Training neural networks directly on large format, super-resolution images can be a slow and memory intensive process. To offset memory limitations of our system, we downsized our images prior to training. Segmentation results from a trained network were later upsized to match the original image. We compared the accuracy of our method to a tiling method, where each image in the training and testing datasets was divided into a sequence of smaller images. These images, which would retain the detail of the original super-resolution images, were then used to train the networks along with their corresponding labels (Fig. 11).

Example of dividing large image into square tiles. A large 5120 × 5120 super resolution image is divided into many smaller image patches, or tiles. The image label, identifying fluorescent nuclei, is similarly divided. A tile overlap is used for test images, to be segmented by a trained network, in order to help recombine overlapping predictions and remove tile borders

When conducting segmentation on test images, the original images (5120 × 5120) were divided into (512 × 512) squares with each square overlapping its neighbors by a 128-pixel margin. The purpose of the overlap is to help merge predictions when reuniting the segmented image squares. This is accomplished by sampling and matching the labels of overlapping segments, eliminating false segment divisions created by tile image borders (Additional file 10: Figure S9). Resulting whole test image segmentations were analyzed for accuracy.

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