Surface dice loss

src/membrain_seg/annotations/extract_patch_cli.py

@@ -21,6 +21,11 @@ def extract_patches(
         help="Path to the folder where extracted patches should be stored. \
             (subdirectories will be created)",
     ),
+    ds_token: str = Option(  # noqa: B008
+        "other",
+        help="Dataset token. Important for distinguishing between different \
+            datasets. Should NOT contain underscores!",
+    ),
     coords_file: str = Option(  # noqa: B008
         None,
         help="Path to a file containing coordinates for patch extraction. The file \
@@ -93,6 +98,7 @@ def extract_patches(
         coords=coords,
         out_dir=out_folder,
         idx_add=idx_add,
+        ds_token=ds_token,
         token=token,
         pad_value=pad_value,
     )

src/membrain_seg/annotations/extract_patches.py

@@ -51,7 +51,7 @@ def pad_labels(patch, padding, pad_value=2.0):
 
 
 def get_out_files_and_patch_number(
-    token, out_folder_raw, out_folder_lab, patch_nr, idx_add
+    ds_token, token, out_folder_raw, out_folder_lab, patch_nr, idx_add
 ):
     """
     Create filenames and corrected patch numbers.
@@ -62,8 +62,10 @@ def get_out_files_and_patch_number(
 
     Parameters
     ----------
+    ds_token : str
+        The dataset identifier used as a part of the filename.
     token : str
-        The unique identifier used as a part of the filename.
+        The tomogram identifier used as a part of the filename.
     out_folder_raw : str
         The directory path where raw data patches are stored.
     out_folder_lab : str
@@ -96,27 +98,34 @@ def get_out_files_and_patch_number(
     """
     patch_nr += idx_add
     out_file_patch = os.path.join(
-        out_folder_raw, token + "_patch" + str(patch_nr) + "_raw.nii.gz"
+        out_folder_raw, ds_token + "_" + token + "_patch" + str(patch_nr) + ".nii.gz"
     )
     out_file_patch_label = os.path.join(
-        out_folder_lab, token + "_patch" + str(patch_nr) + "_labels.nii.gz"
+        out_folder_lab, ds_token + "_" + token + "_patch" + str(patch_nr) + ".nii.gz"
     )
     exist_add = 0
     while os.path.isfile(out_file_patch):
         exist_add += 1
         out_file_patch = os.path.join(
             out_folder_raw,
-            token + "_patch" + str(patch_nr + exist_add) + "_raw.nii.gz",
+            ds_token + "_" + token + "_patch" + str(patch_nr + exist_add) + ".nii.gz",
         )
         out_file_patch_label = os.path.join(
             out_folder_lab,
-            token + "_patch" + str(patch_nr + exist_add) + "_labels.nii.gz",
+            ds_token + "_" + token + "_patch" + str(patch_nr + exist_add) + ".nii.gz",
         )
     return patch_nr + exist_add, out_file_patch, out_file_patch_label
 
 
 def extract_patches(
-    tomo_path, seg_path, coords, out_dir, idx_add=0, token=None, pad_value=2.0
+    tomo_path,
+    seg_path,
+    coords,
+    out_dir,
+    ds_token="other",
+    token=None,
+    idx_add=0,
+    pad_value=2.0,
 ):
     """
     Extracts 3D patches from a given tomogram and corresponding segmentation.
@@ -133,11 +142,13 @@ def extract_patches(
         List of tuples where each tuple represents the 3D coordinates of a patch center.
     out_dir : str
         The output directory where the extracted patches will be saved.
-    idx_add : int, optional
-        The index addition for patch numbering, default is 0.
+    ds_token : str, optional
+        Dataset token to uniquely identify the dataset, default is 'other'.
     token : str, optional
         Token to uniquely identify the tomogram, default is None. If None,
         the base name of the tomogram file path is used.
+    idx_add : int, optional
+        The index addition for patch numbering, default is 0.
     pad_value: float, optional
         Borders of extracted patch are padded with this value ("ignore" label)
 
@@ -170,7 +181,7 @@ def extract_patches(
 
     for patch_nr, cur_coords in enumerate(coords):
         patch_nr, out_file_patch, out_file_patch_label = get_out_files_and_patch_number(
-            token, out_folder_raw, out_folder_lab, patch_nr, idx_add
+            ds_token, token, out_folder_raw, out_folder_lab, patch_nr, idx_add
         )
         print("Extracting patch nr", patch_nr, "from tomo", token)
         try:

src/membrain_seg/annotations/merge_corrections.py

@@ -46,13 +46,15 @@ def get_corrections_from_folder(folder_name, orig_pred_file):
             or filename.startswith("Ignore")
             or filename.startswith("ignore")
         ):
-            print("ATTENTION! Not processing", filename)
-            print("Is this intended?")
+            print(
+                "File does not fit into Add/Remove/Ignore naming! " "Not processing",
+                filename,
+            )
             continue
         readdata = sitk.GetArrayFromImage(
             sitk.ReadImage(os.path.join(folder_name, filename))
         )
-        print("Adding file", filename, "<--")
+        print("Adding file", filename)
 
         if filename.startswith("Add") or filename.startswith("add"):
             add_patch += readdata

src/membrain_seg/segmentation/cli/train_cli.py

@@ -1,4 +1,7 @@
+from typing import List, Optional
+
 from typer import Option
+from typing_extensions import Annotated
 
 from ..train import train as _train
 from .cli import OPTION_PROMPT_KWARGS as PKWARGS
@@ -70,7 +73,7 @@ def train_advanced(
         help="Batch size for training.",
     ),
     num_workers: int = Option(  # noqa: B008
-        1,
+        8,
         help="Number of worker threads for loading data",
     ),
     max_epochs: int = Option(  # noqa: B008
@@ -84,6 +87,22 @@ def train_advanced(
             but also severely increases training time.\
                 Pass "True" or "False".',
     ),
+    use_surface_dice: bool = Option(  # noqa: B008
+        False, help='Whether to use Surface-Dice as a loss. Pass "True" or "False".'
+    ),
+    surface_dice_weight: float = Option(  # noqa: B008
+        1.0, help="Scaling factor for the Surface-Dice loss. "
+    ),
+    surface_dice_tokens: Annotated[
+        Optional[List[str]],
+        Option(
+            help='List of tokens to \
+            use for the Surface-Dice loss. \
+            Pass tokens separately:\
+            For example, train_advanced --surface_dice_tokens "ds1" \
+            --surface_dice_tokens "ds2"'
+        ),
+    ] = None,
     use_deep_supervision: bool = Option(  # noqa: B008
         True, help='Whether to use deep supervision. Pass "True" or "False".'
     ),
@@ -119,6 +138,12 @@ def train_advanced(
         If set to False, data augmentation still happens, but not as frequently.
         More data augmentation can lead to a better performance, but also increases the
         training time substantially.
+    use_surface_dice : bool
+        Determines whether to use Surface-Dice loss, by default True.
+    surface_dice_weight : float
+        Scaling factor for the Surface-Dice loss, by default 1.0.
+    surface_dice_tokens : list
+        List of tokens to use for the Surface-Dice loss, by default ["all"].
     use_deep_supervision : bool
         Determines whether to use deep supervision, by default True.
     project_name : str
@@ -140,6 +165,9 @@ def train_advanced(
         max_epochs=max_epochs,
         aug_prob_to_one=aug_prob_to_one,
         use_deep_supervision=use_deep_supervision,
+        use_surf_dice=use_surface_dice,
+        surf_dice_weight=surface_dice_weight,
+        surf_dice_tokens=surface_dice_tokens,
         project_name=project_name,
         sub_name=sub_name,
     )

src/membrain_seg/segmentation/dataloading/memseg_dataset.py

@@ -1,7 +1,6 @@
 import os
 from typing import Dict
 
-# from skimage import io
 import imageio as io
 import numpy as np
 from torch.utils.data import Dataset
@@ -102,6 +101,7 @@ def __getitem__(self, idx: int) -> Dict[str, np.ndarray]:
             "label": np.expand_dims(self.labels[idx], 0),
         }
         idx_dict = self.transforms(idx_dict)
+        idx_dict["dataset"] = self.dataset_labels[idx]
         return idx_dict
 
     def __len__(self) -> int:
@@ -126,6 +126,7 @@ def load_data(self) -> None:
         print("Loading images into dataset.")
         self.imgs = []
         self.labels = []
+        self.dataset_labels = []
         for entry in self.data_paths:
             label = read_nifti(
                 entry[1]
@@ -137,6 +138,7 @@ def load_data(self) -> None:
             img = np.transpose(img, (1, 2, 0))
             self.imgs.append(img)
             self.labels.append(label)
+            self.dataset_labels.append(get_dataset_token(entry[0]))
 
     def initialize_imgs_paths(self) -> None:
         """
@@ -190,3 +192,23 @@ def test(self, test_folder: str, num_files: int = 20) -> None:
                     os.path.join(test_folder, f"test_mask_ds2_{i}_group{num_mask}.png"),
                     test_sample["label"][1][0, :, :, num_mask],
                 )
+
+
+def get_dataset_token(patch_name):
+    """
+    Get the dataset token from the patch name.
+
+    Parameters
+    ----------
+    patch_name : str
+        The name of the patch.
+
+    Returns
+    -------
+    str
+        The dataset token.
+
+    """
+    basename = os.path.basename(patch_name)
+    dataset_token = basename.split("_")[0]
+    return dataset_token