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6	6	>(% style="color:#27ae60" %)The //QuickNII// and //ilastik// software have different input image size requirements. It is also important that the input images follow the prescribed naming convention.
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8		-For //QuickNII,// the input requirements described in Puchades et al., 2019 are:
9		-24-bit PNG and JPEG. Images can be loaded up to the resolution of 16 megapixels (e.g.4000x4000 or 5000x3000 pixels), however QuickNII does not benefit from image resolutions exceeding the resolution of the monitor in use. For a standard FullHD or WUXGA display
10		-(1920x1080 or 1920x1200 pixels) the useful image area is approximately 1500x1000 pixels,using a similar resolution ensures optimal image-loading performance.
	8	+For //QuickNII,// the input requirements are described in in Puchades et al., 2019. To summarise, images should be in 24-bit PNG or JPEG format, and can be loaded up to a resolution of 16 megapixels (e.g.4000x4000 or 5000x3000 pixels). However //QuickNII// does not benefit from image resolutions exceeding the resolution of the monitor in use. For a standard FullHD or WUXGA display (1920x1080 or 1920x1200 pixels) the useful image area is approximately 1500x1000 pixels. Using a similar resolution ensures optimal image-loading performance.
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12		-For //ilastik// (Borg et al.2019) the histological images are downscaled in order to enable efficient processing. The pixel classification algorithm relies on input from manual user annotations of the training images, and the features ‒ intensity, edge and/or texture ‒ of the image pixels. The  resizing factor is determined by trial and error, with a test run performed with //ilastik// on images of different sizes to determine the optimal resolution for segmentation. As an example, in Yates et al, 2019, the image were downscaled by a factor of 0.1 and 0.05 for cellular features and Alzheimer's plaques respectively (factor applies to image width).
	10	+For //ilastik// (Borg et al.2019) images are downscaled in order to enable efficient processing. The pixel classification algorithm relies on input from manual user annotations of the training images, and the features ‒ intensity, edge and/or texture ‒ of the image pixels. The resizing factor is determined by trial and error, with a test run performed with //ilastik// on images of different sizes to determine the optimal resolution for segmentation. As an example, in Yates et al., 2019, the images were downscaled by a factor of 0.1 and 0.05 for cellular features and Alzheimer's plaques respectively (factor applies to the image width here).
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14	14	=== (% style="color:#c0392b" %)Naming convention(%%) ===
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