Extract patch centers for patients without aneurysm

deepaneseg/data/io.py

@@ -253,8 +253,7 @@ def extract_points_from_patient(patient, r=20, nb_points=100, outfile="points.cs
         d = json.load(f)
     vol, vox2met = read_nii_from_file(os.path.join(patient, d["noskull volume"]))
     print("Extracting points")
-    forbidden_points = read_points_from_csv(os.path.join(patient, d["pts aneurysm"]))
-    fp = points_to_spheres(forbidden_points)[:, :-1]  # drop radii
+    fp = read_aneurysm_spheres(patient)[:, :3]  # aneurysm centers, none for a patient without aneurysm
     q = None
     if random_points:
         min_threshold, max_threshold = np.percentile(vol[vol > 0], 0), np.percentile(vol[vol > 0], 100)

deepaneseg/volume/selection.py

@@ -74,7 +74,7 @@ def select_points(
     removed = np.zeros(len(v), dtype=bool)
     tree = skn.KDTree(p)
 
-    if not forbidden_points is None:
+    if forbidden_points is not None and len(forbidden_points):
         if len(forbidden_points.shape) == 1:
             forbidden_points = forbidden_points[np.newaxis, :]
         for i in tree.query_radius(forbidden_points, r):

tests/test_healthy_patients.py

@@ -45,3 +45,21 @@ def test_adam_evaluation_on_healthy_patient():
     tp, fp, _, _ = adam_evaluation(pred, np.eye(4), NO_SPHERES)
 
     assert (tp, fp) == (0, 1)
+
+
+def test_points_are_extracted_for_a_patient_without_aneurysm(tmp_path):
+    import json
+
+    import nibabel as ni
+    import pandas as pd
+
+    from deepaneseg.data.io import extract_points_from_patient
+
+    vol = np.random.default_rng(0).random((40, 40, 40)).astype(np.float32)
+    ni.save(ni.Nifti1Image(vol, np.diag([0.5, 0.5, 0.5, 1.0])), tmp_path / "noskull.nii.gz")
+    (tmp_path / "config.json").write_text(json.dumps({"noskull volume": "noskull.nii.gz"}))
+
+    extract_points_from_patient(str(tmp_path), r=4, nb_points=5)
+
+    points = pd.read_csv(tmp_path / "points.csv")
+    assert set(points["type"]) == {"Vessel", "Parenchyma"} and len(points) == 10