MedImageParse3D

import base64
data = {
    "input_data": {
        "columns": [ "image", "text" ],
        "index": [ 0 ],
        "data": [
            [
                base64_image,
                "CT imaging of the spleen within the abdomen & Presence of the right kidney detected in abdominal CT images & Abdominal CT showing the left kidney & CT scan of the gallbladder in the abdominal region & CT scan of the esophagus in the abdominal region & Visualization of the liver in abdominal CT imaging & CT imaging of the stomach in the abdomen & Abdominal CT showing aortic structures & Inferior vena cava in abdominal CT & CT scan of the pancreas in the abdominal region & CT imaging of the right adrenal gland in the abdomen & CT imaging of the left adrenal gland in the abdomen & Visualization of the duodenum in abdominal CT imaging & Bladder observed in abdominal CT scans & CT scan of the prostate/uterus in the abdominal region"

            ]
        ]
    }
}

[
  {
    "nifti_file": "{\"data\":\"<BASE64_ENCODED_BYTES>\"}"
  }
]

import json
import base64
import tempfile
import nibabel as nib

def decode_base64_to_nifti(base64_string: str) -> nib.Nifti1Image:
    """
    Decode a Base64 string back to a NIfTI image.
  
    The function expects `base64_string` to be a JSON string
    of the form: '{"data": "<Base64EncodedBytes>"}'.
    """
    # Convert the 'nifti_file' string to a Python dict, then extract the 'data' field
    base64_string = json.loads(base64_string)["data"]
  
    # Decode Base64 string to raw bytes
    byte_data = base64.b64decode(base64_string)
  
    # Write these bytes to a temporary file and load as a NIfTI image
    with tempfile.NamedTemporaryFile(suffix='.nii.gz', delete=False) as temp_file:
        temp_file.write(byte_data)
        nifti_image = nib.load(temp_file.name)
  
    # Return the voxel data as a Numpy array
    return nifti_image.get_fdata()

import json

# Suppose `response` is the raw byte response from urllib
response_data = json.loads(response)

# Extract the JSON-stringified NIfTI
nifti_file_str = response_data[0]["nifti_file"]

# Decode to get the NIfTI volume as a Numpy array
segmentation_array = decode_base64_to_nifti(nifti_file_str)
print(segmentation_array.shape)  

# --- Quick visualization of one axial slice ---
import matplotlib.pyplot as plt
import numpy as np

slice_id = 40  # choose an in-bounds slice index along the third axis (H, W, Z)
slice_ = segmentation_array[:, :, slice_id]

# Exclude background (0) when listing labels
labels = np.unique(slice_)
labels = labels[labels != 0]

plt.figure(figsize=(6, 6))
plt.imshow(slice_, cmap="gray")
plt.title(f"Masks @ slice {slice_id} | labels: {labels.tolist()}")
plt.axis("off")
plt.show()