SimpleITK

Step 1: Load pre-treatment and post-treatment MRI images using sitk.ReadImage.

Step 2: Define a registration method (e.g., rigid registration).

Step 3: Set the initial transform and optimization parameters.

Step 4: Execute the registration using sitk.ImageRegistrationMethod().

Step 5: Apply the resulting transform to the post-treatment image.

Step 6: Visualize the aligned images to assess registration quality.

Step 1: Load the CT scan using sitk.ReadImage.

Step 2: Apply image preprocessing steps like smoothing and intensity normalization.

Step 3: Use thresholding and morphological operations to create an initial lung mask.

Step 4: Refine the segmentation using region growing or level set methods.

Step 5: Evaluate the segmentation accuracy using metrics like Dice coefficient.

Step 6: Save the lung segmentation mask for further analysis.

Step 1: Load the brain MRI scan using sitk.ReadImage.

Step 2: Apply bias field correction to reduce intensity inhomogeneities.

Step 3: Use a k-means clustering or expectation-maximization algorithm to segment the brain into different tissue types.

Step 4: Refine the segmentation using morphological operations and anatomical priors.

Step 5: Evaluate the segmentation accuracy using metrics compared to a manual segmentation.

Step 6: Visualize and analyze the segmented tissue volumes.

Step 1: Load the microscopy image using sitk.ReadImage.

Step 2: Apply image enhancement techniques to improve cell visibility.

Step 3: Use a blob detection algorithm to identify individual cells.

Step 4: Filter the detected blobs based on size and shape to remove false positives.

Step 5: Count the number of remaining blobs to estimate the cell count.

Step 6: Output the cell count and visualize the detected cells overlaid on the original image.

Step 1: Load the medical image (e.g., MRI or CT) using sitk.ReadImage.

Step 2: Segment the tumor using manual or automated segmentation techniques.

Step 3: Convert the segmentation mask to a binary image.

Step 4: Calculate the volume of the tumor by multiplying the number of tumor voxels by the voxel volume.

Step 5: Report the tumor volume in cubic millimeters or centimeters.

Step 6: Track tumor volume changes over time to monitor treatment response.