added cristae model

synapse_net/inference/cristae.py

@@ -62,13 +62,17 @@ def segment_cristae(
         The segmentation mask as a numpy array, or a tuple containing the segmentation mask
         and the predictions if return_predictions is True.
     """
+    mitochondria = kwargs.pop("extra_segmentation")
     with_channels = kwargs.pop("with_channels", True)
     channels_to_standardize = kwargs.pop("channels_to_standardize", [0])
     if verbose:
         print("Segmenting cristae in volume of shape", input_volume.shape)
     # Create the scaler to handle prediction with a different scaling factor.
     scaler = _Scaler(scale, verbose)
-    input_volume = scaler.scale_input(input_volume)
+    # rescale each channel
+    volume = scaler.scale_input(input_volume)
+    mito_seg = scaler.scale_input(mitochondria, is_segmentation=True)
+    input_volume = np.stack([volume, mito_seg], axis=0)
 
     # Run prediction and segmentation.
     if mask is not None:

synapse_net/inference/inference.py

@@ -112,6 +112,7 @@ def get_model_training_resolution(model_type: str) -> Dict[str, float]:
         "active_zone": {"x": 1.44, "y": 1.44, "z": 1.44},
         "compartments": {"x": 3.47, "y": 3.47, "z": 3.47},
         "mitochondria": {"x": 2.07, "y": 2.07, "z": 2.07},
+        "cristae": {"x": 1.44, "y": 1.44, "z": 1.44},
         "ribbon": {"x": 1.188, "y": 1.188, "z": 1.188},
         "vesicles_2d": {"x": 1.35, "y": 1.35},
         "vesicles_3d": {"x": 1.35, "y": 1.35, "z": 1.35},

synapse_net/inference/util.py

@@ -59,7 +59,7 @@ def scale_input(self, input_volume, is_segmentation=False):
 
         if self._original_shape is None:
             self._original_shape = input_volume.shape
-        elif self._oringal_shape != input_volume.shape:
+        elif self._original_shape != input_volume.shape:
             raise RuntimeError(
                 "Scaler was called with different input shapes. "
                 "This is not supported, please create a new instance of the class for it."

synapse_net/tools/segmentation_widget.py

@@ -189,8 +189,11 @@ def on_predict(self):
             extra_seg = self._get_layer_selector_data(self.extra_seg_selector_name)
             kwargs = {"extra_segmentation": extra_seg}
         elif model_type == "cristae":  # Cristae model expects 2 3D volumes
-            image = np.stack([image, self._get_layer_selector_data(self.extra_seg_selector_name)], axis=0)
-            kwargs = {}
+            kwargs = {
+                "extra_segmentation": self._get_layer_selector_data(self.extra_seg_selector_name),
+                "with_channels": True,
+                "channels_to_standardize": [0]
+            }
         else:
             kwargs = {}
         segmentation = run_segmentation(