Commit 7c30eeff authored by Jean-Yves TINEVEZ's avatar Jean-Yves TINEVEZ
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Fix typos, update descriptions.

parent 2a7b0d25
......@@ -181,11 +181,11 @@ dpr =
```
In the following we suppose that `dpr` is an instance of `deproj` created by this example.
____
#### Static method `from_bellaiche`
It is possible to create a `deproj` object from completely different inputs. In this case, we take the segmentation results generated by the MATLAB analysis sofware created by the [lab of Yohanns Bellaiche](https://science.institut-curie.org/research/biology-cancer-genetics-and-epigenetics/developmental-biology-and-genetics/team-bellaiche/) and a mesh generated elsewhere.
It is possible to create a `deproj` object from completely different inputs. In this case, we take the segmentation results generated by the MATLAB analysis sofware created by the [lab of Yohanns Bellaïche](https://science.institut-curie.org/research/biology-cancer-genetics-and-epigenetics/developmental-biology-and-genetics/team-bellaiche/) and a mesh generated elsewhere.
##### The segmentation from the software of Y.B. lab.
......@@ -196,7 +196,7 @@ See the following paper for its reference use:
> Boris Guirao, Stéphane U Rigaud, Floris Bosveld, Anaïs Bailles, Jesús López-Gay, Shuji Ishihara, Kaoru Sugimura, François Graner, Yohanns Bellaïche.
> eLife, vol. 4 (**2015**)
The first step of this tool generates segmentation results, stored in 4 variables: `CELLS`, `FRAME`, `VERTICES` and `SIDES` as `struct` MATLAB objects. You will find in `samples/AdultZebrafishBrainSeg.mat ` folder of this repo, a *minimal subset* of such a segmentation result. We give below a tentative description of how the results are organized and what each variable should contain. We only document the parts needed by DeProj, and this documentation is limited by and based solely on our understanding of the format.
This tool goes beyond the segmentation of the tissue cells. Its complete execution yields the local deformation of the tissue. However DeProj only works on its first step. The first step of this tool generates segmentation results, stored in 4 variables: `CELLS`, `FRAME`, `VERTICES` and `SIDES` as `struct` MATLAB objects. You will find in `samples/AdultZebrafishBrainSeg.mat ` folder of this repo, a *minimal subset* of such a segmentation result. We give below a tentative description of how the results are organized and what each variable should contain. We only document the parts needed by DeProj, and this documentation is limited by and based solely on our understanding of the format.
Each of the four variables listed above is organized as follow:
......@@ -204,16 +204,16 @@ Each of the four variables listed above is organized as follow:
- the variable `numbers`, a `Ncells x 1` array that specifies the ID of the cell (we will import in the `id` field of the corresponding `epicell` )
- the variable `vertices`, a `Ncells x 1` cell array. In each cell array is listed the indices of the junctions that a cell touches.
- the variable `contour_indices`, a `Ncells x 1` cell array. Each cell array contains the linear indices of the pixels of the cell contour. You can transform them into X, Y coordinates by knowing the width of the source image.
- `FRAME` is a `struct` that contains information about the image that was used to generate the segmentation results. DeProj uses:
- `imageSize`, a 2 elements array specifying the width and height of the source image.
- `scale1D` the pixel size in physical units.
- `SIDES` is a `struct` that contains information about the cell-to-cell borders. We only use the:
- `vertices` variable, which is a `Nedges x 2` array containing the indices of the junctions joinsed by such a border.
- `VERTICES`, a struct array containing the information about the cell junctions.
- the variable `numbers`, a `Njunctions x 1` array that specifies the ID of the junction.
- `XYs` a `Njunctions x 2` array containing the X and Y location of each junction, in physical coordinates.
- `FRAME` is a `struct` that contains information about the image that was used to generate the segmentation results. DeProj uses:
- `imageSize`, a 2 elements array specifying the width and height of the source image.
- `scale1D` the pixel size in physical units.
If at least all these variables are present, DeProj can import such a segmentation with the method `from_bellaiches`.
If at least all these variables are present, DeProj can import such a segmentation with the method `from_bellaiche`.
##### The mesh mapping the tissue surface.
......@@ -239,6 +239,10 @@ dpr = deproj.from_bellaiche( ...
This will generate a `deproj` object with the same capabilities as for the other import. See the `RunExample2.m` script for an example of such an import.
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In the following we suppose that `dpr` or `obj` is an instance of `deproj` created by one of the two static methods described above.
#### `to_table`
`T = to_table( obj )`
......@@ -317,7 +321,7 @@ dpr.to_file( 'table.xlsx' )
#### `figure_cell_sizes`
`[ hf, ax1, ax2 ] = plot_sizes( obj, scale_bar_length )`
`[ hf, ax1, ax2 ] = figure_cell_sizes( obj, scale_bar_length )`
Generate a figure with the cells area and perimeter. All the plots generated by the `plot_*` methods are 3D plots. The cells are drawn with their 3D coordinates and you can zoom, rotate, pan to make the curvature of the tissue appear.
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