bbox(): correct coordinates?

[WGierke]

Hey, I'd like to continue my post from #9 here since it appears to me to be a real issue. I'd expect bbox() to enclose an annotation in a cuboid. However, from my POV this appears to be not the case:

import matplotlib.pyplot as plt
import numpy as np
import pylidc as pl
from matplotlib.widgets import Slider

def cube_show_slider(cube, axis=2, is_mask=False, **kwargs):
    """
    Display a 3d ndarray with a slider to move along the third dimension.

    Extra keyword arguments are passed to imshow
    (Kudos to http://nbarbey.github.io/2011/07/08/matplotlib-slider.html)
    """
    import matplotlib.pyplot as plt
    from matplotlib.widgets import Slider

    # check dim
    if not cube.ndim == 3:
        raise ValueError("cube should be an ndarray with ndim == 3")

    # generate figure
    fig = plt.figure()
    subplot = fig.add_subplot(111)
    fig.subplots_adjust(left=0.25, bottom=0.25)

    if is_mask:
        im1 = subplot.imshow(cube[:, :, 0])
        fig.colorbar(im1)
    else:
        # select first image
        s = [slice(0, 1) if i == axis else slice(None) for i in range(3)]
        im = cube[s].squeeze()

        # display image
        l = subplot.imshow(im, **kwargs)

    # define slider
    axcolor = 'lightgoldenrodyellow'
    ax = fig.add_axes([0.25, 0.1, 0.65, 0.03], axisbg=axcolor)

    slider = Slider(ax, 'Axis %i index' % axis, 0, cube.shape[axis] - 1, valinit=0, valfmt='%i')

    def update(val):
        if is_mask:
            subplot.imshow(cube[:, :, int(val)])
            fig.canvas.draw()
        else:
            ind = int(slider.val)
            s = [slice(ind, ind + 1) if i == axis else slice(None)
                 for i in range(3)]
            im = cube[s].squeeze()
            l.set_data(im, **kwargs)
        fig.canvas.draw()

    slider.on_changed(update)

    plt.show()

def display_training_pair(input_cube, output_cube, axis=2, **kwargs):
    # check dims
    if not input_cube.ndim == 3 or not output_cube.ndim == 3:
        raise ValueError("cube should be an ndarray with ndim == 3")

    # generate figure
    fig = plt.figure()
    subplot_input = fig.add_subplot(221)
    subplot_output = fig.add_subplot(222)
    fig.subplots_adjust(left=0.25, bottom=0.25)

    # select first image
    im1 = subplot_output.imshow(output_cube[:, :, 0])
    fig.colorbar(im1)

    s = [slice(0, 1) if i == axis else slice(None) for i in range(3)]
    im = input_cube[s].squeeze()
    # display image
    l = subplot_input.imshow(im, **kwargs)

    # define slider
    axcolor = 'lightgoldenrodyellow'
    ax = fig.add_axes([0.25, 0.1, 0.65, 0.03], axisbg=axcolor)

    slider = Slider(ax, 'Axis %i index' % axis, 0, input_cube.shape[axis] - 1, valinit=0, valfmt='%i')

    def update(val):
        subplot_output.imshow(output_cube[:, :, int(val)])

        ind = int(slider.val)
        s = [slice(ind, ind + 1) if i == axis else slice(None)
             for i in range(3)]
        im = input_cube[s].squeeze()
        l.set_data(im, **kwargs)
        fig.canvas.draw()

    slider.on_changed(update)

    plt.show()

if __name__ == '__main__':
    scan = pl.query(pl.Scan).filter(pl.Scan.patient_id == 'LIDC-IDRI-0001').first()
    Z_RANGE = 340  # (from -340 to 0)
    SLICES = 133
    scan_volume = scan.to_volume()
    scan_volume_mask = np.zeros(scan_volume.shape)
    an = scan.annotations[0]
    an_box = an.bbox()
    z_start = int((Z_RANGE + an_box[2, 0]) / Z_RANGE * SLICES)
    z_end = int((Z_RANGE + an_box[2, 1]) / Z_RANGE * SLICES)
    scan_volume_mask[int(an_box[0, 0]):int(an_box[0, 1]),
                     int(an_box[1, 0]):int(an_box[1, 1]),
                     int(z_start):int(z_end)] = 1

    print("Expected outcome when mask is applied to lung image:")
    vol, seg = an.uniform_cubic_resample(49)
    cube_show_slider(vol * seg, is_mask=True)

    print("Current outcome:")
    display_training_pair(scan_volume, scan_volume_mask)
    cube_show_slider(scan_volume * scan_volume_mask, is_mask=True)

... produces the correct expected result when the binary mask is applied to the data (here using uniform_cubic_resample): but one can see that the binary mask positioned using the bbox() coordinates is not at the exact same position as the annotation which becomes even clearer when the mask is applied to the data and the annotation is not included in the masked image

Am I doing something wrong? All I want is a binary image whose pixels are 1 if there is a cancerous annotation and otherwise 0 while the image shape is the same as the one of scan.to_volume().

[notmatthancock]

You should set the option, an_box = an.bbox(image_coords=True). Actually, image_coords=True should probably be the default for this function so it is consistent with the returned bbox of get_boolean_mask(return_bbox=True).

Try out this code. If you have non-issue questions about using the software, feel free to email me.

[WGierke]

Your snippet works like a charm, thanks a lot! Sorry for the inconvenience.

[notmatthancock]

glad it worked out!