由Xarray中的一系列坐标定义的多边形3D蒙版

Question

Background:

I am using Xarray to analyze medical imaging and region defined contours. My xarray is a 3D array of pixel values with coordinates defined by the patient coordinate system (x, y, z) in mm.

I have a series of coordinates (in x,y,z) which define the vertices of a complex polygon. It is a list of tuples, where each list defines all the vertices in the z-plane. eg

[[(x1, y1, z1),..(xN, yN, z1)], [(x1, y1, z2),..(xM, yM, z2)], ...]

I made a figure, but my hopsital has blocked access to every image sharing website I tried. Sorry.

Help Request:

I'd like to create a mask of the region defined by these polygon-coordinates. I'm having a hard time figuring out how to do this. Guidance would be much appreciated!!

Here is some toy code to illustrate:

pixel_data = np.ones((5,5,5))
x_coords = np.arange(2.4, 7.4, 1)
y_coords = np.arange(-3.6, 1.4, 1)
z_coords = np.arange(202.7, 207.7, 1)[::-1]
coords = {'x': x_coords, 'y': y_coords, 'z': z_coords}

example = xr.DataArray(pixel_data, dims=('y','x','z'), coords=coords)

Array:

<xarray.DataArray (y: 5, x: 5, z: 5)>
    array([[[1., 1., 1., 1., 1.],
            [1., 1., 1., 1., 1.],
            [1., 1., 1., 1., 1.],
            [1., 1., 1., 1., 1.],
            [1., 1., 1., 1., 1.]],
                  ...

Coordinates:
* x        (x) float64 2.4 3.4 4.4 5.4 6.4
* y        (y) float64 -3.6 -2.6 -1.6 -0.6 0.4
* z        (z) float64 206.7 205.7 204.7 203.7 202.7

contours = [[(2.4, -3.6, 203.7),(3.4, 0.4, 203.7),(6.4, -1.6, 203.7)],
            [(2.4, -2.6, 204.7),(4.4, 0.4, 204.7),(6.4, -3.6, 204.7)]]

The x, y, and z coordinates in each tuple will always exactly match an x, y, z-coordinate in my data array.

Answer 1

Ok, I think I figured it out. Not sure if it's the best way. Suggestions welcome.

Need to construct a polygon defined by the vertex coordinates, which have to be converted to index coordinates in the x,y dimensions. Once the polygon is constructed it's relatively simple to assign mask values. See below:

from PIL import Image, ImageDraw
import xarray as xr
import numpy as np

pixel_data = np.ones((5,5,5))
x_coords = np.arange(2.4, 7.4, 1)
y_coords = np.arange(-3.6, 1.4, 1)
z_coords = np.arange(202.7, 207.7, 1)[::-1]
coords = {'x': x_coords, 'y': y_coords, 'z': z_coords}

example = xr.DataArray(pixel_data, dims=('y','x','z'), coords=coords)

contours = [[(2.4, -3.6, 203.7),(3.4, 0.4, 203.7),(6.4, -1.6, 203.7)],
            [(2.4, -2.6, 204.7),(4.4, 0.4, 204.7),(6.4, -3.6, 204.7)]]

def create_mask(array, contour_list):
    'Takes in an Array we want to mask, and a contour'
    'coordinate nested list.'
    mask_array = xr.zeros_like(array)
    slice_dict = {}
    mask_dict = {}

    for i, coord in enumerate(contour_list):
        'Each list inside contour_list is a list of (x,y,z)'
        'coordinates defining the contour on each slice. For each'
        'sequence, the z coord is constant. This for-loop builds'
        'a numpy array for each series of (x,y) coordinates and'
        'stores them in a dict where the corresponding key is the'
        'z-coordinate.'
        x_start = float(example.x[0].values)
        y_start = float(example.y[0].values)
        spacing = float(example.x[0].values) - float(example.x[1].values)
        resized = np.resize(coord, (int(np.size(coord) / 3), 3))
        rtstruct_pixelArray = (resized[:,:2] - [x_start, y_start]) / spacing
        rtstruct_pixelArray = np.rint(rtstruct_pixelArray)
        slice_dict[coord[0][2]] = rtstruct_pixelArray


    for z_slc in slice_dict.keys():
        'For each z-slice, we need to draw a polygon defined by'
        'the points we pulled above. We can do this with ImageDraw.'
        polygon = slice_dict[z_slc].flatten().tolist()
        img = Image.new('L', (example.shape[0], example.shape[1]), 0)
        ImageDraw.Draw(img).polygon(polygon, outline=255, fill=255)
        mask = np.array(img)
        mask_dict[z_slc] = mask

    for z_slc in mask_dict.keys():
        'We can reassign values in mask_array with indexing.'
        'We rely on sel to give us the slice we need. This works'
        'because xarray does not copy the data with sel.'
        mask_slice = mask_array.sel(z=z_slc, method='nearest')
        mask_slice[:,:] = mask_dict[z_slc]

    return mask_array

mask = create_mask(example, contours)

That's a minimally working example!