import gradio as gr import subprocess as sp from skimage.measure import marching_cubes import nibabel as nib from nibabel.processing import resample_to_output def nifti_to_glb(path): # load NIFTI into numpy array image = nib.load(path) resampled = resample_to_output(image, [1, 1, 1], order=1) data = resampled.get_fdata().astype("uint8") # extract surface verts, faces, normals, values = marching_cubes(data, 0) faces += 1 with open('prediction.obj', 'w') as thefile: for item in verts: thefile.write("v {0} {1} {2}\n".format(item[0],item[1],item[2])) for item in normals: thefile.write("vn {0} {1} {2}\n".format(item[0],item[1],item[2])) for item in faces: thefile.write("f {0}//{0} {1}//{1} {2}//{2}\n".format(item[0],item[1],item[2])) def run_model(input_path): sp.check_call(["livermask", "--input", input_path, "--output", "prediction", "--verbose"]) def load_mesh(mesh_file_name): path = mesh_file_name.name run_model(path) nifti_to_glb("prediction-livermask.nii") return "./prediction.obj" if __name__ == "__main__": demo = gr.Interface( fn=load_mesh, inputs=gr.UploadButton(label="Click to Upload a File", file_types=[".nii", ".nii.nz"], file_count="single"), outputs=gr.Model3D(clear_color=[0.0, 0.0, 0.0, 0.0], label="3D Model"), title="livermask: Automatic Liver Parenchyma segmentation in CT", description="Using pretrained deep learning model trained on the LiTS17 dataset", ) demo.launch()