Use atlases

🐾 Usage of eye atlases

💫My question

I want to use that for segment specific area for calculating SNR, CNR... Now I have template.nii.gz: atlas of the eye images (per sex), and max_prob_map.npy and max_prob_map.nii.gz: maximum probability maps (per sex), I also have my 3d image in pkl format. How can I do the registration and mask out the region I need?

💥Answer from ChatGPT

To use the eye atlas and probability maps for segmenting specific regions and calculating metrics like SNR and CNR, you'll need to go through several steps, which include image registration, applying masks, and performing the desired calculations. Here’s a general outline of the process:

1. Prepare the Data

  • You have the following:

    • template.nii.gz: A template atlas (likely a brain or eye atlas).
    • max_prob_map.npy or max_prob_map.nii.gz: Maximum probability maps for each sex.
    • 3D image in PKL format: This is your raw 3D image data.

  • Objective: Register the 3D image to the template atlas, use the probability maps to mask the regions of interest, and calculate metrics like SNR (Signal-to-Noise Ratio) and CNR (Contrast-to-Noise Ratio) on those regions.

2. Convert PKL to NIfTI (if needed)

If your 3D image is stored in a PKL file (Pickle format), you'll first need to load the PKL file and convert it to a NIfTI file (.nii.gz), which is a more common format for medical imaging tasks.

Here's how you can do that in Python:

import pickle
import nibabel as nib
import numpy as np

# Load your PKL file
with open('your_image.pkl', 'rb') as f:
    image_data = pickle.load(f)

# Convert the image data to a NIfTI format
image_nifti = nib.Nifti1Image(image_data, affine=np.eye(4))

# Save the NIfTI file
nib.save(image_nifti, 'your_image.nii.gz')

This will give you a NIfTI file of your image.

3. Register the 3D Image to the Template Atlas

To align your 3D image with the template.nii.gz (the atlas), you can use image registration. A popular tool for this is FSL (flirt or fnirt) or ANTs (Advanced Normalization Tools).

Here’s how you can use FSL to register the images:

FSL Registration Example:

# Using FSL's flirt for linear registration
flirt -in your_image.nii.gz -ref template.nii.gz -out registered_image.nii.gz -omat transform.mat

This command will perform a linear registration of your image to the template and save the output as registered_image.nii.gz along with the transformation matrix transform.mat.

If you need non-linear registration (e.g., to handle more complex deformations), you can use fnirt:

# Using FSL's fnirt for non-linear registration
fnirt --in=your_image.nii.gz --ref=template.nii.gz --out=registered_image_nl.nii.gz --aff=transform.mat

After registration, your 3D image will be aligned to the template atlas.

4. Masking the Desired Region Using Probability Maps

You can use the maximum probability map (max_prob_map.nii.gz) to identify and mask specific regions of interest in the registered image. The probability map indicates the likelihood of a certain structure or region in the atlas.

To extract and mask specific regions based on the probability map:

Using nibabel and numpy:

import nibabel as nib
import numpy as np

# Load the registered 3D image and the probability map
registered_image = nib.load('registered_image.nii.gz')
prob_map = nib.load('max_prob_map.nii.gz')

# Get the image data (as numpy arrays)
image_data = registered_image.get_fdata()
prob_map_data = prob_map.get_fdata()

# Define a threshold (e.g., 50% probability) for masking
threshold = 0.5
mask = prob_map_data > threshold

# Apply the mask to the registered image
masked_image = image_data * mask

# Save the masked image
masked_image_nifti = nib.Nifti1Image(masked_image, registered_image.affine)
nib.save(masked_image_nifti, 'masked_image.nii.gz')

This will create a masked image (masked_image.nii.gz) where the regions of interest, as identified by the probability map, are kept and everything else is set to zero.

5. Compute SNR and CNR

After masking the desired regions, you can now compute SNR and CNR using the masked regions. The formulas for these metrics are:

  • SNR: \text{SNR} = \frac{\mu_{\text{signal}}}{\sigma_{\text{noise}}}
  • \mu_{\text{signal}} is the mean signal intensity in the region of interest.

  • \sigma_{\text{noise}} is the standard deviation of the signal in the background or noise region.

  • CNR: \text{CNR} = \frac{\mu_{\text{signal}} - \mu_{\text{background}}}{\sigma_{\text{background}}}

  • \mu_{\text{signal}} is the mean intensity of the region of interest.
  • \mu_{\text{background}} is the mean intensity of the background.
  • \sigma_{\text{background}} is the standard deviation of the background.

Example Calculation in Python:

# Define the background region (e.g., outside the mask)
background_mask = ~mask

# Calculate SNR for the masked region
signal_region = image_data[mask]
noise_region = image_data[background_mask]

mean_signal = np.mean(signal_region)
std_noise = np.std(noise_region)

SNR = mean_signal / std_noise
print(f"SNR: {SNR}")

# Calculate CNR (assuming background region is well-defined)
mean_background = np.mean(noise_region)
std_background = np.std(noise_region)

CNR = (mean_signal - mean_background) / std_background
print(f"CNR: {CNR}")

6. Summary of Steps:

  1. Convert your PKL file to NIfTI format.
  2. Perform image registration to align your 3D image with the template atlas.
  3. Use the probability maps to create a mask of the region of interest.
  4. Apply the mask to the registered image to isolate the region of interest.
  5. Calculate SNR and CNR using the masked regions and background regions.

Additional Notes:

  • Thresholding the Probability Map: You can adjust the threshold value based on the specificity of the regions you want to mask.
  • Post-processing: You may want to smooth the images or apply other filters depending on your analysis.