Merge pull request #61 from MahdiAll99/dev

Documentation update

MEDimage/__init__.py

@@ -14,7 +14,7 @@
 logging.getLogger(__name__).addHandler(stream_handler)
 
 __author__ = "MEDomicsLab consortium"
-__version__ = "0.9.0"
+__version__ = "0.9.4"
 __copyright__ = "Copyright (C) MEDomicsLab consortium"
 __license__ = "GNU General Public License 3.0"
 __maintainer__ = "MAHDI AIT LHAJ LOUTFI"

MEDimage/learning/DataCleaner.py

@@ -51,7 +51,7 @@ def cut_off_missing_per_sample(self, var_of_type: List[str], missing_cutoff : fl
         """
         # Initialization
         n_observation, n_features = self.df_features.shape
-        empty_vec = np.zeros(n_observation, dtype=np.int)
+        empty_vec = np.zeros(n_observation, dtype=int)
         data = self.df_features[var_of_type]
         empty_vec += data.isna().sum(axis=1).values
 

MEDimage/processing/segmentation.py

@@ -190,7 +190,7 @@ def get_sep_roi_names(name_roi_in: str,
     ind_start = strfind(string=name_roi_in, pattern="{")
     n_roi = len(ind_start)
     ind_stop = strfind(string=name_roi_in, pattern="}")
-    ind_keep = np.ones(n_delim, dtype=np.bool)
+    ind_keep = np.ones(n_delim, dtype=bool)
     for d in np.arange(n_delim):
         for r in np.arange(n_roi):
              # Thus not indise a ROI name

MEDimage/wrangling/DataManager.py

@@ -76,7 +76,7 @@ def __init__(
             path_save: Union[Path, str] = None,
             path_save_checks: Union[Path, str] = None,
             path_pre_checks_settings: Union[Path, str] = None,
-            save: bool = False,
+            save: bool = True,
             n_batch: int = 2
     ) -> None:
         """Constructor of the class DataManager.
@@ -144,7 +144,6 @@ def __init__(
         self.__studies = []
         self.__institutions = []
         self.__scans = []
-        self.__warned = False
 
     def __find_uid_cell_index(self, uid: Union[str, List[str]], cell: List[str]) -> List: 
         """Finds the cell with the same `uid`. If not is present in `cell`, creates a new position
@@ -202,10 +201,11 @@ def __associate_rt_stuct(self) -> None:
         """
         print('--> Associating all RT objects to imaging volumes')
         n_rs = len(self.__dicom.stack_path_rs)
+        self.__dicom.stack_series_rs = list(dict.fromkeys(self.__dicom.stack_series_rs))
         if n_rs:
             for i in trange(0, n_rs):
-                try: # PUT ALL THE DICOM PATHS WITH THE SAME UID IN THE SAME PATH LIST
-                    self.__dicom.stack_series_rs = list(set(self.__dicom.stack_series_rs))
+                try: 
+                    # PUT ALL THE DICOM PATHS WITH THE SAME UID IN THE SAME PATH LIST
                     ind_series_id = self.__find_uid_cell_index(
                                                         self.__dicom.stack_series_rs[i], 
                                                         self.__dicom.cell_series_id)
@@ -275,6 +275,8 @@ def __read_all_dicoms(self) -> None:
                         except:
                             frame_uid = info.FrameOfReferenceUID
                         self.__dicom.stack_frame_rs += [frame_uid]
+                    else:
+                        print("Modality not supported: ", info.Modality)
 
                 except Exception as e:
                     print(f'Error while reading: {file}, error: {e}\n')
@@ -551,9 +553,18 @@ def process_all_niftis(self) -> List[MEDscan]:
         Returns:
             List[MEDscan]: List of MEDscan instances.
         """
+
+        # Reading all NIfTI files
         self.__read_all_niftis()
+
+        # Create the MEDscan instances
         print('--> Reading all NIfTI objects (imaging volumes & masks) to create MEDscan classes')
+        list_instances = []
         for file in tqdm(self.__nifti.stack_path_images):
+            # Assert the list of instances does not exceed the a size of 10
+            if len(list_instances) >= 10:
+                print('The number of MEDscan instances exceeds 10, please consider saving the instances')
+                break
             # INITIALIZE MEDscan INSTANCE AND UPDATE ATTRIBUTES
             medscan = MEDscan()
             medscan.patientID = os.path.basename(file).split("_")[0]
@@ -577,6 +588,8 @@ def process_all_niftis(self) -> List[MEDscan]:
             # SAVE MEDscan INSTANCE
             if self.save and self.paths._path_save:
                 save_MEDscan(medscan, self.paths._path_save)
+            else:
+                list_instances.append(medscan)
 
             # Update the path to the created instances
             name_save = self.__get_MEDscan_name_save(medscan)
@@ -611,6 +624,9 @@ def process_all_niftis(self) -> List[MEDscan]:
                         "naming convention 'study-institution-id' (Ex: Glioma-TCGA-001)")
         print('DONE')
 
+        if list_instances:
+            return list_instances
+
     def update_from_csv(self, path_csv: Union[str, Path] = None) -> None:
         """Updates the class from a given CSV and summarizes the processed scans again according to it.
 

README.md

@@ -5,7 +5,6 @@
 [![PyPI - Python Version](https://img.shields.io/badge/python-3.8%20|%203.9%20|%203.10-blue)](https://www.python.org/downloads/release/python-380/)
 [![TestPyPI - version](https://img.shields.io/badge/pypi-v0.2.0-blue)](https://test.pypi.org/project/medimage-pkg/0.2.0/)
 [![Continuous Integration](https://github.com/MahdiAll99/MEDimage/actions/workflows/python-app.yml/badge.svg)](https://github.com/MahdiAll99/MEDimage/actions/workflows/python-app.yml)
-[![Upload Python Package](https://github.com/MahdiAll99/MEDimage/actions/workflows/python-publish.yml/badge.svg)](https://github.com/MahdiAll99/MEDimage/actions/workflows/python-publish.yml)
 [![Documentation Status](https://readthedocs.org/projects/medimage/badge/?version=latest)](https://medimage.readthedocs.io/en/latest/?badge=latest)
 [![License: GPL-3](https://img.shields.io/badge/license-GPLv3-blue)](LICENSE)
 [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/MahdiAll99/MEDimage/blob/main/notebooks/tutorial/DataManager-Tutorial.ipynb)
@@ -20,7 +19,7 @@
   * [5. Tutorials](#5-tutorials)
   * [6. IBSI Standardization](#6-ibsi-standardization)
     * [IBSI Chapter 1](#ibsi-chapter-1)
-    * [IBSI Chapter 2 (In progress)](#ibsi-chapter-2-in-progress)
+    * [IBSI Chapter 2](#ibsi-chapter-2)
   * [7. Acknowledgement](#7-acknowledgement)
   * [8. Authors](#8-authors)
   * [9. Statement](#9-statement)
@@ -39,7 +38,7 @@ The MEDimage package requires *Python 3.8* or more. If you don't have it install
 ### Package installation
 You can easily install the ``MEDimage`` package from PyPI using:
 ```
-pip install MEDimage
+pip install medimage-pkg
 ```
 
 For more installation options (Conda, Poetry...) check out the [installation documentation](https://medimage.readthedocs.io/en/latest/Installation.html).
@@ -111,8 +110,8 @@ The image biomarker standardization initiative ([IBSI](https://theibsi.github.io
       - **Phase 1**: [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/MahdiAll99/MEDimage/blob/main/notebooks/ibsi/ibsi1p1.ipynb)
       - **Phase 2**: [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/MahdiAll99/MEDimage/blob/main/notebooks/ibsi/ibsi1p2.ipynb)
 
-  - ### IBSI Chapter 2 (In progress)
-      [The IBSI chapter 2](https://theibsi.github.io/ibsi2/) was launched in June 2020 and is still in progress. It is dedicated to the standardization of commonly used imaging filters in radiomic studies. We have created two [jupyter notebooks](https://github.com/MahdiAll99/MEDimage/tree/main/notebooks/ibsi) for each phase of the chapter and made them available for the users to run the IBSI tests for themselves and validate image filtering and image biomarker calculations from filter response maps. The tests can also be explored in interactive Colab notebooks that are directly accessible here: 
+  - ### IBSI Chapter 2
+      [The IBSI chapter 2](https://theibsi.github.io/ibsi2/) was launched in June 2020 and reached completion in February 2024. It is dedicated to the standardization of commonly used imaging filters in radiomic studies. We have created two [jupyter notebooks](https://github.com/MahdiAll99/MEDimage/tree/main/notebooks/ibsi) for each phase of the chapter and made them available for the users to run the IBSI tests for themselves and validate image filtering and image biomarker calculations from filter response maps. The tests can also be explored in interactive Colab notebooks that are directly accessible here: 
 
       - **Phase 1**: [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/MahdiAll99/MEDimage/blob/main/notebooks/ibsi/ibsi2p1.ipynb)
       - **Phase 2**: [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/MahdiAll99/MEDimage/blob/main/notebooks/ibsi/ibsi2p2.ipynb)
@@ -177,4 +176,4 @@ Here's what the license entails:
 9. The software author or license can not be held liable for any damages inflicted by the software.
 ```
 
-More information on about the [LICENSE can be found here](https://github.com/MahdiAll99/MEDimage/blob/main/LICENSE.md)
+More information on about the [LICENSE can be found here](https://github.com/MEDomics-UdeS/MEDimage/blob/main/LICENSE.md)

docs/Installation.rst

@@ -11,7 +11,7 @@ Install via pip
 ---------------
 ``MEDimage`` is available on PyPi for installation via ``pip`` which allows you to install the package in one step ::
 
-    pip install MEDimage
+    pip install medimage-pkg
 
 Install from source
 -------------------

docs/tutorials.rst

@@ -3,18 +3,18 @@ Instructions
 
 Download dataset
 ----------------
-    In all tutorials, we use an open-access dataset containing medical images for various cancer types (Glioma, sarcoma...) 
-    and different imaging modalities (MR, CT, and PET). The dataset has been pre-processed to adhere to package norms.
+    In all tutorials, we use open-access data of medical images of various cancer types (Glioma, sarcoma...) 
+    and with different imaging modalities (MR, CT, and PET). All data has been pre-processed to adhere to package norms.
 
-    To download the dataset (~3.2 GB) and organize it in your local workspace, run the following command in your terminal from 
-    the package parent folder ::
+    In order to run the tutorials, you must first download the dataset. We recommend downloading only a subset (~347 MB) instead of the 
+    full dataset (~3.2 GB). To do so, run the following command in your terminal from the package parent folder: ::
     
-       python scripts/download_data.py --full-sts
+       python scripts/download_data.py --subset
     
     .. note::
-        The dataset is large, and options are available to download only a subset. For more information, run:
-        
-           python scripts/download_data.py --help
+        To download the full dataset, simply run the following command in your terminal from the package parent folder: ::
+
+           python scripts/download_data.py --full-sts
 
 CSV file
 --------

environment.yml

@@ -8,7 +8,7 @@ dependencies:
   - nibabel
   - pandas<2.0.0
   - pillow
-  - pydicom
+  - pydicom>1.2.0, <=1.3.0
   - pywavelets
   - scikit-image
   - scipy
@@ -27,7 +27,7 @@ dependencies:
     - numpyencoder
     - protobuf
     - pycaret
-    - ray
+    - ray[default]
     - scikit_image
     - SimpleITK
     - scikit_learn

pyproject.toml

@@ -1,6 +1,6 @@
 [tool.poetry]
 name = "medimage-pkg"
-version = "0.9.0"
+version = "0.9.4"
 description = "MEDimage is a Python package for processing and extracting features from medical images"
 authors = ["MEDomics Consortium <medomics.info@gmail.com>"]
 license = "GPL-3.0"
@@ -15,38 +15,38 @@ packages = [ {include = "MEDimage"} ]
 
 [tool.poetry.dependencies]
 python = ">=3.8.0,<=3.10"
-isort = "5.10.1"
-ipykernel = "^6.15.1"
-ipywidgets = "^8.0.1"
-jupyter = "^1.0.0"
-matplotlib = "3.7.0"
-networkx = "2.8.5"
-neuroCombat = "0.2.12"
-nibabel = "3.2.2"
-nilearn = "0.10.1"
-numpy = "1.22.4"
-numpyencoder = "0.3.0"
-pandas = "1.5.3"
-Pillow = "9.2.0"
-protobuf = "3.20.*"
-pycaret = "3.0.4"
-pydicom = "1.2.2"
-PyWavelets = "1.1.1"
-ray = "2.5.1"
-scikit-image = "0.18.2"
-scipy = "1.7.0"
-setuptools = "62.2.0"
-SimpleITK = "2.1.1.2"
-scikit_learn = "1.2.2"
-seaborn = "0.13.2"
-Sphinx = "7.2.6"
+numpy = "*"
+isort = "*"
+ipykernel = "*"
+ipywidgets = "*"
+jupyter = "*"
+matplotlib = "*"
+networkx = "*"
+neuroCombat = "*"
+nibabel = "*"
+nilearn = "*"
+numpyencoder = "*"
+pandas = "<2.0.0"
+Pillow = "*"
+protobuf = "*"
+pycaret = "*"
+pydicom = ">1.2.0, <=1.3.0"
+PyWavelets = "*"
+ray = { version = "*", extras = ["default"] }
+scikit_image = "*"
+scipy = "*"
+setuptools = "*"
+SimpleITK = "*"
+scikit_learn = "*"
+seaborn = "*"
+Sphinx = "*"
 sphinx-carousel = "1.2.0"
 sphinx-jsonschema = "1.19.1"
 sphinx-rtd-dark-mode = "1.2.4"
-tqdm = "4.65.0"
-wget = "3.2"
-tabulate = "0.9.0"
-xgboost = "1.7.6"
+tqdm = "*"
+wget = "*"
+tabulate = "*"
+xgboost = "*"
 
 [tool.poetry.dev-dependencies]
 

requirements.txt

@@ -13,9 +13,9 @@ pandas<2.0.0
 Pillow
 protobuf
 pycaret
-pydicom
+pydicom>1.2.0, <=1.3.0
 PyWavelets
-ray
+ray[default]
 scikit_image
 scipy
 setuptools

scripts/download_data.py

@@ -21,7 +21,7 @@ def main(full_sts: bool, subset: bool) -> None:
     print("\n================ Downloading first part of data ================")
     try:
         wget.download(
-        "https://sandbox.zenodo.org/record/1106515/files/MEDimage-Dataset-No-STS.zip?download=1",
+        "https://sandbox.zenodo.org/records/45640/files/MEDimage-Dataset-No-STS.zip?download=1",
         out=os.getcwd())
     except Exception as e:
         print("MEDimage-Dataset-No-STS.zip download failed, error:", e)
@@ -114,29 +114,29 @@ def main(full_sts: bool, subset: bool) -> None:
         print("\n================ Downloading second part of data ================")
         try:
             wget.download(
-            "https://sandbox.zenodo.org/record/1106515/files/MEDimage-Dataset-STS-McGill-001-005.zip?download=1",
+            "https://sandbox.zenodo.org/records/45644/files/MEDimage-STS-Dataset-Subset.zip?download=1",
             out=os.getcwd())
             pass
         except Exception as e:
-            print("MEDimage-Dataset-STS-McGill-001-005.zip download failed, error:", e)
+            print("MEDimage-STS-Dataset-Subset.zip download failed, error:", e)
 
         # unzip data
         print("\n================ Extracting second part of data  ================")
         try:
-            with zipfile.ZipFile(os.getcwd() + "/MEDimage-Dataset-STS-McGill-001-005.zip", 'r') as zip_ref:
+            with zipfile.ZipFile(os.getcwd() + "/MEDimage-STS-Dataset-Subset.zip", 'r') as zip_ref:
                 zip_ref.extractall(os.getcwd())
                 # remove zip file after extraction
-            os.remove(os.getcwd() + "/MEDimage-Dataset-STS-McGill-001-005.zip")
+            os.remove(os.getcwd() + "/MEDimage-STS-Dataset-Subset.zip")
         except Exception as e:
-            print("MEDimage-Dataset-STS-McGill-001-005.zip extraction failed, error:", e)
+            print("MEDimage-STS-Dataset-Subset.zip extraction failed, error:", e)
 
         # organize data in the right folder
         print("\n================== Organizing data in folders  ==================")
         try:
-            shutil.move(os.getcwd() + "/STS-McGill-001-005", 
+            shutil.move(os.getcwd() + "/MEDimage-STS-Dataset-Subset", 
                     os.getcwd() + "/notebooks" + "/tutorial" + "/data" + "/DICOM-STS")
         except Exception as e:
-            print("Failed to move STS-McGill-001-005 folder, error:", e)
+            print("Failed to move MEDimage-STS-Dataset-Subset folder, error:", e)
 
 
 if __name__ == "__main__":

setup.py

@@ -14,7 +14,7 @@
 
 setup(
     name="MEDimage",
-    version="0.9.0",
+    version="0.9.4",
     author="MEDomics consortium",
     author_email="medomics.info@gmail.com",
     description="Python Open-source package for medical images processing and radiomic features extraction",