Mouse Nuclei

The mouse nuclei dataset is composed of a train and test dataset.

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# Imports necessary to execute the code

import matplotlib.pyplot as plt
import numpy as np
from careamics import CAREamist
from careamics.config import create_n2v_configuration
from careamics.utils.metrics import scale_invariant_psnr
from careamics_portfolio import PortfolioManager
from PIL import Image
# Imports necessary to execute the code import matplotlib.pyplot as plt import numpy as np from careamics import CAREamist from careamics.config import create_n2v_configuration from careamics.utils.metrics import scale_invariant_psnr from careamics_portfolio import PortfolioManager from PIL import Image

Import the dataset¶

The dataset can be directly downloaded using the careamics-portfolio package, which uses pooch to download the data.

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# download file
portfolio = PortfolioManager()
files = portfolio.denoiseg.MouseNuclei_n20.download()
files.sort()

# load images
train_data = np.load(files[1])["X_train"]
print(f"Train data shape: {train_data.shape}")
# download file portfolio = PortfolioManager() files = portfolio.denoiseg.MouseNuclei_n20.download() files.sort() # load images train_data = np.load(files[1])["X_train"] print(f"Train data shape: {train_data.shape}")

Data description¶

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portfolio.denoiseg.MouseNuclei_n20.description
portfolio.denoiseg.MouseNuclei_n20.description

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'A dataset depicting diverse and non-uniformly clustered nuclei in the mouse skull, consisting of 908 training and 160 validation patches. The test set counts 67 additional images'

Visualize data¶

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indices = [34, 293, 571, 783]

fig, ax = plt.subplots(2, 2, figsize=(8, 8))
ax[0, 0].imshow(train_data[indices[0]], cmap="gray")
ax[0, 0].set_title(f"Image {indices[0]}")
ax[0, 0].set_xticks([])
ax[0, 0].set_yticks([])

ax[0, 1].imshow(train_data[indices[1]], cmap="gray")
ax[0, 1].set_title(f"Image {indices[1]}")
ax[0, 1].set_xticks([])
ax[0, 1].set_yticks([])

ax[1, 0].imshow(train_data[indices[2]], cmap="gray")
ax[1, 0].set_title(f"Image {indices[2]}")
ax[1, 0].set_xticks([])
ax[1, 0].set_yticks([])

ax[1, 1].imshow(train_data[indices[3]], cmap="gray")
ax[1, 1].set_title(f"Image {indices[3]}")
ax[1, 1].set_xticks([])
ax[1, 1].set_yticks([])

plt.show()
indices = [34, 293, 571, 783] fig, ax = plt.subplots(2, 2, figsize=(8, 8)) ax[0, 0].imshow(train_data[indices[0]], cmap="gray") ax[0, 0].set_title(f"Image {indices[0]}") ax[0, 0].set_xticks([]) ax[0, 0].set_yticks([]) ax[0, 1].imshow(train_data[indices[1]], cmap="gray") ax[0, 1].set_title(f"Image {indices[1]}") ax[0, 1].set_xticks([]) ax[0, 1].set_yticks([]) ax[1, 0].imshow(train_data[indices[2]], cmap="gray") ax[1, 0].set_title(f"Image {indices[2]}") ax[1, 0].set_xticks([]) ax[1, 0].set_yticks([]) ax[1, 1].imshow(train_data[indices[3]], cmap="gray") ax[1, 1].set_title(f"Image {indices[3]}") ax[1, 1].set_xticks([]) ax[1, 1].set_yticks([]) plt.show()

No description has been provided for this image

Train with CAREamics¶

The easiest way to use CAREamics is to create a configuration and a CAREamist.

Create configuration¶

The configuration can be built from scratch, giving the user full control over the various parameters available in CAREamics. However, a straightforward way to create a configuration for a particular algorithm is to use one of the convenience functions.

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config = create_n2v_configuration(
    experiment_name="mouse_nuclei_n2v",
    data_type="array",
    axes="SYX",
    patch_size=(64, 64),
    batch_size=16,
    num_epochs=10,
)

print(config)
config = create_n2v_configuration( experiment_name="mouse_nuclei_n2v", data_type="array", axes="SYX", patch_size=(64, 64), batch_size=16, num_epochs=10, ) print(config)

{'algorithm_config': {'algorithm': 'n2v',
                      'loss': 'n2v',
                      'lr_scheduler': {'name': 'ReduceLROnPlateau',
                                       'parameters': {}},
                      'model': {'architecture': 'UNet',
                                'conv_dims': 2,
                                'depth': 2,
                                'final_activation': 'None',
                                'in_channels': 1,
                                'independent_channels': True,
                                'n2v2': False,
                                'num_channels_init': 32,
                                'num_classes': 1},
                      'optimizer': {'name': 'Adam',
                                    'parameters': {'lr': 0.0001}}},
 'data_config': {'axes': 'SYX',
                 'batch_size': 16,
                 'data_type': 'array',
                 'patch_size': [64, 64],
                 'transforms': [{'flip_x': True,
                                 'flip_y': True,
                                 'name': 'XYFlip',
                                 'p': 0.5},
                                {'name': 'XYRandomRotate90', 'p': 0.5},
                                {'masked_pixel_percentage': 0.2,
                                 'name': 'N2VManipulate',
                                 'roi_size': 11,
                                 'strategy': 'uniform',
                                 'struct_mask_axis': 'none',
                                 'struct_mask_span': 5}]},
 'experiment_name': 'mouse_nuclei_n2v',
 'training_config': {'accumulate_grad_batches': 1,
                     'check_val_every_n_epoch': 1,
                     'checkpoint_callback': {'auto_insert_metric_name': False,
                                             'mode': 'min',
                                             'monitor': 'val_loss',
                                             'save_last': True,
                                             'save_top_k': 3,
                                             'save_weights_only': False,
                                             'verbose': False},
                     'enable_progress_bar': True,
                     'gradient_clip_algorithm': 'norm',
                     'max_steps': -1,
                     'num_epochs': 10,
                     'precision': '32'},
 'version': '0.1.0'}

Train¶

A CAREamist can be created using a configuration alone, and then be trained by using the data already loaded in memory.

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# instantiate a CAREamist
careamist = CAREamist(source=config)

# train
careamist.train(
    train_source=train_data,
)
# instantiate a CAREamist careamist = CAREamist(source=config) # train careamist.train( train_source=train_data, )

Predict with CAREamics¶

Prediction is done with the same CAREamist used for training. Because the image is large we predict using tiling.

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prediction = careamist.predict(source=train_data)
prediction = careamist.predict(source=train_data)

Visualize the prediction¶

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# download ground truth
files = portfolio.denoiseg.MouseNuclei_n0.download()
files.sort()

# load images
gt_data = np.load(files[1])["X_train"]
print(f"GT data shape: {gt_data.shape}")
# download ground truth files = portfolio.denoiseg.MouseNuclei_n0.download() files.sort() # load images gt_data = np.load(files[1])["X_train"] print(f"GT data shape: {gt_data.shape}")

GT data shape: (908, 128, 128)

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indices = [389, 621]

for i in indices:
    # compute psnr
    psnr_noisy = scale_invariant_psnr(gt_data[i], train_data[i])
    psnr_denoised = scale_invariant_psnr(gt_data[i], prediction[i].squeeze())

    # plot images
    fig, ax = plt.subplots(1, 3, figsize=(10, 10))
    ax[0].imshow(train_data[i], cmap="gray")
    ax[0].set_title(f"Noisy Image\nPSNR: {psnr_noisy:.2f}")
    ax[0].set_xticks([])
    ax[0].set_yticks([])

    ax[1].imshow(prediction[i].squeeze(), cmap="gray")
    ax[1].set_title(f"Denoised Image\nPSNR: {psnr_denoised:.2f}")
    ax[1].set_xticks([])
    ax[1].set_yticks([])

    ax[2].imshow(gt_data[i], cmap="gray")
    ax[2].set_title("GT Image")
    ax[2].set_xticks([])
    ax[2].set_yticks([])

    plt.show()
indices = [389, 621] for i in indices: # compute psnr psnr_noisy = scale_invariant_psnr(gt_data[i], train_data[i]) psnr_denoised = scale_invariant_psnr(gt_data[i], prediction[i].squeeze()) # plot images fig, ax = plt.subplots(1, 3, figsize=(10, 10)) ax[0].imshow(train_data[i], cmap="gray") ax[0].set_title(f"Noisy Image\nPSNR: {psnr_noisy:.2f}") ax[0].set_xticks([]) ax[0].set_yticks([]) ax[1].imshow(prediction[i].squeeze(), cmap="gray") ax[1].set_title(f"Denoised Image\nPSNR: {psnr_denoised:.2f}") ax[1].set_xticks([]) ax[1].set_yticks([]) ax[2].imshow(gt_data[i], cmap="gray") ax[2].set_title("GT Image") ax[2].set_xticks([]) ax[2].set_yticks([]) plt.show()

Compute metrics¶

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psnrs = np.zeros(gt_data.shape[0])

for i in range(gt_data.shape[0]):
    psnrs[i] = scale_invariant_psnr(gt_data[i], prediction[i].squeeze())

print(f"PSNR: {np.mean(psnrs):.2f} ± {np.std(psnrs):.2f}")
psnrs = np.zeros(gt_data.shape[0]) for i in range(gt_data.shape[0]): psnrs[i] = scale_invariant_psnr(gt_data[i], prediction[i].squeeze()) print(f"PSNR: {np.mean(psnrs):.2f} ± {np.std(psnrs):.2f}")

PSNR: 32.45 ± 1.91

Export the model¶

The model is automatically saved during training (the so-called checkpoints) and can be loaded back easily, but you can also export the model to the BioImage Model Zoo format.

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# create a cover image
index_cover = 5
height, width = 128, 128

# create image
cover = np.zeros((height, width))

# normalize train and prediction
norm_train = (train_data[index_cover] - train_data[index_cover].min()) / (
    train_data[index_cover].max() - train_data[index_cover].min()
)

pred = prediction[index_cover].squeeze()
norm_pred = (pred - pred.min()) / (pred.max() - pred.min())

# fill in halves
cover[:, : width // 2] = norm_train[:height, : width // 2]
cover[:, width // 2 :] = norm_pred[:height, width // 2 :]

# plot the single image
plt.imshow(cover, cmap="gray")

# save the image
im = Image.fromarray(cover * 255)
im = im.convert("L")
im.save("MouseNuclei_Noise2Void.jpeg")
# create a cover image index_cover = 5 height, width = 128, 128 # create image cover = np.zeros((height, width)) # normalize train and prediction norm_train = (train_data[index_cover] - train_data[index_cover].min()) / ( train_data[index_cover].max() - train_data[index_cover].min() ) pred = prediction[index_cover].squeeze() norm_pred = (pred - pred.min()) / (pred.max() - pred.min()) # fill in halves cover[:, : width // 2] = norm_train[:height, : width // 2] cover[:, width // 2 :] = norm_pred[:height, width // 2 :] # plot the single image plt.imshow(cover, cmap="gray") # save the image im = Image.fromarray(cover * 255) im = im.convert("L") im.save("MouseNuclei_Noise2Void.jpeg")

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general_description = (
    "This model is a UNet trained with the Noise2Void algorithm to denoise images. "
    "The training data consists of images of non-uniformly clustered nuclei in the "
    "mouse skull. The notebook used to train this model is available on the CAREamics "
    "documentation website at the following link: "
    "https://careamics.github.io/0.1/applications/Noise2Void/Mouse_Nuclei/."
)
print(general_description)
general_description = ( "This model is a UNet trained with the Noise2Void algorithm to denoise images. " "The training data consists of images of non-uniformly clustered nuclei in the " "mouse skull. The notebook used to train this model is available on the CAREamics " "documentation website at the following link: " "https://careamics.github.io/0.1/applications/Noise2Void/Mouse_Nuclei/." ) print(general_description)

This model is a UNet trained with the Noise2Void algorithm to denoise images. The training data consists of images of non-uniformly clustered nuclei in the mouse skull. The notebook used to train this model is available on the CAREamics documentation website at the following link: https://careamics.github.io/0.1/applications/Noise2Void/Mouse_Nuclei/.

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# Export the model
careamist.export_to_bmz(
    path_to_archive="mousenuclei_n2v_model.zip",
    friendly_model_name="MouseNuclei_N2V",
    input_array=train_data[[indices[0]]].astype(np.float32),
    authors=[{"name": "CAREamics authors", "affiliation": "Human Technopole"}],
    data_description=portfolio.denoiseg.MouseNuclei_n20.description,
    general_description=general_description
)
# Export the model careamist.export_to_bmz( path_to_archive="mousenuclei_n2v_model.zip", friendly_model_name="MouseNuclei_N2V", input_array=train_data[[indices[0]]].astype(np.float32), authors=[{"name": "CAREamics authors", "affiliation": "Human Technopole"}], data_description=portfolio.denoiseg.MouseNuclei_n20.description, general_description=general_description )