COVID-19 lung CT image segmentation
B y :
S w a p n a Ta l l a
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Agenda
Segmentation Process
Dataset Description
Methods
Visualization of CT Image
Normalization & Splitting Data
Model Creation
Model Compilation and Evaluation
Conclusion
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Segmentation
Process
Loading the Dataset
Preprocessing the data
Data Splitting
Model Creation(U-Net)
Model Compilation
Model Evaluation
Model Inference
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Dataset
It consists of two parts:
Medseg part:
images_medseg.npy - training images – 100 slices 512x512 size
masks_medseg.npy - training masks – 100 masks with 4 channels: (0
- "ground glass", 1 - "consolidations", 2 - "lungs other", 3 -
"background" )
test_images_medseg.npy - test images – 10 slices 512x512 size
Radiopedia part:
images_radiopsedia.npy - training images – 829 slices 512x512 size
masks_radiopedia.npy - training masks – 829 masks with 4
channels: (0 - "ground glass", 1 - "consolidations", 2 - "lungs other", 3
- "background" )
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Methods
U-Net Architecture:
Images were segmented by a radiologist using 3 labels: ground-glass
(mask value =1), consolidation (=2) and pleural effusion (=3).
Hyperparameter Tuning:
 Size of the Conv2D layers' filters (3x3)
 Filter count in Conv2D layers (32, 64, 128, 256, 512)
 Conv2DTranspose layers' stride size, for instance in (2, 2)
 Activation Function, "relu"
 padding ( "same")
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Random CT Image
Visualization(medseg)
# Choose a random image and its mask to
visualize
idx = 0 # index of the image you want to v
isualize
image = images_medseg[idx, :, :, 0]
mask = masks_medseg[idx, :, :, 0]
fig, axs = plt.subplots(1, 3, figsize=(20, 10
))
axs[0].imshow(image, cmap='gray')
axs[0].set_title('CT Image')
axs[1].imshow(mask, cmap='gray')
axs[1].set_title('GGO')
axs[2].imshow(masks_medseg[idx, :, :, 1],
cmap='gray')
axs[2].set_title('Consolidations')
plt.show()
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Random CT Image
Visualization(Radiopedia)
# Visualizing the data
# Plot an example image and its corre
sponding mask
idx = 30 # index of the image you w
ant to visualize
image = images_radiopedia[idx, :, :,
0]
mask = masks_radiopedia[idx, :, :, 1]
fig, axs = plt.subplots(1, 3, figsize=(2
0, 10))
axs[0].imshow(image, cmap='gray')
axs[0].set_title('Image')
axs[1].imshow(masks_radiopedia[idx
, :, :, 0], cmap='gray')
axs[1].set_title('GGO')
axs[2].imshow(mask, cmap='gray')
axs[2].set_title('Consolidations')
plt.show()
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Normalization & Splitting Data
# Normalize images to [0, 1] range
images_medseg = (images_medseg -
np.min(images_medseg)) / (np.max(images_medseg) -
np.min(images_medseg))
images_radiopedia = (images_radiopedia -
np.min(images_radiopedia)) / (np.max(images_radiopedia) -
np.min(images_radiopedia))
test_images_medseg = (test_images_medseg -
np.min(test_images_medseg)) / (np.max(test_images_medseg) -
np.min(test_images_medseg))
# Split the data into training, validation, and testing sets
x_train = images_radiopedia
y_train = masks_radiopedia
x_val = images_medseg
y_val = masks_medseg
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Model
Creation(U-Net)
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Model Compile and
Evaluation
# Model Compile
model.compile(optimizer='adam', loss='binary_cr
ossentropy', metrics=['accuracy'])
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# Model Evaluation
history = unet_model.fit(x_train, y_trai
n, batch_size=32, epochs=10, validatio
n_data=(x_val, y_val))
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Conclusion
• In computer vision, image
segmentation is a very beneficial
process that may be used in a range
of use-cases, including medical
imaging and driverless cars, to
collect various segments or classes
in real-time. One can now
experiment with utilizing U-Net to
implement image segmentation on
various challenges or by
investigating other models that are
helpful in image segmentation.
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Thank you
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