Source code for spinalcordtoolbox.vertebrae.detect_c2c3

# coding: utf-8
# This is the interface API to detect the posterior edge of C2-C3 disc.
# The models have been trained as explained in (Gros et al. 2018, MIA,,
# in section 2.1.2, except that the cords are not straightened for the C2-C3 disc detection task.
# To train a new model:
# - Edit "$SCT_DIR/dev/detect_c2c3/" according to your needs, then save the file.
# - Run "source sct_launcher" in a terminal
# - Run the script "$SCT_DIR/dev/detect_c2c3/"
# To use this model when running the module "detect_c2c3" (herebelow) and "sct_label_vertebrae":
# - Save the trained model in "$SCT_DIR/data/c2c3_disc_models/"
# Author: charley
# Copyright (c) 2018 Polytechnique Montreal <>
# About the license: see the file LICENSE.TXT

import os
import logging

import numpy as np
import nibabel as nib
from scipy.ndimage.measurements import center_of_mass

from spinalcordtoolbox.image import Image, zeros_like
from spinalcordtoolbox.utils import run_proc, TempFolder, __data_dir__
from spinalcordtoolbox.flattening import flatten_sagittal

logger = logging.getLogger(__name__)

[docs]def detect_c2c3(nii_im, nii_seg, contrast, nb_sag_avg=7.0, verbose=1): """ Detect the posterior edge of C2-C3 disc. :param nii_im: :param nii_seg: :param contrast: :param verbose: :return: """ # path to the pmj detector path_model = os.path.join(__data_dir__, 'c2c3_disc_models', '{}_model'.format(contrast)) # check if model exists if not os.path.isfile(path_model+'.yml'): raise FileNotFoundError( "The model file {} does not exist. Please download it using sct_download_data".format(path_model+'.yml')) orientation_init = nii_im.orientation z_seg_max = np.max(np.where(nii_seg.change_orientation('PIR').data)[1]) # Flatten sagittal nii_im = flatten_sagittal(nii_im, nii_seg,verbose=verbose) nii_seg_flat = flatten_sagittal(nii_seg, nii_seg, verbose=verbose) # create temporary folder with intermediate results"Creating temporary folder...") tmp_folder = TempFolder() tmp_folder.chdir() # Extract mid-slice nii_im.change_orientation('PIR') nii_seg_flat.change_orientation('PIR') mid_RL = int(np.rint(nii_im.dim[2] * 1.0 / 2)) nb_sag_avg_half = int(nb_sag_avg / 2 / nii_im.dim[6]) midSlice = np.mean([:, :, mid_RL-nb_sag_avg_half:mid_RL+nb_sag_avg_half+1], 2) # average 7 slices midSlice_seg =[:, :, mid_RL] nii_midSlice = zeros_like(nii_im) = midSlice'data_midSlice.nii') # Run detection'Run C2-C3 detector...') os.environ["FSLOUTPUTTYPE"] = "NIFTI_PAIR" cmd_detection = 'isct_spine_detect -ctype=dpdt "%s" "%s" "%s"' % \ (path_model, 'data_midSlice', 'data_midSlice_pred') # The command below will fail, but we don't care because it will output an image (prediction), which we # will use later on. s, o = run_proc(cmd_detection, verbose=0, is_sct_binary=True, raise_exception=False) pred = nib.load('data_midSlice_pred_svm.hdr').get_data() if verbose >= 2: # copy the "prediction data before post-processing" in an Image object nii_pred_before_postPro = nii_midSlice.copy() = pred # 2D data with orientation, mid sag slice of the original data"pred_midSlice_before_postPro.nii.gz") # save it) # DEBUG trick: check if the detection succeed by running: fsleyes data_midSlice data_midSlice_pred_svm -cm red -dr 0 100 # If a "red cluster" is observed in the neighbourhood of C2C3, then the model detected it. # Create mask along centerline midSlice_mask = np.zeros(midSlice_seg.shape) mask_halfSize = int(np.rint(25.0 / nii_midSlice.dim[4])) for z in range(midSlice_mask.shape[1]): row = midSlice_seg[:, z] # 2D data with PI orientation, mid sag slice of the original data if np.any(row > 0): med_y = int(np.rint(np.median(np.where(row > 0)))) midSlice_mask[med_y-mask_halfSize:med_y+mask_halfSize, z] = 1 # 2D data with PI orientation, mid sag slice of the original data if verbose >= 2: # copy the created mask in an Image object nii_postPro_mask = nii_midSlice.copy() = midSlice_mask # 2D data with PI orientation, mid sag slice of the original data"mask_midSlice.nii.gz") # save it # mask prediction pred[midSlice_mask == 0] = 0 pred[:, z_seg_max:] = 0 # Mask above SC segmentation if verbose >= 2: # copy the "prediction data after post-processing" in an Image object nii_pred_after_postPro = nii_midSlice.copy() = pred"pred_midSlice_after_postPro.nii.gz") # save it # assign label to voxel nii_c2c3 = zeros_like(nii_seg_flat) # 3D data with PIR orientaion if np.any(pred > 0):'C2-C3 detected...') pred_bin = (pred > 0).astype(np.int_) coord_max = np.where(pred == np.max(pred)) pa_c2c3, is_c2c3 = coord_max[0][0], coord_max[1][0] nii_seg.change_orientation('PIR') rl_c2c3 = int(np.rint(center_of_mass(np.array([:, is_c2c3, :]))[1]))[pa_c2c3, is_c2c3, rl_c2c3] = 3 else: logger.warning('C2-C3 not detected...') # remove temporary files tmp_folder.chdir_undo() if verbose < 2:"Remove temporary files...") tmp_folder.cleanup() else:"Temporary files saved to "+tmp_folder.get_path()) nii_c2c3.change_orientation(orientation_init) return nii_c2c3
[docs]def detect_c2c3_from_file(fname_im, fname_seg, contrast, fname_c2c3=None, verbose=1): """ Detect the posterior edge of C2-C3 disc. :param fname_im: :param fname_seg: :param contrast: :param fname_c2c3: :param verbose: :return: fname_c2c3 """ # load data'Load data...') nii_im = Image(fname_im) nii_seg = Image(fname_seg) # detect C2-C3 nii_c2c3 = detect_c2c3(nii_im.copy(), nii_seg, contrast, verbose=verbose) # Output C2-C3 disc label # by default, output in the same directory as the input images'Generate output file...') if fname_c2c3 is None: fname_c2c3 = os.path.join(os.path.dirname(nii_im.absolutepath), "label_c2c3.nii.gz") return fname_c2c3