Utils for loading, augmenting and writing 3D medical images on PyTorch.
Project description
TorchIO
torchio is a Python package containing a set of tools to efficiently
read, sample and write 3D medical images in deep learning applications
written in PyTorch,
including intensity and spatial transforms
for data augmentation. Transforms include typical computer vision operations
such as random affine transformations and also domain specific ones such as
simulation of intensity artifacts due to
MRI magnetic field inhomogeneity
(work in progress) or
k-space motion artifacts.
This package has been greatly inspired by NiftyNet.
Index
Features
Dataset
ImagesDataset is a reader of medical images that directly inherits from
torch.utils.Dataset.
It can be used with a
torch.utils.DataLoader
for efficient reading and data augmentation.
import torchio
subject_a = {
't1': dict(path='~/Dropbox/MRI/t1.nii.gz', type=torchio.INTENSITY),
'label': dict(path='~/Dropbox/MRI/t1_seg.nii.gz', type=torchio.LABEL),
}
subject_b = {
't1': dict(path='/tmp/colin27_t1_tal_lin.nii.gz', type=torchio.INTENSITY),
'label': dict(path='/tmp/colin27_seg1.nii.gz', type=torchio.LABEL),
}
paths_list = [subject_a, subject_b]
subjects_dataset = torchio.ImagesDataset(paths_list)
subject_sample = subjects_dataset[0]
Samplers
torchio includes grid, uniform and label patch samplers. There is also an
aggregator used for dense predictions. The code for these is almost
copy-pasted from NiftyNet.
For more information about patch-based training, see NiftyNet docs.
import torch
import torchio
CHANNELS_DIMENSION = 1
patch_overlap = 4
grid_sampler = torchio.inference.GridSampler(
input_array, # some NumPy array
patch_size=128,
patch_overlap=patch_overlap,
)
patch_loader = torch.utils.data.DataLoader(grid_sampler, batch_size=4)
aggregator = torchio.inference.GridAggregator(
input_array,
patch_overlap=patch_overlap,
)
with torch.no_grad():
for patches_batch in patch_loader:
input_tensor = patches_batch['one_modality']
locations = patches_batch['location']
logits = model(input_tensor) # some torch.nn.Module
labels = logits.argmax(dim=CHANNELS_DIMENSION, keepdim=True)
outputs = labels
aggregator.add_batch(outputs, locations)
output_array = aggregator.output_array
Queue
A patches Queue (or buffer) can be used for randomized patch-based sampling
during training.
This interactive animation
can be used to understand how the queue works.
import torch
import torchio
queue_dataset = torchio.Queue(
subjects_dataset=subjects_dataset,
queue_length=300,
samples_per_volume=10,
patch_size=96,
sampler_class=torchio.sampler.ImageSampler,
num_workers=4,
)
batch_loader = DataLoader(queue_dataset, batch_size=4)
num_epochs = 20
for epoch_index in range(num_epochs):
for batch in batch_loader:
logits = model(batch) # model is some torch.nn.Module
Transforms
The transforms package should remind users of
torchvision.transforms.
They take as input the samples generated by an
ImagesDataset.
Intensity
MRI k-space motion artifacts
Magnetic resonance images suffer from motion artifacts when the subject moves during image acquisition. This transform follows Shaw et al., 2019 to simulate motion artifacts for data augmentation.
MRI magnetic field inhomogeneity
WIP.
Normalization
Histogram standardization
Z-normalization
Rescale
Noise
Spatial
Flip
Affine transform
B-spline dense deformation
Installation
I recommend cloning and doing an editable installation, as this is still very experimental and changes very often.
git clone https://github.com/fepegar/torchio.git
pip install --editable torchio
If you want to quickly try the latest released version, just run
pip install torchio
Example
import time
import multiprocessing as mp
from torch.utils.data import DataLoader
from torchvision.transforms import Compose
from torchio import ImagesDataset, Queue
from torchio.sampler import ImageSampler
from torchio.utils import create_dummy_dataset
from torchio.transforms import (
ZNormalization,
RandomNoise,
RandomFlip,
RandomAffine,
)
# Define training and patches sampling parameters
num_epochs = 4
patch_size = 128
queue_length = 100
samples_per_volume = 10
batch_size = 4
def model(batch, sleep_time=0.1):
"""Dummy function to simulate a forward pass through the network"""
time.sleep(sleep_time)
return batch
# Create a dummy dataset in the temporary directory, for this example
subjects_paths = create_dummy_dataset(
num_images=100,
size_range=(193, 229),
force=False,
)
# Each element of subjects_paths is a dictionary:
# subject = {
# 'one_image': dict(path=path_to_one_image, type=torchio.INTENSITY),
# 'another_image': dict(path=path_to_another_image, type=torchio.INTENSITY),
# 'a_label': dict(path=path_to_a_label, type=torchio.LABEL),
# }
# Define transforms for data normalization and augmentation
transforms = (
ZNormalization(),
RandomNoise(std_range=(0, 0.25)),
RandomAffine(scales=(0.9, 1.1), degrees=10),
RandomFlip(axes=(0,)),
)
transform = Compose(transforms)
subjects_dataset = ImagesDataset(subjects_paths, transform)
sample = subjects_dataset[0]
# Run a benchmark for different numbers of workers
workers = range(mp.cpu_count() + 1)
for num_workers in workers:
print('Number of workers:', num_workers)
# Define the dataset as a queue of patches
queue_dataset = Queue(
subjects_dataset,
queue_length,
samples_per_volume,
patch_size,
ImageSampler,
num_workers=num_workers,
)
batch_loader = DataLoader(queue_dataset, batch_size=batch_size)
start = time.time()
for epoch_index in range(num_epochs):
for batch in batch_loader:
logits = model(batch)
print('Time:', int(time.time() - start), 'seconds')
print()
Output:
Number of workers: 0
Time: 394 seconds
Number of workers: 1
Time: 372 seconds
Number of workers: 2
Time: 278 seconds
Number of workers: 3
Time: 259 seconds
Number of workers: 4
Time: 242 seconds
======= History
0.2.0 (2019-12-06)
- First release on PyPI.
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