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icc update
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@skoudoro
skoudoro committed Aug 7, 2023
1 parent 3b3acf1 commit e842aec
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Showing 2 changed files with 50 additions and 38 deletions.
85 changes: 48 additions & 37 deletions quantconn/cli.py
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Original file line number Diff line number Diff line change
Expand Up @@ -60,7 +60,8 @@ def process(db_path: Annotated[Path, typer.Option("--db-path", "-db",
print_input_info(db_path, destination)
subjects = get_valid_subjects(db_path, subject)

for sub in subjects:
total_subjects = len(subjects)
for nb_sub, sub in enumerate(subjects, start=1):
t1_path = pjoin(db_path, sub, "anat", f"{sub}_T1w.nii.gz")
# t1_label_path = pjoin(db_path, sub, "anat", "aparc+aseg.nii.gz")
t1_label_path = pjoin(db_path, sub, "anat", "atlas_freesurfer_inT1space.nii.gz")
Expand All @@ -82,7 +83,7 @@ def process(db_path: Annotated[Path, typer.Option("--db-path", "-db",
os.makedirs(output_path)

try:
print(f"[bold blue]Processing [green]{sub} / {mod}[/green] acquisition [/bold blue]")
print(f"[bold blue] ({nb_sub}/{total_subjects}) Processing [green]{sub} / {mod}[/green] acquisition [/bold blue]")
process_data(pjoin(data_folder, "dwi.nii.gz"),
pjoin(data_folder, "dwi.bval"),
pjoin(data_folder, "dwi.bvec"),
Expand Down Expand Up @@ -121,9 +122,11 @@ def evaluate(db_path: Annotated[Path, typer.Option("--db-path", "-db",

_, all_bundles_files = get_30_bundles_atlas_hcp842()

for sub in subjects:
total_subjects = len(subjects)

for nb_sub, sub in enumerate(subjects, start=1):
# t1_path = pjoin(db_path, "anat", f"{sub}_T1w.nii.gz")
print(f"[bold blue]Evaluating [green]{sub}[/green] subject [/bold blue]")
print(f"[bold blue]({nb_sub}/{total_subjects}) Evaluating [green]{sub}[/green] subject [/bold blue]")
selected_bundles = ['AF_R', 'AF_L', 'CST_L', 'CST_R', 'OR_L', 'OR_R']
output_path = pjoin(destination, sub, 'metrics')
if not os.path.exists(output_path):
Expand Down Expand Up @@ -232,45 +235,53 @@ def merge(destination: Annotated[Path, typer.Option("--destination", "-dest",
np.savetxt(_merging_results_path, np.asarray(_merging_results),
delimiter=',', header=','.join(headers), fmt='%s')

results_conn = pg.intraclass_corr(data=df_conn, targets='# subject',
raters='metric', ratings='score')
results_conn = results_conn.set_index('Description')
icc_con = results_conn.loc['Average raters absolute', 'ICC']
print(f"Connectivity score : {icc_con.round(3)}")

data = pd.read_csv(_merging_results_path)
df_mm = pd.DataFrame(columns=['# subject', 'metric', 'score'])

for i in range(len(data)):
for mt in headers[3:]:
df_mm_2 = pd.DataFrame({'# subject': [data['# subject'][i]],
'group': [data['group'][i]],
'metric': [mt],
'score': [data[mt][i]]})
df_mm = pd.concat([df_mm, df_mm_2])

df_mm.to_csv(pjoin(destination, '_microstructural_measures_scores.csv'))

results_mm = pg.intraclass_corr(data=df_mm, targets='# subject',
raters='metric', ratings='score')
results_mm = results_mm.set_index('Description')
icc_mm = results_mm.loc['Average raters absolute', 'ICC']
print(f"Microstructural measures score : {icc_mm.round(3)}")

results_ss = pg.intraclass_corr(data=df_ss, targets='# subject',
raters='metric', ratings='score')
results_ss = results_ss.set_index('Description')
icc_ss = results_ss.loc['Average raters absolute', 'ICC']
print(f"Shape Similarity score : {icc_ss.round(3)}")
# Start Computing ICC
results_conn = []
for metric in ['betweenness_centrality', 'global_efficiency',
'modularity']:
df_tmp = df_conn[df_conn['metric'] == metric]
icc_conn = pg.intraclass_corr(data=df_tmp, targets='# subject',
raters='group', ratings='score')
icc_conn.set_index('Type')
results_conn.append(float(icc_conn.loc[icc_conn['Type'] == 'ICC1',
'ICC']))

print(f"Connectivity all scores : {results_conn}")
print(f"Connectivity final score : {np.asarray(results_conn).mean()}")

df_mm = pd.read_csv(_merging_results_path)
results_mm = []
for bundle_metric in headers[2:]:
icc_mm = pg.intraclass_corr(data=df_mm, targets='# subject',
raters='group', ratings=bundle_metric)

icc_mm.set_index('Type')

results_mm.append(float(icc_mm.loc[icc_mm['Type'] == 'ICC1', 'ICC']))

with open(pjoin(destination, '_bundle_metrics_icc_report.csv'), 'w') as fh:
writer = csv.writer(fh, delimiter=',')
writer.writerow(headers[2:])
writer.writerow(results_mm)
print(f"Microstructural measures all scores : {results_mm}")
print(f"Microstructural measures final score : {np.asarray(results_mm).mean()}")

results_ss = []
for metric in ['shape_similarity', 'shape_profile']:
df_tmp = df_ss[df_ss['metric'] == metric]
results_ss.append(df_tmp.score.mean())

print(f"Shape Similarity all scores : {results_ss}")
print(f"Shape Similarity final score : {np.asarray(results_ss).mean()}")

# Save results
with open(pjoin(destination, '_final_sore.csv'), 'w') as fh:
writer = csv.writer(fh, delimiter=',')
writer.writerow(['Connectivity score', 'Microstructural measures',
'Shape Similarity Score'])
writer.writerow([float(icc_con.round(3)),
float(icc_mm.round(3)),
float(icc_ss.round(3))])
writer.writerow([float(np.asarray(results_conn).mean()),
float(np.asarray(results_mm).mean()),
float(np.asarray(results_ss).mean())])

print(":green_circle: [bold green]Success ! :love-you_gesture: [/bold green]")

Expand Down
3 changes: 2 additions & 1 deletion quantconn/process.py
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Original file line number Diff line number Diff line change
Expand Up @@ -27,6 +27,7 @@
from dipy.tracking import utils
from dipy.io.stateful_tractogram import Space, StatefulTractogram
from dipy.io.streamline import save_trk, load_trk
from dipy.tracking.metrics import length
from dipy.tracking.local_tracking import LocalTracking
from dipy.tracking.streamline import Streamlines, transform_streamlines
from dipy.segment.mask import median_otsu
Expand Down Expand Up @@ -127,11 +128,11 @@ def process_data(nifti_fname, bval_fname, bvec_fname, t1_fname, output_path,

# remove short streamlines
print(':left_arrow_curving_right: Remove short streamlines (< 30mm)')
from dipy.tracking.metrics import length
streamlines_generator = (s for s in streamlines_generator
if length(s) > 30.0)
target_streamlines = Streamlines(streamlines_generator)

print(':left_arrow_curving_right: Saving tractogram')
header = create_tractogram_header(TrkFile, resliced_affine,
maskdata.shape[:3],
new_vox_size,
Expand Down

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