Simulated MR artifacts (torchio)

import fastai.medical.imaging

If you use these transformations which all build on TorchIO, please cite the following paper: > Pérez-García et al., TorchIO: a Python library for efficient loading, preprocessing, augmentation and patch-based sampling of medical images in deep learning. Link: https://arxiv.org/abs/2003.04696

Spike artifact

import matplotlib.pyplot as plt
from ipywidgets import interact, interactive, fixed, interact_manual
import ipywidgets as widgets

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get_spike_series

 get_spike_series (image, model, start=0, end=2.5, step=0.5,
                   spikePosition=[0.1, 0.1], **kwargs)

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spikeTransform

 spikeTransform (image, intensityFactor, spikePosition=[0.1, 0.1])

fname = "example/b0/images/val_example_0.png"
img = lambda: Image.open(fname).resize((256,256)).convert ("RGB")
trueMask = lambda: Image.open(fname.replace("image","mask"))

spikeTransform(img(), 1, spikePosition=[.5,.5])

def interactiveSpikeTransform(intensity, x, y):
    plt.imshow(spikeTransform(img(), intensity, [x,y])) #figsize=(8,8))

interactive_plot = interactive(
    interactiveSpikeTransform,
    intensity=widgets.FloatSlider(min=0, max=3, step=.1, value=1, continuous_update=True),
    x=widgets.FloatSlider(min=0, max=.99, step=.05, value=.5, continuous_update=True),
    y=widgets.FloatSlider(min=0, max=.99, step=.05, value=.5, continuous_update=True)
)
interactive_plot

plt.imshow(spikeTransform(img(), .8, [.4,.3]), cmap="bone") #figsize=(8,8),

<matplotlib.image.AxesImage>

def label_func(x):
    pass
def acc_seg(input, target):
    pass
def diceComb(input, targs):
    pass
def diceLV(input, targs):
    pass
def diceMY(input, targs):
    pass

trainedModel = Fastai2_model('chfc-cmi/transversal-cmr-seg', 'b0_transversal_5_5', force_reload=False)

Using cache found in /home/markus/.cache/torch/hub/chfc-cmi_transversal-cmr-seg_master

Spike intensity

series = get_spike_series(img(), trainedModel, truth=trueMask(), tfm_y=False)

plot_series(series)

spikePosition = [.45,.55]
plot_series(get_spike_series(img(), trainedModel, truth=trueMask(), spikePosition=spikePosition))

spikePosition = [.5,.52]
plot_series(get_spike_series(img(), trainedModel, truth=trueMask(), spikePosition=spikePosition))

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eval_spike_series

 eval_spike_series (image, mask, model, step=0.1, start=0, end=2.5,
                    spikePosition=[0.1, 0.1], param_name='intensity',
                    **kwargs)

results = eval_spike_series(img(), trueMask(), trainedModel)
plt.plot(results['intensity'], results['c1'], label='LV')
plt.plot(results['intensity'], results['c2'], label='MY')
plt.legend()
plt.xlabel('relative intensity')
plt.ylabel('dice')
_ = plt.title(f'spike position: {[.1,.1]}')

Spike position

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get_spike_pos_series

 get_spike_pos_series (image, model, start=0.1, end=0.9, step=0.1,
                       intensityFactor=0.5, spikePositionY=0.1, **kwargs)

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spikePosTransform

 spikePosTransform (image, spikePositionX, spikePositionY=0.1,
                    intensityFactor=0.5)

series = get_spike_pos_series(img(), trainedModel, intensityFactor=0.1, spikePositionY=0.1)
plot_series(series, nrow=1, figsize=(20,15), param_name='Spike X Position')

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eval_spike_pos_series

 eval_spike_pos_series (image, mask, model, step=0.1, start=0.1, end=0.9,
                        intensityFactor=0.1, param_name='Spike X
                        Position', **kwargs)

intensityFactor = 0.2
results = eval_spike_pos_series(img(), trueMask(), trainedModel, step=0.05, intensityFactor=intensityFactor)
plt.plot(results['Spike X Position'], results['c1'], label='LV')
plt.plot(results['Spike X Position'], results['c2'], label='MY')
plt.legend()
plt.xlabel('X position of the spike')
plt.ylabel('Dice')
_ = plt.title(f'Spike intensity = {intensityFactor}')

gif_series(
    get_spike_pos_series(img(),trainedModel, start=0.1, end=0.9 ,step=0.1, intensityFactor=0.2),
    "example/kaggle/spike_pos.gif",
    param_name="Spike X Position",
    duration=400
)

segmentation sensitivity to spike position

Bias Field (\(B_0\))

The bias field function is still experimental as it lacks configurability. To make it deterministic all coefficients are set to the exact same value. This represents only one specific possibility of field inhomogeneity.

image = img()
image = np.array(image)[:,:,0]
image = np.expand_dims(np.array(image), 0)
image = np.expand_dims(np.array(image), 0)
image.shape

(1, 1, 256, 256)

coefficients = RandomBiasField().get_params(3,[-.3,-.3])

bf = BiasField.generate_bias_field(image, order=3, coefficients=coefficients)

plt.imshow(bf[0])

<matplotlib.image.AxesImage>

image[0][0].shape
image = image/255

image = torch.from_numpy(image)
result = image[0][0] * bf[0]
result = torch.stack((result,result,result))
result = np.array((torch.clamp(result,0,1)))
result = result*255
result = result.astype(np.uint8)
result = np.moveaxis(result, 0, 2)
result = Image.fromarray(result)
plt.imshow(result)

<matplotlib.image.AxesImage>

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get_biasfield_series

 get_biasfield_series (image, model, start=0, end=-0.6, step=-0.2,
                       order=3, **kwargs)

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biasfieldTransform

 biasfieldTransform (image, coef, order=3)

plot_series(get_biasfield_series(img(),trainedModel,step=-.1),nrow=1)

biasfieldTransform(img(),-.5)

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eval_biasfield_series

 eval_biasfield_series (image, mask, model, step=-0.05, start=0,
                        end=-0.55, order=3, **kwargs)

results = eval_biasfield_series(img(), trueMask(), trainedModel)
plt.plot(results['coefficient'], results['c1'], label='LV')
plt.plot(results['coefficient'], results['c2'], label='MY')
plt.legend()
plt.xlabel('coefficient')
_ = plt.ylabel('dice')