added cristae model and single channel transfrom

synapse_net/inference/cristae.py

@@ -42,6 +42,7 @@ def segment_cristae(
     return_predictions: bool = False,
     scale: Optional[List[float]] = None,
     mask: Optional[np.ndarray] = None,
+    **kwargs
 ) -> Union[np.ndarray, Tuple[np.ndarray, np.ndarray]]:
     """Segment cristae in an input volume.
 
@@ -61,6 +62,8 @@ 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.
     """
+    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.
@@ -72,7 +75,7 @@ def segment_cristae(
         mask = scaler.scale_input(mask, is_segmentation=True)
     pred = get_prediction(
         input_volume, model_path=model_path, model=model, mask=mask,
-        tiling=tiling, with_channels=True, verbose=verbose
+        tiling=tiling, with_channels=with_channels, channels_to_standardize=channels_to_standardize, verbose=verbose
     )
     foreground, boundaries = pred[:2]
     seg = _run_segmentation(foreground, verbose=verbose, min_size=min_size)

synapse_net/inference/inference.py

@@ -10,6 +10,7 @@
 from .mitochondria import segment_mitochondria
 from .ribbon_synapse import segment_ribbon_synapse_structures
 from .vesicles import segment_vesicles
+from .cristae import segment_cristae
 from .util import get_device
 from ..file_utils import get_cache_dir
 
@@ -25,6 +26,7 @@ def _get_model_registry():
         "compartments": "527983720f9eb215c45c4f4493851fd6551810361eda7b79f185a0d304274ee1",
         "mitochondria": "24625018a5968b36f39fa9d73b121a32e8f66d0f2c0540d3df2e1e39b3d58186",
         "mitochondria2": "553decafaff4838fff6cc8347f22c8db3dee5bcbeffc34ffaec152f8449af673",
+        "cristae": "f96c90484f4ea92ac0515a06e389cc117580f02c2aacdc44b5828820cf38c3c3",
         "ribbon": "7c947f0ddfabe51a41d9d05c0a6ca7d6b238f43df2af8fffed5552d09bb075a9",
         "vesicles_2d": "eb0b74f7000a0e6a25b626078e76a9452019f2d1ea6cf2033073656f4f055df1",
         "vesicles_3d": "b329ec1f57f305099c984fbb3d7f6ae4b0ff51ec2fa0fa586df52dad6b84cf29",
@@ -35,6 +37,7 @@ def _get_model_registry():
         "compartments": "https://owncloud.gwdg.de/index.php/s/DnFDeTmDDmZrDDX/download",
         "mitochondria": "https://owncloud.gwdg.de/index.php/s/1T542uvzfuruahD/download",
         "mitochondria2": "https://owncloud.gwdg.de/index.php/s/GZghrXagc54FFXd/download",
+        "cristae": "https://owncloud.gwdg.de/index.php/s/Df7OUOyQ1Kc2eEO/download",
         "ribbon": "https://owncloud.gwdg.de/index.php/s/S3b5l0liPP1XPYA/download",
         "vesicles_2d": "https://owncloud.gwdg.de/index.php/s/d72QIvdX6LsgXip/download",
         "vesicles_3d": "https://owncloud.gwdg.de/index.php/s/A425mkAOSqePDhx/download",
@@ -214,14 +217,16 @@ def run_segmentation(
     """
     if model_type.startswith("vesicles"):
         segmentation = segment_vesicles(image, model=model, tiling=tiling, scale=scale, verbose=verbose, **kwargs)
-    elif model_type == "mitochondria":
+    elif model_type == "mitochondria" or model_type == "mitochondria2":
         segmentation = segment_mitochondria(image, model=model, tiling=tiling, scale=scale, verbose=verbose, **kwargs)
     elif model_type == "active_zone":
         segmentation = segment_active_zone(image, model=model, tiling=tiling, scale=scale, verbose=verbose, **kwargs)
     elif model_type == "compartments":
         segmentation = segment_compartments(image, model=model, tiling=tiling, scale=scale, verbose=verbose, **kwargs)
     elif model_type == "ribbon":
         segmentation = _segment_ribbon_AZ(image, model=model, tiling=tiling, scale=scale, verbose=verbose, **kwargs)
+    elif model_type == "cristae":
+        segmentation = segment_cristae(image, model=model, tiling=tiling, scale=scale, verbose=verbose, **kwargs)
     else:
         raise ValueError(f"Unknown model type: {model_type}")
     return segmentation

synapse_net/inference/util.py

@@ -2,7 +2,7 @@
 import time
 import warnings
 from glob import glob
-from typing import Dict, Optional, Tuple, Union
+from typing import Dict, List, Optional, Tuple, Union
 
 # # Suppress annoying import warnings.
 # with warnings.catch_warnings():
@@ -85,6 +85,7 @@ def get_prediction(
     model: Optional[torch.nn.Module] = None,
     verbose: bool = True,
     with_channels: bool = False,
+    channels_to_standardize: Optional[List[int]] = None,
     mask: Optional[np.ndarray] = None,
 ) -> np.ndarray:
     """Run prediction on a given volume.
@@ -99,6 +100,7 @@ def get_prediction(
         tiling: The tiling configuration for the prediction.
         verbose: Whether to print timing information.
         with_channels: Whether to predict with channels.
+        channels_to_standardize: List of channels to standardize. Defaults to None.
         mask: Optional binary mask. If given, the prediction will only be run in
             the foreground region of the mask.
 
@@ -120,8 +122,12 @@ def get_prediction(
     # We standardize the data for the whole volume beforehand.
     # If we have channels then the standardization is done independently per channel.
     if with_channels:
+        input_volume = input_volume.astype(np.float32, copy=False)
         # TODO Check that this is the correct axis.
-        input_volume = torch_em.transform.raw.standardize(input_volume, axis=(1, 2, 3))
+        if channels_to_standardize is None:  # assume all channels
+            channels_to_standardize = range(input_volume.shape[0])
+        for ch in channels_to_standardize:
+            input_volume[ch] = torch_em.transform.raw.standardize(input_volume[ch])
     else:
         input_volume = torch_em.transform.raw.standardize(input_volume)
 

synapse_net/tools/segmentation_widget.py

@@ -178,6 +178,9 @@ def on_predict(self):
         if model_type == "ribbon":  # Currently only the ribbon model needs the extra seg.
             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 = {}
         else:
             kwargs = {}
         segmentation = run_segmentation(