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fix(metrics): return the minimum for Hausdorff percentile=0 #9033

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fix(metrics): return the minimum for Hausdorff percentile=0 #9033

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5 changes: 4 additions & 1 deletion monai/metrics/hausdorff_distance.py
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Original file line number Diff line number Diff line change
Expand Up @@ -204,9 +204,12 @@ def _compute_percentile_hausdorff_distance(
if surface_distance.shape == (0,):
return torch.tensor(np.nan, dtype=torch.float, device=surface_distance.device)

if not percentile:
if percentile is None:
return surface_distance.max()

if percentile == 0:
return surface_distance.min()

if 0 <= percentile <= 100:
return torch.quantile(surface_distance, percentile / 100)
raise ValueError(f"percentile should be a value between 0 and 100, get {percentile}.")
41 changes: 41 additions & 0 deletions tests/metrics/test_hausdorff_distance.py
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Original file line number Diff line number Diff line change
Expand Up @@ -19,6 +19,7 @@
from parameterized import parameterized

from monai.metrics import HausdorffDistanceMetric
from monai.metrics.hausdorff_distance import _compute_percentile_hausdorff_distance

_devices = ["cpu"]
if torch.cuda.is_available():
Expand Down Expand Up @@ -116,6 +117,25 @@ def create_spherical_seg_3d(
],
[19.924858845171276, 20.09975124224178, 14, 18, 22, 33],
],
[
[
# percentile=0 is the 0th-percentile (minimum) surface distance, not the max
create_spherical_seg_3d(radius=20, centre=(20, 20, 20)),
create_spherical_seg_3d(radius=20, centre=(19, 19, 19)),
None,
0,
],
[0, 0, 0, 0, 0, 0],
],
[
[
create_spherical_seg_3d(radius=15, centre=(20, 33, 22), im_spacing=test_spacing),
create_spherical_seg_3d(radius=30, centre=(20, 33, 22), im_spacing=test_spacing),
test_spacing,
0,
],
[5.099999904632568, 5.099999904632568, 6, 6, 6, 6],
],
[
[
create_spherical_seg_3d(radius=20, centre=(20, 20, 20), im_spacing=test_spacing),
Expand Down Expand Up @@ -167,6 +187,15 @@ def _describe_test_case(test_func, test_number, params):
return f"device: {_device} metric: {metric} directed:{directed} expected: {test_output}"


TEST_CASES_PERCENTILE = [
[[0.0, 3.0], None, 3.0],
[[0.0, 3.0], 0, 0.0],
[[np.inf, np.inf, np.inf], 0, np.inf],
[[1.0, 2.0, 3.0], 50, 2.0],
[[], 0, np.nan],
]


class TestHausdorffDistance(unittest.TestCase):

@parameterized.expand(TEST_CASES_EXPANDED, doc_func=_describe_test_case)
Expand Down Expand Up @@ -204,6 +233,18 @@ def test_nans(self, input_data):
np.testing.assert_allclose(0, result, rtol=1e-7)
np.testing.assert_allclose(0, not_nans, rtol=1e-7)

@parameterized.expand(TEST_CASES_PERCENTILE)
def test_percentile(self, surface_distances, percentile, expected_value):
surface_distance = torch.tensor(surface_distances, dtype=torch.float)
result = _compute_percentile_hausdorff_distance(surface_distance, percentile)
np.testing.assert_allclose(expected_value, result, rtol=1e-7)

def test_percentile_out_of_range(self):
for percentile in [-1, 101]:
with self.subTest(percentile=percentile):
with self.assertRaises(ValueError):
_compute_percentile_hausdorff_distance(torch.tensor([1.0, 2.0, 3.0]), percentile)


if __name__ == "__main__":
unittest.main()
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