for cell line training set B (containing both discrete and dense nuclear texture) was applied to the 512 × 512 and 1024 × 1024 colon tissue test sets. Both the colon tissue and cell line network models were used to create two new datasets (each) by either blurring or performing a histogram equalization on the images. Networks were trained on the blurred or equalized images using the same optimal parameters as the original image datasets were trained on, and then tested on their corresponding blurred or equalized test sets.

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