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Add the updated instructions for downloading data
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,108 @@ | ||
| import os | ||
| import random | ||
| import numpy as np | ||
| import nibabel as nib | ||
| from pathlib import Path | ||
| import matplotlib.pyplot as plt | ||
| import matplotlib.animation as animation | ||
| from IPython.display import clear_output | ||
| plt.rcParams['animation.html'] = 'jshtml' | ||
| plt.rcParams['animation.embed_limit'] = 2**128 | ||
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| def load_patient_volume(dir_path, idx=None): | ||
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| all_patient_paths = set(nifti_file.parent for nifti_file in Path(dir_path).rglob('*.nii*')) | ||
| if idx is None or idx < len(all_patient_paths): | ||
| patient_path = random.choice(list(all_patient_paths)) | ||
| else: | ||
| patient_path = list(all_patient_paths)[idx] | ||
| vol_FLAIR = nib.load(next(patient_path.rglob('*FLAIR.nii*'))).get_fdata(dtype=np.float32).T | ||
| vol_T1c = nib.load(next(patient_path.rglob('*T1c.nii*'))).get_fdata(dtype=np.float32).T | ||
| vol_T2 = nib.load(next(patient_path.rglob('*T2.nii*'))).get_fdata(dtype=np.float32).T | ||
| vol_SWI = nib.load(next(patient_path.rglob('*SWI.nii*'))).get_fdata(dtype=np.float32).T | ||
| vol_mask = nib.load(next(patient_path.rglob('*tumor*.nii*'))).get_fdata(dtype=np.float32).T | ||
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| multimodal_vol = np.stack([vol_FLAIR,vol_T1c,vol_T2,vol_SWI]) | ||
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| return multimodal_vol, vol_mask | ||
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| def plot_masked_volumes(mri_vol, mri_mask, figsize=(20,4), save=False, norm=0.4): | ||
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| modalities, (min_slice,mid_slice,max_slice) = preprocess_volumes(mri_vol,mri_mask) | ||
| norm_mri_mask = (mri_mask/mri_mask.max())*norm | ||
| fig, ax = plt.subplots(1,len(modalities), figsize=figsize) | ||
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| ims = [] | ||
| for n, (vol,name) in enumerate(modalities): | ||
| # Set the initial image | ||
| ims += [ax[n].imshow(vol[mid_slice,...] + norm_mri_mask[mid_slice,...], aspect='auto', animated=True)] | ||
| ax[n].set_title(name) | ||
| ax[n].set_axis_off() | ||
|
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| def update(i): | ||
| for n, (vol,_) in enumerate(modalities): | ||
| ims[n].set_data(vol[min_slice:max_slice,...][i] + norm_mri_mask[min_slice:max_slice,...][i]) | ||
| return ims | ||
|
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| clear_output() | ||
|
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| # Create the animation object | ||
| animation_fig = animation.FuncAnimation(fig, update, frames=max_slice-min_slice, interval=100, blit=True, repeat_delay=10) | ||
|
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| # Show the animation | ||
| animation_fig | ||
| if save: | ||
| animation_fig.save('./img/all_mri_mask.gif', writer='pillow') | ||
|
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| # Show the animation | ||
| return animation_fig | ||
|
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| def plot_mask_vs_preds(mri_preds, mri_mask, figsize=(10,4), save=False): | ||
|
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| multimodal_mri = np.stack([mri_preds,mri_preds,mri_preds,mri_preds]) | ||
| modalities, (min_slice,mid_slice,max_slice) = preprocess_volumes(multimodal_mri,mri_mask) | ||
| fig, ax = plt.subplots(1,2,figsize=figsize) | ||
|
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| vol,_ = modalities[0] | ||
| ims = [] | ||
| # Set the initial image | ||
| ims = [ax[0].imshow(vol[mid_slice,...], aspect='auto', animated=True), | ||
| ax[1].imshow(mri_mask[mid_slice,...], aspect='auto', animated=True)] | ||
| ax[0].set_title(f'Predicted') | ||
| ax[1].set_title(f'Mask') | ||
| ax[0].set_axis_off() | ||
| ax[1].set_axis_off() | ||
|
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| def update(i): | ||
| ims[0].set_data(vol[min_slice:max_slice,...][i]) | ||
| ims[1].set_data(mri_mask[min_slice:max_slice,...][i]) | ||
| return ims | ||
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| clear_output() | ||
|
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| # Create the animation object | ||
| animation_fig = animation.FuncAnimation(fig, update, frames=max_slice-min_slice, interval=100, blit=True, repeat_delay=10) | ||
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| if save: | ||
| animation_fig.save('./img/mri_preds_vs_mask.gif', writer='pillow') | ||
|
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| # Show the animation | ||
| return animation_fig | ||
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| def preprocess_volumes(mri_vol, mri_mask): | ||
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| norm_mri_vol = mri_vol/mri_vol.max(axis=(1,2,3)).reshape(4,1,1,1) | ||
| modalities = [(norm_mri_vol[0,...], 'FLAIR'), (norm_mri_vol[1,...], 'T1c'), (norm_mri_vol[2,...], 'T2'), (norm_mri_vol[3,...], 'SWI')] | ||
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| min_mask_slice = np.argwhere(mri_mask)[:,0].min() | ||
| max_mask_slice = np.argwhere(mri_mask)[:,0].max() | ||
| min_vol_slice = np.argwhere(mri_vol[0,...])[:,0].min() | ||
| max_vol_slice = np.argwhere(mri_vol[0,...])[:,0].max() | ||
| min_slice = min_vol_slice + abs(min_mask_slice - min_vol_slice)//2 | ||
| max_slice = max_vol_slice - abs(max_vol_slice - max_mask_slice)//2 | ||
| mid_slice = min_slice + (max_slice - min_slice)//2 | ||
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| return modalities, (min_slice,mid_slice,max_slice) |
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