Merge branch '0014_mc_imp'

docs/results/nnDetectionV001.md

@@ -15,10 +15,10 @@
 | Model       | LIDC                 | RibFrac  | CADA     | Kits19   |
 |:-----------:|:--------------------:|:--------:|:--------:|:--------:|
 | nnDetection | 0.605                | 0.765    | 0.924    | 0.923    |
-| nnUNetPlus  | 0.385<sup>*</sup>    | 0.700    | 0.955    | 0.935    |
-| nnUNetBasic | 0.346<sup>*</sup>    | 0.667    | 0.930    | 0.908    |
+| nnUNetPlus  | 0.439<sup>*</sup>    | 0.700    | 0.955    | 0.935    |
+| nnUNetBasic | 0.411<sup>*</sup>    | 0.667    | 0.930    | 0.908    |
 
-<sup>*</sup> results with corrected numerical values in softdice loss. Out of the box results: nnUNetPlus 0.304 and nnUNetBasic 0.232
+<sup>*</sup> results with corrected numerical values in softdice loss and improved multi-class import.
 
 &nbsp;
 
@@ -27,21 +27,24 @@
 5 Fold Cross Validation
 
 | Model       | ADAM     | ProstateX              | Pancreas | Hepatic Vessel   | Colon | Liver |
-|:-----------:|:--------:|:----------:|:--------:|:----------------:|:-----:|:-----:|
+|:-----------:|:--------:|:----------------------:|:--------:|:----------------:|:-----:|:-----:|
 | nnDetection | 0.780    | 0.300                  | 0.766    | 0.727            | 0.662 | 0.628 |
-| nnUNetPlus  | 0.720    | 0.197      | 0.721    | 0.721            | 0.579 | 0.678 |
-| nnUNetBasic | 0.657    | 0.204      | 0.691    | 0.699            | 0.509 | 0.567 |
+| nnUNetPlus  | 0.720    | 0.220<sup>*</sup>      | 0.721    | 0.721            | 0.579 | 0.678 |
+| nnUNetBasic | 0.657    | 0.202<sup>*</sup>      | 0.691    | 0.699            | 0.509 | 0.567 |
+
+<sup>*</sup>improved multi-class import
 
 &nbsp;
 
 Test Split
 
 | Model       | ProstateX              | Pancreas | Hepatic Vessel   | Colon | Liver |
-|:-----------:|:----------:|:--------:|:----------------:|:-----:|:-----:|
+|:-----------:|:----------------------:|:--------:|:----------------:|:-----:|:-----:|
 | nnDetection | 0.221                  | 0.791    | 0.664            | 0.696 | 0.790 |
-| nnUNetPlus  | 0.078      | 0.704    | 0.684            | 0.731 | 0.760 |
+| nnUNetPlus  | 0.123<sup>*</sup>      | 0.704    | 0.684            | 0.731 | 0.760 |
 
 ADAM Results are listed under Benchmarks
+<sup>*</sup>improved multi-class import
 
 &nbsp;
 

nndet/io/itk.py

@@ -74,26 +74,6 @@ def create_circle_mask_itk(image_itk: sitk.Image,
     return mask_itk
 
 
-def copy_meta_data_itk(source: sitk.Image, target: sitk.Image) -> sitk.Image:
-    """
-    Copy meta data between files
-
-    Args:
-        source: source file
-        target: target file
-
-    Returns:
-        sitk.Image: target file with copied meta data
-    """
-    # for i in source.GetMetaDataKeys():
-    #     target.SetMetaData(i, source.GetMetaData(i))
-    raise NotImplementedError("Does not work!")
-    target.SetOrigin(source.GetOrigin())
-    target.SetDirection(source.GetDirection())
-    target.SetSpacing(source.GetSpacing())
-    return target
-
-
 def load_sitk(path: Union[Path, str], **kwargs) -> sitk.Image:
     """
     Functional interface to load image with sitk

nndet/io/prepare.py

@@ -19,18 +19,16 @@ import numpy as np
 import SimpleITK as sitk
 
 from pathlib import Path
-from typing import Dict, List, Sequence, Optional
+from typing import List, Sequence
 
-from nndet.io.paths import Pathlike
 from loguru import logger
 from sklearn.model_selection import train_test_split
 
+from nndet.io.paths import Pathlike
 from nndet.io.paths import get_case_ids_from_dir
-from nndet.io.load import save_json
-from nndet.utils.clustering import seg2instances, remove_classes, reorder_classes
 
 
-__all__ = ["maybe_split_4d_nifti", "instances_from_segmentation", "sitk_copy_metadata"]
+__all__ = ["maybe_split_4d_nifti"]
 
 
 def maybe_split_4d_nifti(source_file: Path, output_folder: Path):
@@ -114,99 +112,6 @@ def create_itk_image_spatial_props(
     return data_itk
 
 
-def sitk_copy_metadata(img_source: sitk.Image, img_target: sitk.Image) -> sitk.Image:
-    """
-    Copy metadata (spacing, origin, direction) from source to target image
-
-    Args
-        img_source: source image
-        img_target: target image
-
-    Returns:
-        SimpleITK.Image: target image with copied metadata
-    """ 
-    raise RuntimeError("Deprecated")
-    spacing = img_source.GetSpacing()
-    img_target.SetSpacing(spacing)
-
-    origin = img_source.GetOrigin()
-    img_target.SetOrigin(origin)
-
-    direction = img_source.GetDirection()
-    img_target.SetDirection(direction)
-    return img_target
-
-
-def instances_from_segmentation(source_file: Path, output_folder: Path,
-                                rm_classes: Sequence[int] = None,
-                                ro_classes: Dict[int, int] = None,
-                                subtract_one_of_classes: bool = True,
-                                fg_vs_bg: bool = False,
-                                file_name: Optional[str] = None
-                                ):
-    """
-    1. Optionally removes classes from the segmentation (
-    e.g. organ segmentation's which are not useful for detection)
-
-    2. Optionally reorders the segmentation indices
-
-    3. Converts semantic segmentation to instance segmentation's via
-    connected components
-
-    Args:
-        source_file: path to semantic segmentation file
-        output_folder: folder where processed file will be saved
-        rm_classes: classes to remove from semantic segmentation
-        ro_classes: reorder classes before instances are generated
-        subtract_one_of_classes: subtracts one from the classes
-            in the instance mapping (detection networks assume
-            that classes start from 0)
-        fg_vs_bg: map all foreground classes to a single class to run
-            foreground vs background detection task.
-        file_name: name of saved file (without file type!)
-    """
-    if subtract_one_of_classes and fg_vs_bg:
-        logger.info("subtract_one_of_classes will be ignored because fg_vs_bg is "
-                    "active and all foreground classes ill be mapped to 0")
-
-    seg_itk = sitk.ReadImage(str(source_file))
-    seg_npy = sitk.GetArrayFromImage(seg_itk)
-
-    if rm_classes is not None:
-        seg_npy = remove_classes(seg_npy, rm_classes)
-
-    if ro_classes is not None:
-        seg_npy = reorder_classes(seg_npy, ro_classes)
-
-    instances, instance_classes = seg2instances(seg_npy)
-    if fg_vs_bg:
-        num_instances_check = len(instance_classes)
-        seg_npy[seg_npy > 0] = 1
-        instances, instance_classes = seg2instances(seg_npy)
-        num_instances = len(instance_classes)
-        if num_instances != num_instances_check:
-            logger.warning(f"Lost instance: Found {num_instances} instances before "
-                           f"fg_vs_bg but {num_instances_check} instances after it")
-
-    if subtract_one_of_classes:
-        for key in instance_classes.keys():
-            instance_classes[key] -= 1
-
-    if fg_vs_bg:
-        for key in instance_classes.keys():
-            instance_classes[key] = 0
-
-    seg_itk_new = sitk.GetImageFromArray(instances)
-    seg_itk_new = sitk_copy_metadata(seg_itk, seg_itk_new)
-
-    if file_name is None:
-        suffix_length = sum(map(len, source_file.suffixes))
-        file_name = source_file.name[:-suffix_length]
-
-    save_json({"instances": instance_classes}, output_folder / f"{file_name}.json")
-    sitk.WriteImage(seg_itk_new, str(output_folder / f"{file_name}.nii.gz"))
-
-
 def create_test_split(splitted_dir: Pathlike,
                       num_modalities: int,
                       test_size: float = 0.3,

nndet/utils/clustering.py

@@ -22,17 +22,16 @@ from typing import Dict, Sequence, Union, Tuple, Optional
 from nndet.io.transforms.instances import get_bbox_np
 
 
-def seg2instances(seg: np.ndarray,
-                  exclude_background: bool = True,
+def seg_to_instances(
+    seg: np.ndarray,
     min_num_voxel: int = 0,
     ) -> Tuple[np.ndarray, Dict[int, int]]:
     """
-    Use connected components with ones matrix to created instance from segmentation
+    Use connected components with ones matrix to created
+    instances from segmentation
 
     Args:
         seg: semantic segmentation [spatial dims]
-        exclude_background: skips background class for the mapping
-            from instances to classes
         min_num_voxel: minimum number of voxels of an instance
 
     Returns:
@@ -40,14 +39,62 @@ def seg2instances(seg: np.ndarray,
         Dict[int, int]: mapping from instances to classes
     """
     structure = np.ones([3] * seg.ndim)
-    instances_temp, _ = label(seg, structure=structure)
+    
+    unique_classes = np.unique(seg)
+    unique_classes = unique_classes[unique_classes > 0]
+
+    instances = np.zeros_like(seg)
+    instance_classes = {}
+
+    i = 1
+    for uc in unique_classes:
+        binary_class_mask = (seg == uc)
+        instances_temp, _ = label(binary_class_mask, structure=structure)
+
+        instance_ids = np.unique(instances_temp)
+        instance_ids = instance_ids[instance_ids > 0]
+
+        for iid in instance_ids:
+            instance_binary_mask = instances_temp == iid
+            
+            if min_num_voxel > 0:
+                if instance_binary_mask.sum() < min_num_voxel:  # remove small instances
+                    continue
+
+            instances[instance_binary_mask] = i  # save instance to final mask
+            instance_classes[int(i)] = uc
+            i = i + 1  # bump instance index
+    return instances, instance_classes
+
+
+def seg_to_instances_voted(
+    seg: np.ndarray,
+    min_num_voxel: int = 0,
+    ) -> Tuple[np.ndarray, Dict[int, int]]:
+    """
+    Conntected component analysis is performed on foreground
+    (independent of exact class) and the final class
+    is determined via majority voting.
+
+    Args:
+        seg: semantic segmentation [spatial dims]
+        min_num_voxel: minimum number of voxels of an instance
+
+    Returns:
+        np.ndarray: instance segmentation
+        Dict[int, int]: mapping from instances to classes
+    """
+    structure = np.ones([3] * seg.ndim)
+
+    binary_fg_mask = (seg > 0).astype(int)
+    instances_temp, _ = label(binary_fg_mask, structure=structure)
 
     instance_ids = np.unique(instances_temp)
-    if exclude_background:
     instance_ids = instance_ids[instance_ids > 0]
 
+    instances = np.zeros_like(seg)
     instance_classes = {}
-    instances = np.zeros_like(instances_temp)
+
     i = 1
     for iid in instance_ids:
         instance_binary_mask = instances_temp == iid
@@ -57,15 +104,24 @@ def seg2instances(seg: np.ndarray,
                 continue
 
         instances[instance_binary_mask] = i  # save instance to final mask
-        single_idx = np.argwhere(instance_binary_mask)[0]  # select semantic class
-        semantic_class = int(seg[tuple(single_idx)])
-        instance_classes[int(i)] = semantic_class  # save class
+        cls_id, cls_count = np.unique(
+            seg[instance_binary_mask], return_counts=True) # count classes in region
+        majority_voted_class = cls_id[np.argmax(cls_count)] # select class with most votes
+
+        assert 0 not in cls_id
+        assert majority_voted_class > 0
+
+        instance_classes[int(i)] = majority_voted_class
         i = i + 1  # bump instance index
     return instances, instance_classes
 
 
-def remove_classes(seg: np.ndarray, rm_classes: Sequence[int], classes: Dict[int, int] = None,
-                   background: int = 0) -> Union[np.ndarray, Tuple[np.ndarray, Dict[int, int]]]:
+def remove_classes(
+    seg: np.ndarray,
+    rm_classes: Sequence[int],
+    classes: Dict[int, int] = None,
+    background: int = 0,
+    ) -> Union[np.ndarray, Tuple[np.ndarray, Dict[int, int]]]:
     """
     Remove classes from segmentation (also works on instances
     but instance ids may not be consecutive anymore)
@@ -90,7 +146,10 @@ def remove_classes(seg: np.ndarray, rm_classes: Sequence[int], classes: Dict[int
         return seg, classes
 
 
-def reorder_classes(seg: np.ndarray, class_mapping: Dict[int, int]) -> np.ndarray:
+def reorder_classes(
+    seg: np.ndarray,
+    class_mapping: Dict[int, int],
+    ) -> np.ndarray:
     """
     Reorders classes in segmentation
 
@@ -106,7 +165,8 @@ def reorder_classes(seg: np.ndarray, class_mapping: Dict[int, int]) -> np.ndarra
     return seg
 
 
-def compute_score_from_seg(instances: np.ndarray,
+def compute_score_from_seg(
+    instances: np.ndarray,
     instance_classes: Dict[int, int],
     probs: np.ndarray,
     aggregation: str = "max",
@@ -148,7 +208,8 @@ def compute_score_from_seg(instances: np.ndarray,
     return np.asarray(instance_scores)
 
 
-def instance_results_from_seg(probs: np.ndarray,
+def softmax_to_instances(
+    probs: np.ndarray,
     aggregation: str,
     stuff: Optional[Sequence[int]] = None,
     min_num_voxel: int = 0,
@@ -178,12 +239,16 @@ def instance_results_from_seg(probs: np.ndarray,
             `pred_labels`: predicted class for each instance/box
             `pred_scores`: predicted score for each instance/box
     """
+    if probs.shape[0] < 2:
+        raise ValueError("softmax_to_instances only works for softmax probabilities")
+
     if min_threshold is not None:
         if probs.shape[0] > 2:
-            fg_argmax = np.argmax(probs, axis=0)
-            fg_mask = np.max(probs[1:], axis=0) > min_threshold
+            cluster_map = np.max(probs[1:], axis=0) > min_threshold
+            class_map = np.argmax(probs[1:], axis=0) + 1
+
             seg = np.zeros_like(probs[0])
-            seg[fg_mask] = fg_argmax[fg_mask]
+            seg[cluster_map] = class_map[cluster_map]
         else:
             seg = probs[1] > min_threshold
     else:
@@ -192,12 +257,12 @@ def instance_results_from_seg(probs: np.ndarray,
     if stuff is not None:
         for s in stuff:
             seg[seg == s] = 0
-    instances, instance_classes = seg2instances(seg,
-                                                exclude_background=True,
-                                                min_num_voxel=min_num_voxel,
+
+    instances, instance_classes = seg_to_instances_voted(seg, min_num_voxel=min_num_voxel)
+
+    instance_scores = compute_score_from_seg(
+        instances, instance_classes, probs, aggregation=aggregation,
         )
-    instance_scores = compute_score_from_seg(instances, instance_classes, probs,
-                                             aggregation=aggregation)
     instance_classes = {int(key): int(item) - 1 for key, item in instance_classes.items()}
     tmp = get_bbox_np(instances[None], instance_classes)
     instance_boxes = tmp["boxes"]

nndet/utils/nnunet.py

@@ -28,7 +28,6 @@ from typing import Sequence, Union
 from nndet.io.itk import load_sitk_as_array, load_sitk
 from nndet.io.load import save_json, load_json
 from nndet.io.paths import get_case_ids_from_dir
-from nndet.io.prepare import sitk_copy_metadata
 from nndet.io.transforms.instances import instances_to_segmentation_np
 
 Pathlike = Union[str, Path]

projects/Task019_ADAM/scripts/prepare.py

 import os
 import shutil
+from typing import Sequence, Dict, Optional
+
+import SimpleITK as sitk
 from pathlib import Path
+from loguru import logger
 
 from nndet.io import save_json
 from nndet.io.prepare import instances_from_segmentation
 from nndet.utils.check import env_guard
 from nndet.utils.info import maybe_verbose_iterable
+from nndet.utils.clustering import seg_to_instances, remove_classes, reorder_classes
+
+
+def instances_from_segmentation(
+    source_file: Path, output_folder: Path,
+    rm_classes: Sequence[int] = None,
+    ro_classes: Dict[int, int] = None,
+    subtract_one_of_classes: bool = True,
+    fg_vs_bg: bool = False,
+    file_name: Optional[str] = None
+    ):
+    """
+    1. Optionally removes classes from the segmentation (
+    e.g. organ segmentation's which are not useful for detection)
+
+    2. Optionally reorders the segmentation indices
+
+    3. Converts semantic segmentation to instance segmentation's via
+    connected components
+
+    Args:
+        source_file: path to semantic segmentation file
+        output_folder: folder where processed file will be saved
+        rm_classes: classes to remove from semantic segmentation
+        ro_classes: reorder classes before instances are generated
+        subtract_one_of_classes: subtracts one from the classes
+            in the instance mapping (detection networks assume
+            that classes start from 0)
+        fg_vs_bg: map all foreground classes to a single class to run
+            foreground vs background detection task.
+        file_name: name of saved file (without file type!)
+    """
+    if subtract_one_of_classes and fg_vs_bg:
+        logger.info("subtract_one_of_classes will be ignored because fg_vs_bg is "
+                    "active and all foreground classes ill be mapped to 0")
+
+    seg_itk = sitk.ReadImage(str(source_file))
+    seg_npy = sitk.GetArrayFromImage(seg_itk)
+
+    if rm_classes is not None:
+        seg_npy = remove_classes(seg_npy, rm_classes)
+
+    if ro_classes is not None:
+        seg_npy = reorder_classes(seg_npy, ro_classes)
+
+    instances, instance_classes = seg_to_instances(seg_npy)
+    if fg_vs_bg:
+        num_instances_check = len(instance_classes)
+        seg_npy[seg_npy > 0] = 1
+        instances, instance_classes = seg_to_instances(seg_npy)
+        num_instances = len(instance_classes)
+        if num_instances != num_instances_check:
+            logger.warning(f"Lost instance: Found {num_instances} instances before "
+                           f"fg_vs_bg but {num_instances_check} instances after it")
+
+    if subtract_one_of_classes:
+        for key in instance_classes.keys():
+            instance_classes[key] -= 1
+
+    if fg_vs_bg:
+        for key in instance_classes.keys():
+            instance_classes[key] = 0
+
+    seg_itk_new = sitk.GetImageFromArray(instances)
+    seg_itk_new.SetSpacing(seg_itk.GetSpacing())
+    seg_itk_new.SetOrigin(seg_itk.GetOrigin())
+    seg_itk_new.SetDirection(seg_itk.GetDirection())
+
+    if file_name is None:
+        suffix_length = sum(map(len, source_file.suffixes))
+        file_name = source_file.name[:-suffix_length]
+
+    save_json({"instances": instance_classes}, output_folder / f"{file_name}.json")
+    sitk.WriteImage(seg_itk_new, str(output_folder / f"{file_name}.nii.gz"))
 
 
 def run_prep_fg_v_bg(

projects/Task021_ProstateX/scripts/prepare.py

@@ -11,8 +11,8 @@ from nndet.io.prepare import create_test_split
 from loguru import logger
 
 from nndet.utils.check import env_guard
-from nndet.io import save_json, save_yaml
-from nndet.io.itk import load_sitk, load_sitk_as_array, copy_meta_data_itk
+from nndet.io import save_json
+from nndet.io.itk import load_sitk, load_sitk_as_array
 from nndet.utils.info import maybe_verbose_iterable
 
 
@@ -99,7 +99,9 @@ def prepare_case(case_id,
             mask[_mask > 0] = idx
 
         mask_final = sitk.GetImageFromArray(mask)
-        copy_meta_data_itk(t2_data_itk, mask_final)
+        mask_final.SetOrigin(t2_data_itk.GetOrigin())
+        mask_final.SetDirection(t2_data_itk.GetDirection())
+        mask_final.SetSpacing(t2_data_itk.GetSpacing())
 
         df_case = df_labels.loc[df_labels['ProxID'] == case_id]
         instances = {}

scripts/convert_seg2det.py

@@ -26,16 +26,15 @@ import numpy as np
 import SimpleITK as sitk
 from hydra import initialize_config_module
 from loguru import logger
-from scipy import ndimage
-from scipy.ndimage import label
 from tqdm import tqdm
 
 from nndet.core.boxes import box_size_np
-from nndet.io import get_case_ids_from_dir, load_json, save_json
+from nndet.io import save_json
 from nndet.io.transforms.instances import get_bbox_np
-from nndet.io.itk import copy_meta_data_itk, load_sitk, load_sitk_as_array
+from nndet.io.itk import load_sitk, load_sitk_as_array
 from nndet.utils.config import compose
 from nndet.utils.check import env_guard
+from nndet.utils.clustering import seg_to_instances
 
 
 def prepare_detection_label(case_id: str,
@@ -66,11 +65,10 @@ def prepare_detection_label(case_id: str,
         sitk.WriteImage(stuff_seg_itk, str(label_dir / f"{case_id}_stuff.nii.gz"))
 
     # prepare things information
-    structure = np.ones([3] * seg.ndim)
     things_seg = np.copy(seg)
     things_seg[stuff_seg > 0] = 0  # remove all stuff classes from segmentation
 
-    instances_not_filtered, _ = label(things_seg, structure=structure)
+    instances_not_filtered, instances_not_filtered_classes = seg_to_instances(things_seg)
     final_mapping = {}
     if instances_not_filtered.max() > 0:
         boxes = get_bbox_np(instances_not_filtered[None])["boxes"]
@@ -92,11 +90,8 @@ def prepare_detection_label(case_id: str,
 
             if all(bsize_world[isotopic_axis] > min_size) and (instance_vol > min_vol):
                 instances[instance_mask] = start_id
-
-                single_idx = np.argwhere(instance_mask)[0]
-                semantic_class = int(seg[tuple(single_idx)])
+                semantic_class = instances_not_filtered_classes[int(iid)]
                 final_mapping[start_id] = things_classes.index(semantic_class)
-
                 start_id += 1
     else:
         instances = np.zeros_like(instances_not_filtered)
@@ -128,9 +123,12 @@ def main():
     ============================================================================  
     Needs additional information from dataset.json/.yaml:
         `seg2det_stuff`: these are classes which are interpreted semantically
+            (stuff classes are experimental and will probably changed in
+             the future)
         `seg2det_things`: these are classes which are interpreted as instances
          Both entries should be lists with the indices of the respective
          classes where the position will determine its new class
+         (currently only one classes is supported here)
          e.g.
             `seg2det_stuff`: [2,] -> remap class 2 from semantic segmentation
                 to new stuff class 1 (stuff classes start at one)

scripts/nnunet/nnunet_import.py

@@ -33,7 +33,7 @@ from loguru import logger
 
 from nndet.evaluator.registry import evaluate_box_dir
 from nndet.io import load_pickle, save_pickle, get_task, load_json
-from nndet.utils.clustering import instance_results_from_seg
+from nndet.utils.clustering import softmax_to_instances
 from nndet.utils.config import compose
 from nndet.utils.info import maybe_verbose_iterable
 
@@ -194,10 +194,12 @@ def import_dir(
                   stuff=stuff,
                   )
 
-    # for s in maybe_verbose_iterable(source):
-    #     _fn(s, target_dir)
+    if num_workers > 0:
         with Pool(processes=num_workers) as p:
             p.starmap(_fn, zip(source, repeat(target_dir)))
+    else:
+        for s in maybe_verbose_iterable(source):
+            _fn(s, target_dir)
 
 
 def import_single_case(logits_source: Path,
@@ -240,7 +242,7 @@ def import_single_case(logits_source: Path,
         tmp[:, bbox[0][0]:bbox[0][1], bbox[1][0]:bbox[1][1], bbox[2][0]:bbox[2][1]] = probs
         probs = tmp
 
-    res = instance_results_from_seg(probs,
+    res = softmax_to_instances(probs,
                                aggregation=aggregation,
                                min_num_voxel=min_num_voxel,
                                min_threshold=min_threshold,

scripts/preprocess.py

@@ -38,80 +38,11 @@ from nndet.planning import PLANNER_REGISTRY
 from nndet.planning.experiment.utils import create_labels
 from nndet.planning.properties.registry import medical_instance_props
 from nndet.io.load import load_pickle, load_npz_looped
-from nndet.io.prepare import maybe_split_4d_nifti, instances_from_segmentation
 from nndet.io.paths import get_paths_raw_to_split, get_paths_from_splitted_dir, subfiles, get_case_id_from_path
 from nndet.preprocessing import ImageCropper
 from nndet.utils.check import check_dataset_file, check_data_and_label_splitted
 
 
-def run_splitting_4d(data_dir: Path, output_dir: Path, num_processes: int) -> None:
-    """
-    Due to historical reasons this framework uses 3D niftis instead of 4D niftis
-    This function splits present 4D niftis into 3D niftis per channel
-
-    Args:
-        data_dir (str): top directory where data is located
-        output_dir (str): output directory for splitted data
-        num_processes (int): number of processes to use to split data
-        rm_classes: classes to remove from segmentation
-        ro_classes: reorder classes in segmentation
-        subtract_one_from_classes: subtract one from all classes in mapping
-        instances_from_seg: converts semantic segmentations to instance
-            segmentations via connected components
-    """
-    if output_dir.is_dir():
-        shutil.rmtree(output_dir)
-    output_dir.mkdir(parents=True)
-
-    source_files, target_folders = get_paths_raw_to_split(data_dir, output_dir)
-    with Pool(processes=num_processes) as p:
-        p.starmap(maybe_split_4d_nifti, zip(source_files, target_folders))
-
-
-def prepare_labels(data_dir: Path,
-                   output_dir: Path,
-                   num_processes: int,
-                   rm_classes: Sequence[int],
-                   ro_classes: Dict[int, int],
-                   subtract_one_from_classes: bool,
-                   instances_from_seg: bool = True):
-    """
-    Copy labels to splitted dir.
-    Optionally, runs connected components and removes classes from
-    semantic segmentations
-
-    Args:
-        data_dir: path to task base dir
-        output_dir: base dir of prepared labels
-        num_processes: number of processes to use
-        rm_classes: remove specific classes from semantic segmentation.
-            Can only be used with `instances_from_seg`
-        ro_classes: reorder classes in semantic segmentation for
-            connected components. Can only be used with `instances_from_seg`
-        subtract_one_from_classes: class indices for detection start from 0.
-            Subtracts 1 from classes extracted from segmentation
-        instances_from_seg: Run connected components. Defaults to True.
-    """
-    for labels_subdir in ("labelsTr", "labelsTs"):
-        if not (data_dir / labels_subdir).is_dir():
-            continue
-        labels_output_dir = output_dir / labels_subdir
-
-        if instances_from_seg:
-            if not labels_output_dir.is_dir():
-                labels_output_dir.mkdir(parents=True)
-
-            with Pool(processes=num_processes) as p:
-                paths = list(map(Path, subfiles(data_dir / labels_subdir,
-                                                identifier="*.nii.gz", join=True)))
-                paths = [path for path in paths if not path.name.startswith('.')]
-                p.starmap(instances_from_segmentation, zip(
-                    paths, repeat(labels_output_dir), repeat(rm_classes),
-                    repeat(ro_classes), repeat(subtract_one_from_classes)))
-        else:
-            shutil.copytree(data_dir / labels_subdir, labels_output_dir)
-
-
 def run_cropping_and_convert(cropped_output_dir: Path,
                              splitted_4d_output_dir: Path,
                              data_info: dict,

scripts/utils.py

@@ -15,6 +15,9 @@ limitations under the License.
 """
 
 def boxes2nii():
+    """
+    Only for visualisation purposes.
+    """
     import os
     import argparse
     from pathlib import Path
@@ -87,11 +90,11 @@ def boxes2nii():
 
         prediction_meta = {}
         for instance_id, (pbox, pscore, plabel) in enumerate(zip(boxes, scores, labels), start=1):
-            mask_slicing = [slice(int(pbox[0]), int(pbox[2])),
-                            slice(int(pbox[1]), int(pbox[3])),
+            mask_slicing = [slice(int(pbox[0]) + 1, int(pbox[2])),
+                            slice(int(pbox[1]) + 1, int(pbox[3])),
                             ]
             if instance_mask.ndim == 3:
-                mask_slicing.append(slice(int(pbox[4]), int(pbox[5])))
+                mask_slicing.append(slice(int(pbox[4]) + 1, int(pbox[5])))
             instance_mask[tuple(mask_slicing)] = instance_id
 
             prediction_meta[int(instance_id)] = {
@@ -112,6 +115,9 @@ def boxes2nii():
 
 
 def seg2nii():
+    """
+    Only for visualisation purposes.
+    """
     import os
     import argparse
     from pathlib import Path