File size: 2,996 Bytes
3d03c44 cc6d035 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 | 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)
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