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5	5	== (% style="color:#c0392b" %)Input requirements(%%) ==
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7		-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.
	7	+For //QuickNII,// the input requirements are described in details in Puchades et al., 2019. To summarise, images should be in 24-bit PNG or JPEG format, with a resolution up to 16 megapixels (e.g. 4000x4000 or 5000x3000 pixels). Keep in mind that QuickNII does not benefit from image resolutions exceeding the resolution of the monitor in use. For a standard FullHD or widescreen display (1920x1080 or 1920x1200 pixels) the useful image area in QuickNII is approximately 1500x1000 pixels. Using a similar resolution ensures optimal image-loading performance.
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9		-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).
	9	+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 10 for cellular features, and a factor of 20 for Alzheimer's plaques with respect to image width.
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11		-=== (% style="color:#c0392b" %)Naming convention(%%) ===
	11	+== (% style="color:#c0392b" %)Naming convention(%%) ==
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13	13	For QUINT analysis, Nutil Quantifier quantifies objects extracted from segmentations and registers them to regions defined by customised atlas maps. To match segmentations and corresponding atlas maps together, Nutil Quantifier relies on a unique ID in the file name of both files.The ID should be unique to the particular brain section and in the format: sXXX.., with XXX.. representing the section number. The section number should reflect the serial order and spacing of the sections (e.g. s002, s006, s010 for every 4^^th^^ section starting with section 2).
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17	17	(It is fine to include a string of letters and numbers followed by the unique ID).
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20		-=== (% style="color:#c0392b" %)Running the transformation(%%) ===
	20	+== (% style="color:#c0392b" %)Running the transformation(%%) ==
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22	22	Consult the Nutil user manual for detailed procedure (lincluded in the Nutil package available for download at Nitrc.org)
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