Stabilize versions (#8)

* Specify package versions that work together

* Update paths to assets to align with current asset pathing scheme

* Specify nibabel as writer when saving cropped tumors

* Specify package versions for Setup.py

* Fix directory paths for tumor isolation

* Fix directory pathing for lung segmentation pipeline

* Fix pathing in the created dataset file

assets/readme.md

@@ -4,7 +4,7 @@
 
 1. Install synthlung by running `pip install .`
 2. Download the MSD Lung Tumor dataset from [here](https://drive.google.com/drive/folders/1HqEgzS8BV2c7xYNrZdEAnrHk7osJJ--2).
-3. Extract the zip file into `/assets/`.
+3. Extract the zip file into `/assets/images/`.
 4. Run `synthlung format --dataset msd` to adjust dataset format
 5. Run `synthlung seed --dataset msd` to extract tumor seeds from the dataset
 6. Run `synthlung host --dataset msd` to extract lung masks from the images

requirements.txt

@@ -1,7 +1,10 @@
-numpy
-torch
-monai
-tqdm
-lungmask
-pytest
-pytest-cov
+numpy==1.26.2
+torch==2.4.1
+monai==1.3.2
+tqdm==4.66.5
+lungmask==0.2.20
+pytest==8.3.3
+pytest-cov==5.0.0
+requests==2.32.3
+nibabel==5.3.0
+scikit-image==0.24.0

setup.py

@@ -10,11 +10,16 @@
     packages=setuptools.find_packages(),
     entry_points={"console_scripts": ["synthlung = synthlung.__main__:main"]},
     install_requires=[
-        "numpy",
-        "torch",
-        "tqdm",
-        "monai",
-        "lungmask"
+        "numpy==1.26.2",
+        "torch==2.4.1",
+        "tqdm==4.66.5",
+        "monai==1.3.2",
+        "lungmask==0.2.20",
+        "pytest==8.3.3",
+        "pytest-cov==5.0.0",
+        "requests==2.32.3",
+        "nibabel==5.3.0",
+        "scikit-image==0.24.0"
     ],
     classifiers=[
         "Programming Language :: Python :: 3",

synthlung/__main__.py

@@ -7,13 +7,13 @@
 import json
 
 def seed():
-    json_file_path = "./assets/source/dataset.json"
+    json_file_path = "./assets/images/source/dataset.json"
 
     with open(json_file_path, 'r') as json_file:
         image_dict = json.load(json_file)
     crop_pipeline = TumorCropPipeline()
     crop_pipeline(image_dict)
-    formatter = MSDGenerateJSONFormatter("./assets/seeds/")
+    formatter = MSDGenerateJSONFormatter("./assets/images/seeds/")
     formatter.generate_json()
 
 def format_msd():
@@ -23,11 +23,11 @@ def format_msd():
 
 def generate_randomized_tumors():
     tumor_inserter = InsertTumorPipeline()
-    json_file_path = "./assets/source/dataset.json"
+    json_file_path = "./assets/images/source/dataset.json"
     with open(json_file_path, 'r') as json_file:
         image_dict = json.load(json_file)
 
-    json_seed_path = "./assets/seeds/dataset.json"
+    json_seed_path = "./assets/images/seeds/dataset.json"
     with open(json_seed_path, 'r') as json_file:
         seeds_dict = json.load(json_file)
 
@@ -36,12 +36,12 @@ def generate_randomized_tumors():
 def mask_hosts():
     lung_masker = LMInferer()
     host_masker = LungMaskPipeline(lung_masker)
-    json_file_path = "./assets/source/dataset.json"
+    json_file_path = "./assets/images/source/dataset.json"
     with open(json_file_path, 'r') as json_file:
         image_dict = json.load(json_file)
 
     host_masker(image_dict)
-    json_generator = HostJsonGenerator('./assets/hosts/')
+    json_generator = HostJsonGenerator('./assets/images/hosts/')
     json_generator.generate_json()
 
 def main():

synthlung/utils/dataset_formatter.py

@@ -9,7 +9,7 @@
 LABEL_NII_GZ = 'label.nii.gz'
 
 class MSDImageSourceFormatter(ImageSourceFormatter, JSONGenerator):
-    def __init__(self, source_directory: str = "./assets/Task06_Lung/", target_directory: str = "./assets/source/") -> None:
+    def __init__(self, source_directory: str = "./assets/images/Task06_Lung/", target_directory: str = "./assets/images/source/") -> None:
         self.target_directory = target_directory
         self.source_directory = source_directory
 

synthlung/utils/lung_segmentation_pipeline.py

@@ -43,7 +43,7 @@ def __init__(self, lungmask_inferer: LMInferer) -> None:
         self.compose = Compose([
             LoadImaged(keys=['image'], image_only = False),
             MaskLungs(lungmask_inferer=self.inferer),
-            SaveImaged(keys=['mask'], output_dir='./assets/hosts/', output_postfix='', separate_folder=False)
+            SaveImaged(keys=['mask'], output_dir='./assets/images/hosts/', output_postfix='', separate_folder=False)
         ])
 
     def __call__(self, image_dict) -> Any:
@@ -63,7 +63,7 @@ def generate_json(self) -> None:
         for filename in os.listdir(self.path):
             if filename.endswith((NII_GZ_EXTENSION)):
                 sample_data = {
-                    "host_image": "./assets/source/msd/" + (filename[:filename.index(LABEL_NII_GZ)] + IMAGE_NII_GZ).replace('host_', 'source_'),
+                    "host_image": "./assets/images/hosts/" + (filename[:filename.index(LABEL_NII_GZ)] + IMAGE_NII_GZ).replace('host_', 'source_'),
                     "host_label": self.path + filename
                 }
                 dataset_json.append(sample_data)

synthlung/utils/tumor_isolation_pipeline.py

@@ -64,14 +64,13 @@ def __call__(self, sample:dict) -> Any:
 
 
 class TumorCropPipeline(object):
-    monai.config.BACKEND = "Nibabel"
     def __init__(self) -> None:
         self.compose = Compose([
             LoadImaged(keys=['image', 'label'], image_only = False),
             TumorSeedIsolationd(image_key='image', label_key='label', image_output_key='seed_image', label_output_key='seed_label'),
             RenameSourceToSeed(meta_dict_keys=['seed_image_meta_dict', 'seed_label_meta_dict']),
-            SaveImaged(keys=['seed_image'], output_dir='./assets/seeds/', output_postfix='', separate_folder=False),
-            SaveImaged(keys=['seed_label'], output_dir='./assets/seeds/', output_postfix='', separate_folder=False)
+            SaveImaged(keys=['seed_image'], output_dir='./assets/images/seeds/', output_postfix='', separate_folder=False, writer=monai.data.NibabelWriter),
+            SaveImaged(keys=['seed_label'], output_dir='./assets/images/seeds/', output_postfix='', separate_folder=False, writer=monai.data.NibabelWriter)
         ])
 
     def __call__(self, image_dict) -> None: