updated ncc metrics

GANDLF/cli/generate_metrics.py

@@ -18,10 +18,7 @@
     peak_signal_noise_ratio,
     mean_squared_log_error,
     mean_absolute_error,
-    ncc_mean,
-    ncc_std,
-    ncc_max,
-    ncc_min,
+    ncc_metrics,
 )
 from GANDLF.losses.segmentation import dice
 from GANDLF.metrics.segmentation import (
@@ -375,18 +372,9 @@ def __percentile_clip(
             # ncc metrics
             compute_ncc = parameters.get("compute_ncc", True)
             if compute_ncc:
-                overall_stats_dict[current_subject_id]["ncc_mean"] = ncc_mean(
-                    output_infill, gt_image_infill
-                )
-                overall_stats_dict[current_subject_id]["ncc_std"] = ncc_std(
-                    output_infill, gt_image_infill
-                )
-                overall_stats_dict[current_subject_id]["ncc_max"] = ncc_max(
-                    output_infill, gt_image_infill
-                )
-                overall_stats_dict[current_subject_id]["ncc_min"] = ncc_min(
-                    output_infill, gt_image_infill
-                )
+                calculated_ncc_metrics = ncc_metrics(output_infill, gt_image_infill)
+                for key, value in calculated_ncc_metrics.items():
+                    overall_stats_dict[current_subject_id][key] = value.item()
 
             # only voxels that are to be inferred (-> flat array)
             # these are required for mse, psnr, etc.

GANDLF/metrics/__init__.py

@@ -35,10 +35,7 @@
     peak_signal_noise_ratio,
     mean_squared_log_error,
     mean_absolute_error,
-    ncc_mean,
-    ncc_std,
-    ncc_max,
-    ncc_min,
+    ncc_metrics,
 )
 import GANDLF.metrics.classification as classification
 import GANDLF.metrics.regression as regression

GANDLF/metrics/synthesis.py

@@ -163,69 +163,27 @@ def __convert_to_grayscale(image: sitk.Image) -> sitk.Image:
     return correlation_filter.Execute(target_image, pred_image)
 
 
-def ncc_mean(prediction: torch.Tensor, target: torch.Tensor) -> float:
+def ncc_metrics(prediction: torch.Tensor, target: torch.Tensor) -> dict:
     """
-    Computes normalized cross correlation mean between target and prediction.
+    Computes normalized cross correlation metrics between target and prediction.
 
     Args:
         prediction (torch.Tensor): The prediction tensor.
         target (torch.Tensor): The target tensor.
 
     Returns:
-        float: The normalized cross correlation mean.
+        dict: The normalized cross correlation metrics.
     """
-    stats_filter = sitk.StatisticsImageFilter()
     corr_image = _get_ncc_image(target, prediction)
-    stats_filter.Execute(corr_image)
-    return stats_filter.GetMean()
-
-
-def ncc_std(prediction: torch.Tensor, target: torch.Tensor) -> float:
-    """
-    Computes normalized cross correlation standard deviation between target and prediction.
-
-    Args:
-        prediction (torch.Tensor): The prediction tensor.
-        target (torch.Tensor): The target tensor.
-
-    Returns:
-        float: The normalized cross correlation standard deviation.
-    """
-    stats_filter = sitk.StatisticsImageFilter()
-    corr_image = _get_ncc_image(target, prediction)
-    stats_filter.Execute(corr_image)
-    return stats_filter.GetSigma()
-
-
-def ncc_max(prediction: torch.Tensor, target: torch.Tensor) -> float:
-    """
-    Computes normalized cross correlation maximum between target and prediction.
-
-    Args:
-        prediction (torch.Tensor): The prediction tensor.
-        target (torch.Tensor): The target tensor.
-
-    Returns:
-        float: The normalized cross correlation maximum.
-    """
-    stats_filter = sitk.StatisticsImageFilter()
-    corr_image = _get_ncc_image(target, prediction)
-    stats_filter.Execute(corr_image)
-    return stats_filter.GetMaximum()
-
-
-def ncc_min(prediction: torch.Tensor, target: torch.Tensor) -> float:
-    """
-    Computes normalized cross correlation minimum between target and prediction.
-
-    Args:
-        prediction (torch.Tensor): The prediction tensor.
-        target (torch.Tensor): The target tensor.
-
-    Returns:
-        float: The normalized cross correlation minimum.
-    """
-    stats_filter = sitk.StatisticsImageFilter()
-    corr_image = _get_ncc_image(target, prediction)
-    stats_filter.Execute(corr_image)
-    return stats_filter.GetMinimum()
+    stats_filter = sitk.LabelStatisticsImageFilter()
+    stats_filter.UseHistogramsOn()
+    # ensure that we are not considering zeros
+    onesImage = corr_image == corr_image
+    stats_filter.Execute(corr_image, onesImage)
+    return {
+        "mean": stats_filter.GetMean(1),
+        "std": stats_filter.GetSigma(1),
+        "max": stats_filter.GetMaximum(1),
+        "min": stats_filter.GetMinimum(1),
+        "median": stats_filter.GetMedian(1),
+    }