visualization

Visualize and animate DRRs in 2D and 3D

2D Visualization

Uses matplotlib and imageio to plot DRRs and animate optimization over DRRs.

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plot_drr

def plot_drr(
    img:torch.Tensor, title:str | None=None, ticks:bool | None=True, axs:matplotlib.axes._axes.Axes | None=None,
    cmap:str='gray', **imshow_kwargs
):

Plot an image generated by a DRR module.

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plot_mask

def plot_mask(
    img:torch.Tensor, axs:matplotlib.axes._axes.Axes,
    colors:list=['rgb(102,194,165)', 'rgb(252,141,98)', 'rgb(141,160,203)', 'rgb(231,138,195)', 'rgb(166,216,84)', 'rgb(255,217,47)', 'rgb(229,196,148)'],
    alpha:float=0.5, return_masks:bool=False
):

Plot a 2D rendered segmentation mask. Meant to be called after plot_drr.

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animate

def animate(
    out:str | pathlib.Path, # Savepath
    df:pandas.DataFrame, drr:DRR, parameterization:str, convention:str=None, ground_truth:torch.Tensor | None=None,
    verbose:bool=True, dtype:dtype=torch.float32, device:str='cpu', **kwargs
):

Animate the optimization of a DRR.

df is a pandas.DataFrame with columns ["alpha", "beta", "gamma", "bx", "by", "bz"]. Each row in df is an iteration of optimization with the updated values for that timestep.

3D Visualization

Uses pyvista and trame to interactively visualize DRR geometry in 3D.

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drr_to_mesh

def drr_to_mesh(
    subject:Subject, # torchio.Subject with a `volume` attribute
    method:str, # Either `surface_nets` or `marching_cubes`
    threshold:float=150, # Min value for marching cubes (Hounsfield units)
    extract_largest:bool=True, # Extract the largest connected component from the mesh
    verbose:bool=True, # Display progress bars for mesh processing steps
):

Convert the CT in a DRR object into a mesh.

If using method=="surface_nets", ensure you have pyvista>=0.43 and vtk>=9.3 installed.

The mesh processing steps are:

  1. Keep only largest connected components (optional)
  2. Smooth
  3. Decimate (if method=="marching_cubes")
  4. Fill any holes
  5. Clean (remove any redundant vertices/edges)

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labelmap_to_mesh

def labelmap_to_mesh(
    subject:Subject, # torchio.Subject with  a `mask` attribute
    verbose:bool=True, # Display progress bars for mesh processing steps
):

Call self as a function.

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img_to_mesh

def img_to_mesh(
    drr:DRR, pose:RigidTransform, calibration:RigidTransform=None, **kwargs
):

For a given pose (not batched), turn the camera and detector into a mesh. Additionally, render the DRR for the pose. Convert into a texture that can be applied to the detector mesh.

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add_image

def add_image(
    drr:DRR, pose:RigidTransform, pl:pyvista.Plotter
):

Add a camera to an existing scene.

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visualize_scene

def visualize_scene(
    drr:DRR, pose:RigidTransform, labelmap:bool=False, grid:bool=True, verbose:bool=False, **kwargs
):

Given a DRR and a RigidTransform, render the 3D scene in PyVista. **kwargs are passed to drr_to_mesh.