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| #%config InlineBackend.figure_format = 'retina' | |
| from pathlib import Path | |
| import torch | |
| import torch.nn as nn | |
| import torchio as tio | |
| import numpy as np | |
| from tqdm.notebook import tqdm | |
| import gradio as gr | |
| from matplotlib import pyplot as plt | |
| torch.set_grad_enabled(False); | |
| # Download an example image | |
| import urllib | |
| url, filename = ("https://github.com/mateuszbuda/brain-segmentation-pytorch/raw/master/assets/TCGA_CS_4944.png", "TCGA_CS_4944.png") | |
| try: urllib.URLopener().retrieve(url, filename) | |
| except: urllib.request.urlretrieve(url, filename) | |
| def inference(img): | |
| path = img.name | |
| slices = [tio.ScalarImage(path).data] | |
| tensor = torch.cat(slices, dim=-1) | |
| guessed_affine = np.diag([-1, -1, 9, 1]) | |
| subject = tio.Subject(mri=tio.ScalarImage(tensor=tensor, affine=guessed_affine)) | |
| subject_preprocessed = tio.ZNormalization()(subject) | |
| subject_preprocessed.plot() | |
| subject_preprocessed.mri | |
| patch_overlap = 0 | |
| patch_size = 256, 256, 1 | |
| grid_sampler = tio.inference.GridSampler( | |
| subject_preprocessed, | |
| patch_size, | |
| patch_overlap, | |
| ) | |
| patch_loader = torch.utils.data.DataLoader(grid_sampler, batch_size=8) | |
| aggregator = tio.inference.GridAggregator(grid_sampler) | |
| model = torch.hub.load( | |
| 'mateuszbuda/brain-segmentation-pytorch', | |
| 'unet', | |
| in_channels=3, | |
| out_channels=1, | |
| init_features=32, | |
| pretrained=True, | |
| ) | |
| for patches_batch in tqdm(patch_loader): | |
| input_tensor = patches_batch['mri'][tio.DATA][..., 0] | |
| locations = patches_batch[tio.LOCATION] | |
| probs = model(input_tensor)[..., np.newaxis] | |
| aggregator.add_batch(probs, locations) | |
| output_tensor = aggregator.get_output_tensor() | |
| output_subject = tio.Subject(prediction=tio.ScalarImage(tensor=output_tensor, affine=guessed_affine)) | |
| images = subject_preprocessed.mri.tensor.detach().numpy().reshape((3, 256, 256)) | |
| mask = output_subject.prediction.tensor.detach().numpy().reshape((256, 256)) | |
| images = np.moveaxis(np.moveaxis(images, 0, 2), 0, 1) | |
| mask = np.moveaxis(mask, 0, 1) | |
| f, ax = plt.subplots(1, 2) | |
| ax[0].set_axis_off() | |
| ax[1].set_axis_off() | |
| ax[0].imshow(images) | |
| ax[1].imshow(mask, cmap='gray') | |
| return f | |
| title = "U-NET FOR BRAIN MRI" | |
| description = "Gradio demo for u-net for brain mri, U-Net with batch normalization for biomedical image segmentation with pretrained weights for abnormality segmentation in brain MRI. To use it, simply add your image, or click one of the examples to load them. Read more at the links below." | |
| article = "<p style='text-align: center'><a href='https://mateuszbuda.github.io/2017/12/01/brainseg.html'>Segmentation of brain tumor in magnetic resonance images</a> | <a href='https://github.com/mateuszbuda/brain-segmentation-pytorch'>Github Repo</a></p>" | |
| examples = [ | |
| ['TCGA_CS_4944.png'] | |
| ] | |
| gr.Interface(inference, gr.inputs.Image(label="input image", type='file'), gr.outputs.Image(type='plot'), description=description, article=article, title=title, examples=examples, analytics_enabled=False).launch() |