Context based inference

platipy/imaging/cnn/dataset.py

@@ -235,7 +235,7 @@ def apply(self, img, context_map, masks=[]):
 
 
 def crop_img_using_localise_model(
-    img, localise_model, spacing=[1, 1, 1], crop_to_grid_size=[100, 100, 100]
+    img, localise_model, spacing=[1, 1, 1], crop_to_grid_size=[100, 100, 100], context_seg=None
 ):
     """Crops an image using a LocaliseUNet
 
@@ -246,6 +246,8 @@ def crop_img_using_localise_model(
         spacing (list, optional): The image spacing (mm) to resample to. Defaults to [1,1,1].
         crop_to_grid_size (list, optional): The size of the grid to crop to. Defaults to
           [100,100,100].
+        context_seg (sitk.Image, optional): Use this segmentation instead of localise model if
+          provided. Defaults to None.
 
     Returns:
         SimpleITK.Image: The cropped image.
@@ -254,15 +256,18 @@ def crop_img_using_localise_model(
     if isinstance(localise_model, str):
         localise_model = Path(localise_model)
 
-    if isinstance(localise_model, Path):
-        if localise_model.is_dir():
-            # Find the first actual model checkpoint in this directory
-            localise_model = next(localise_model.glob("*.ckpt"))
+    if context_seg is not None:
+        localise_pred = context_seg
+    else:
+        if isinstance(localise_model, Path):
+            if localise_model.is_dir():
+                # Find the first actual model checkpoint in this directory
+                localise_model = next(localise_model.glob("*.ckpt"))
 
-        localise_model = LocaliseUNet.load_from_checkpoint(localise_model)
+            localise_model = LocaliseUNet.load_from_checkpoint(localise_model)
 
-    localise_model.eval()
-    localise_pred = localise_model.infer(img)
+        localise_model.eval()
+        localise_pred = localise_model.infer(img)
 
     img = preprocess_image(img, spacing=spacing, crop_to_grid_size_xy=None)
     localise_pred = resample_mask_to_image(img, localise_pred)

platipy/imaging/cnn/train.py

@@ -283,6 +283,7 @@ def infer(
         latent_dim=True,
         spaced_range=[-1.5, 1.5],
         preprocess=True,
+        return_latent_space=False
     ):
         # sample strategy in "mean", "random", "spaced"
 
@@ -340,6 +341,7 @@ def infer(
                         localise_path,
                         spacing=self.hparams.spacing,
                         crop_to_grid_size=self.hparams.localise_voxel_grid_size,
+                        context_seg=seg
                     )
                 else:
                     img = preprocess_image(
@@ -406,6 +408,9 @@ def infer(
                     else:
                         self.prob_unet.forward(x)
 
+                if return_latent_space:
+                    return self.prob_unet.prior_latent_space
+
                 for sample in samples:
                     if self.hparams.prob_type == "prob":
                         if sample["name"] == "mean":