Merge pull request #52 from BrainLesion/38-rename-to-sq_assd

new: assd renaming

panoptica/result.py

@@ -54,7 +54,7 @@ def __str__(self):
             f"Panoptic Quality (PQ): {self.pq}\n"
             f"DSC-based Segmentation Quality (DQ_DSC): {self.sq_dsc} ± {self.sq_dsc_sd}\n"
             f"DSC-based Panoptic Quality (PQ_DSC): {self.pq_dsc}\n"
-            f"Average symmetric surface distance (ASSD): {self.instance_assd} ± {self.instance_assd_sd}"
+            f"Average symmetric surface distance (ASSD): {self.sq_assd} ± {self.sq_assd_sd}"
         )
 
     def to_dict(self):
@@ -71,8 +71,8 @@ def to_dict(self):
             "sq_dsc": self.sq_dsc,
             "sq_dsc_sd": self.sq_dsc_sd,
             "pq_dsc": self.pq_dsc,
-            "assd": self.instance_assd,
-            "assd_sd": self.instance_assd_sd,
+            "sq_assd": self.sq_assd,
+            "sq_assd_sd": self.sq_assd_sd,
         }
 
     @property
@@ -134,7 +134,9 @@ def rq(self) -> float:
             float: Recognition Quality (RQ).
         """
         if self.tp == 0:
-            return 0.0 if self.num_pred_instances + self.num_ref_instances > 0 else np.nan
+            return (
+                0.0 if self.num_pred_instances + self.num_ref_instances > 0 else np.nan
+            )
         return self.tp / (self.tp + 0.5 * self.fp + 0.5 * self.fn)
 
     @property
@@ -146,7 +148,9 @@ def sq(self) -> float:
             float: Segmentation Quality (SQ).
         """
         if self.tp == 0:
-            return 0.0 if self.num_pred_instances + self.num_ref_instances > 0 else np.nan
+            return (
+                0.0 if self.num_pred_instances + self.num_ref_instances > 0 else np.nan
+            )
         return np.sum(self._iou_list) / self.tp
 
     @property
@@ -178,7 +182,9 @@ def sq_dsc(self) -> float:
             float: Average Dice coefficient.
         """
         if self.tp == 0:
-            return 0.0 if self.num_pred_instances + self.num_ref_instances > 0 else np.nan
+            return (
+                0.0 if self.num_pred_instances + self.num_ref_instances > 0 else np.nan
+            )
         return np.sum(self._dice_list) / self.tp
 
     @property
@@ -202,23 +208,36 @@ def pq_dsc(self) -> float:
         return self.sq_dsc * self.rq
 
     @property
-    def instance_assd(self) -> float:
+    def sq_assd(self) -> float:
         """
-        Get the average symmetric surface distance averaged over instances
+        Calculate the average average symmetric surface distance (ASSD) for matched instances. Analogue to segmentation quality but based on ASSD.
 
         Returns:
-            float: average symmetric surface distance.
+            float: average symmetric surface distance. (ASSD)
         """
         if self.tp == 0:
-            return np.nan if self.num_pred_instances + self.num_ref_instances == 0 else np.inf
+            return (
+                np.nan
+                if self.num_pred_instances + self.num_ref_instances == 0
+                else np.inf
+            )
         return np.sum(self._assd_list) / self.tp
 
     @property
-    def instance_assd_sd(self) -> float:
+    def sq_assd_sd(self) -> float:
         """
-        Get the standard deviation of all assd calculations
-
+        Calculate the standard deviation of average symmetric surface distance (ASSD) for matched instances. Analogue to segmentation quality but based on ASSD.
         Returns:
-            float: std of average symmetric surface distance.
+            float: Standard deviation of average symmetric surface distance (ASSD).
         """
         return np.std(self._assd_list) if len(self._assd_list) > 0 else np.nan
+
+    @property
+    def pq_assd(self) -> float:
+        """
+        Calculate the Panoptic Quality (PQ) based on ASSD-based SQ and RQ.
+
+        Returns:
+            float: Panoptic Quality (PQ).
+        """
+        return self.sq_assd * self.rq

unit_tests/test_panoptic_evaluator.py

@@ -4,13 +4,12 @@
 # coverage html
 import unittest
 
-from panoptica.instance_approximator import ConnectedComponentsInstanceApproximator, CCABackend
-from panoptica.instance_matcher import NaiveThresholdMatching
-from panoptica.instance_evaluator import evaluate_matched_instance
 import numpy as np
-from panoptica.result import PanopticaResult
-from panoptica.utils.datatypes import SemanticPair, UnmatchedInstancePair, MatchedInstancePair, _ProcessingPair
+
 from panoptica.evaluator import Panoptic_Evaluator
+from panoptica.instance_approximator import ConnectedComponentsInstanceApproximator
+from panoptica.instance_matcher import NaiveThresholdMatching
+from panoptica.utils.datatypes import SemanticPair
 
 
 class Test_Panoptic_Evaluator(unittest.TestCase):
@@ -56,7 +55,7 @@ def test_pred_empty(self):
         self.assertEqual(result.fn, 1)
         self.assertEqual(result.sq, 0.0)
         self.assertEqual(result.pq, 0.0)
-        self.assertEqual(result.instance_assd, np.inf)
+        self.assertEqual(result.sq_assd, np.inf)
 
     def test_ref_empty(self):
         a = np.zeros([50, 50], np.uint16)
@@ -79,7 +78,7 @@ def test_ref_empty(self):
         self.assertEqual(result.fn, 0)
         self.assertEqual(result.sq, 0.0)
         self.assertEqual(result.pq, 0.0)
-        self.assertEqual(result.instance_assd, np.inf)
+        self.assertEqual(result.sq_assd, np.inf)
 
     def test_both_empty(self):
         a = np.zeros([50, 50], np.uint16)
@@ -102,4 +101,4 @@ def test_both_empty(self):
         self.assertEqual(result.fn, 0)
         self.assertTrue(np.isnan(result.sq))
         self.assertTrue(np.isnan(result.pq))
-        self.assertTrue(np.isnan(result.instance_assd))
+        self.assertTrue(np.isnan(result.sq_assd))