Ljubljana dataset

PyTorch wrapper for the Ljubljana dataset with parsing of pose parameters

Ljubljana

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LjubljanaDataset

 LjubljanaDataset (view:str,
                   filename:Union[str,pathlib.Path,NoneType]=None,
                   preprocess:bool=True)

Get X-ray projections and poses from specimens in the Ljubljana dataset.

Given a specimen ID and projection index, returns the projection and the camera matrix for DiffDRR.

	Type	Default	Details
view	str		“ap” or “lat” or “ap-max” or “lat-max”
filename	Union	None	Path to DeepFluoro h5 file
preprocess	bool	True	Preprocess X-rays

Distribution over camera poses

We sample the three rotational and three translational parameters of $\mathfrak{se}(3)$ from independent normal distributions defined with sufficient variance to capture wide perturbations from the isocenter.

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get_random_offset

 get_random_offset (view, batch_size:int, device)

Fiducial markers

The LjubljanaDataset class also contains a method for evaluating the registration error for a predicted pose. Digital fiducial markers were placed along the centerlines of the vessels. Projecting them with predicted pose parameters can be used to measure their distance from the true fiducials.

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Evaluator

 Evaluator (subject, idx)

Initialize self. See help(type(self)) for accurate signature.

Deep learning transforms

We transform X-rays and DRRs before inputting them to a deep learning model by

• Rescaling pixels to [0, 1]
• Resizing the images to a specified size
• Normalizing pixels by the mean and std dev

from tqdm import tqdm

mean, vars = [], []
for view in ["ap", "lat"]:
    specimen = LjubljanaDataset(view, filename="../../data/ljubljana.h5")
    for _, _, _, _, _, _, _, _, _, img, _, _ in tqdm(specimen, ncols=50):
        img = (img - img.min()) / (img.max() - img.min())
        mean.append(img.mean())
        vars.append(img.var())

print("Pixel mean :", sum(mean) / len(mean))
print("Pixel std dev :", (sum(vars) / len(vars)).sqrt())

100%|█████████████| 10/10 [00:12<00:00,  1.25s/it]
100%|█████████████| 10/10 [00:03<00:00,  2.76it/s]

Pixel mean : tensor(0.0774)
Pixel std dev : tensor(0.0569)

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Transforms

 Transforms (height:int, width:int, eps:float=1e-06)

Transform X-rays and DRRs before inputting to CNN.

Examples

import matplotlib.pyplot as plt
from diffdrr.drr import DRR
from diffdrr.visualization import plot_drr

subject = LjubljanaDataset(view="ap", filename="../../data/ljubljana.h5")

for idx in range(len(subject)):
    (
        volume,
        spacing,
        focal_len,
        height,
        width,
        delx,
        dely,
        x0,
        y0,
        img,
        pose,
        isocenter_pose,
    ) = subject[idx]
    volume[volume < 500] = 0.0
    if idx == 5:
        volume[volume < 1000] = 0.0

    drr = DRR(
        volume,
        spacing,
        focal_len / 2,
        height // 8,
        delx * 8,
        width // 8,
        dely * 8,
        x0,
        y0,
        reverse_x_axis=True,
    ).cuda()
    transforms = Transforms(height // 8, width // 8)

    img = transforms(img).cuda()
    pred_img = drr(None, None, None, pose=pose.cuda())
    pred_img = transforms(pred_img)

    plt.figure(figsize=(12, 3))
    ax = plt.subplot(131)
    plot_drr(img, axs=ax, title="DSA")
    ax = plt.subplot(132)
    plot_drr(pred_img, axs=ax, title="DRR")
    ax = plt.subplot(133)
    plot_drr(img - pred_img, axs=ax, title="Difference", cmap="RdBu")
    plt.show()

subject = LjubljanaDataset(view="lat", filename="../../data/ljubljana.h5")

for idx in range(len(subject)):
    (
        volume,
        spacing,
        focal_len,
        height,
        width,
        delx,
        dely,
        x0,
        y0,
        img,
        pose,
        isocenter_pose,
    ) = subject[idx]
    volume[volume < 500] = 0.0
    if idx == 5:
        volume[volume < 1000] = 0.0

    drr = DRR(
        volume,
        spacing,
        focal_len / 2,
        height // 8,
        delx * 8,
        width // 8,
        dely * 8,
        x0,
        y0,
        reverse_x_axis=True,
    ).cuda()
    transforms = Transforms(height // 8, width // 8)

    img = transforms(img).cuda()
    pred_img = drr(None, None, None, pose=pose.cuda())
    pred_img = transforms(pred_img)

    plt.figure(figsize=(12, 3))
    ax = plt.subplot(131)
    plot_drr(img, axs=ax, title="DSA")
    ax = plt.subplot(132)
    plot_drr(pred_img, axs=ax, title="DRR")
    ax = plt.subplot(133)
    plot_drr(img - pred_img, axs=ax, title="Difference", cmap="RdBu")
    plt.show()