Last modified by puchades on 2021/12/07 10:37
From version 19.2
edited by annedevismes
on 2021/06/08 10:40
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To version 4.2
edited by puchades
on 2020/03/24 09:56
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1 -2. Image pre-processing with Nutil Transform
1 +2. Image preparation with Nutil
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1 -XWiki.annedevismes
1 +XWiki.puchades
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1 -**The //Transform// feature in the //Nutil// software **enables **image rotation, renaming, resising, and mirroring** and is used to prepare image series for atlas alignment with //QuickNII //and segmentation with //ilastik//. It should be noted that //QuickNII //and //ilastik //have different input-image size requirements. In addition, it is important that the input images follow the standard naming convention described below.
1 +== Image pre-processing with Nutil Transform ==
2 2  
3 ->(% 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.
4 4  
5 -== (% style="color:#c0392b" %)Input requirements(%%) ==
4 +**The //Transform// feature in the //Nutil// software **enables **image rotation, renaming, resizing and mirroring** and was used to prepare the image series for //QuickNII //alignment and //ilastik //segmentation.
6 6  
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.
6 +>The input size requirements for the //QuickNII// and //ilastik// software differ. Naming convention of files is important.
8 8  
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 resising 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 by a factor of 20 for Alzheimer's plaques with respect to image width.
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.
10 10  
11 -== (% style="color:#c0392b" %)Naming convention(%%) ==
12 +For //ilastik// (Borg et al.2019), the resizing was performed in order to enable efficient processing. To clarify, the pixel classification algorithm relies on input from manual user annotations of training images, and the features ‒ intensity, edge and/or texture ‒ of the image pixels.  In practice, a test run was performed with //ilastik// on images of different sizes to find the optimal resolution for segmentation, with a final resize factor of e.g 0.1  for cellular features, and a factor of 0.05 for series with larger features like Alzheimer plaques.
12 12  
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).
14 +==== **Naming convention:** ====
14 14  
15 -Example: tg2345_MMSH_s001_segmentation.png
16 +For //QuickNII, //the// //serial section images should be assigned consecutive serial numbers, preferably indicated by three-digit numbers at the end of the file name, e.g. Sample_ID_s001.tif. Section sampling is given by the serial numbers.
16 16  
17 -It is fine to include a string of letters and numbers followed by the unique ID.
18 +For running the QUINT analysis the Segmentation files and the customized atlas maps need to match and therefore
18 18  
20 +=== H3 Headings Will Appear In The Table of Content ===
19 19  
20 -== (% style="color:#c0392b" %)Running the transformation(%%) ==
22 +==== You can also add images ====
21 21  
22 -Consult the //Nutil //user manual for the detailed procedure (included in the //Nutil //package [[available for download here>>https://www.nitrc.org/projects/nutil/]]).
24 +[[image:Collaboratory.Apps.Article.Code.ArticleSheet@placeholder.jpg]]
23 23  
24 -[[image:Figure_2_v3.jpg]]
26 +Photo by David Clode
27 +
28 +==== Or code ====
29 +
30 +Code blocks can be added by using the code macro:
31 +
32 +{{code language="python"}}
33 +x = 1
34 +if x == 1:
35 + # indented four spaces
36 + print("x is 1.")
37 +{{/code}}
38 +
39 +(% class="wikigeneratedid" id="HH4Won27tAppearinToC" %)
40 +
Figure_2_v3.jpg
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1 -XWiki.puchades
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1 -4.4 MB
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