| import ants |
| import matplotlib |
| matplotlib.use("Agg") |
| import matplotlib.pyplot as plt |
| import numpy as np |
| import gradio as gr |
|
|
|
|
| def perform_registration(reference_image_path, input_image_path): |
| fixed_image = ants.reorient_image2(ants.image_read(reference_image_path), "LIA") |
| moving_image = ants.reorient_image2(ants.image_read(input_image_path), "LIA") |
|
|
| fixed_image_nn = ants.resample_image(fixed_image, (0.2, 0.2, 0.2), False, 0) |
| moving_image_nn = ants.resample_image(moving_image, (0.2, 0.2, 0.2), False, 0) |
|
|
| fixed_image_n3 = ants.n3_bias_field_correction(fixed_image_nn) |
| moving_image_n3 = ants.n3_bias_field_correction(moving_image_nn) |
|
|
| affine = ants.registration(fixed_image_n3, moving_image_n3, "Affine") |
| affine_registered = ants.apply_transforms( |
| fixed=fixed_image_n3, moving=moving_image_n3, |
| transformlist=affine["fwdtransforms"]) |
|
|
| syn = ants.registration(fixed_image_n3, affine_registered, "SyN") |
| final_registered = ants.apply_transforms( |
| fixed=fixed_image_n3, moving=affine_registered, |
| transformlist=syn["fwdtransforms"]) |
|
|
| registered_image_path = "registered_image.nii.gz" |
| ants.image_write(final_registered, registered_image_path) |
|
|
| fixed_arr = fixed_image_n3.numpy() |
| reg_arr = final_registered.numpy() |
| sagittal = fixed_arr.shape[2] // 2 |
| rotated_fixed = np.rot90(fixed_arr[:, :, sagittal], k=1) |
| rotated_reg = np.rot90(reg_arr[:, :, sagittal], k=1) |
| return rotated_fixed.astype(np.float32), rotated_reg.astype(np.float32), registered_image_path |
|
|
|
|
| def registration_app(reference_file, input_file): |
| fixed, registered, registered_image_path = perform_registration(reference_file, input_file) |
|
|
| plt.figure(figsize=(6, 6)) |
| plt.subplot(1, 2, 1) |
| plt.imshow(fixed, cmap="gray") |
| plt.title("Reference Image") |
| plt.axis("off") |
| plt.subplot(1, 2, 2) |
| plt.imshow(registered, cmap="gray") |
| plt.title("Post-Registered Image") |
| plt.axis("off") |
| plt.tight_layout() |
| plt.savefig("visualization.png") |
| plt.close() |
| return "visualization.png", registered_image_path |
|
|
|
|
| title = "EasyReg v1: Easy to Use Non-linear MRI Alignment for Rodent Brain Images" |
| description = ( |
| "How to Use: 1. Drag and drop the Reference MRI (reference image). " |
| "2. Drag and drop the Input MRI (image to be aligned). 3. Click 'Submit'. " |
| "4. Based on typical rodent MRI standards, the procedure will be completed in " |
| "approximately 2 minutes. Please view the resulting image displayed on the right " |
| "and download the result file below." |
| ) |
|
|
| demo = gr.Interface( |
| fn=registration_app, |
| inputs=[ |
| gr.File(label="Reference MRI", type="filepath"), |
| gr.File(label="Input MRI", type="filepath"), |
| ], |
| outputs=[ |
| gr.Image(type="filepath", label="Image"), |
| gr.File(label="Save"), |
| ], |
| title=title, |
| description=description, |
| ) |
|
|
| if __name__ == "__main__": |
| demo.launch(server_name="0.0.0.0", server_port=7860, show_api=False) |
|
|
