orcaz 0.1.3.7

ORCA (Optimized Registration through Conditional Adversarial networks)

ORCA: Optimized Registration through Conditional Adversarial networks

Requirements ✅

For an optimal experience with ORCA, ensure the following:

• Operating System: ORCA runs smoothly on Windows, Mac, or Linux.
• Memory: At least 32GB of RAM ensures ORCA operates without a hitch.
• GPU: For blazing-fast predictions, an NVIDIA GPU comes highly recommended. But if you don't have one, fret not! ORCA will still get the job done, just at a more leisurely pace. For training new models, you must have a GPU!
• Python: Version 3.9.2 or above. We like to stay updated!

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Installation Guide 🛠️

Navigating the installation process is a breeze. Just follow the steps below:

For Linux and MacOS 🐧🍏

1. Create a Python environment, for example, 'orca-env'.

python3 -m venv orca-env

2. Activate your environment.

source orca-env/bin/activate  # for Linux
source orca-env/bin/activate  # for MacOS

3. Install ORCA.

pip install orcaz

For Windows 🪟

1. Set up a Python environment, say 'orca-env'.

python -m venv orca-env

2. Get your environment up and running.

.\orca-env\Scripts\activate

3. Hop over to the PyTorch website and fetch the right version for your system. This step is crucial!
4. Finish up by installing ORCA.

pip install orcaz

Usage Guide 📚

Command-line tool for data folder processing :computer:

orcaz -d <path_to_patient_dir> -m <mode>

Here <path_to_patient_dir> refers to the directory containing your subject's PET and CT images. Where <mode> is the name of the mode for which we want to use the tool, from the available options.

train: Yes we can !! Orca can be used to train your own models as a generic cGAN paltform. More instructions for that to follow !

generate: This option will force orca to generate a synthetic CT from your PET data and stop before coregistration.

coreg: Option to generate synethic CT and perform the coregistration pipeline with an output of the co-registered CT. ORCA in its full glory !!

Using ORCA requires your data to be structured according to specific conventions. ORCA supports both DICOM and NIFTI formats.

Required Directory Structure 🌳

Please structure your dataset as follows:

EXAMPLE_Data_folder/
├── S1
│   ├── S1_CT
│   │   ├── xyz_1.dcm
│   │   ├── xyz_2.dcm
│   │   ├── .
│   │   ├── .
│   │   ├── .
│   │   └── xyz_532.dcm
│   └── S1_FDG_NAC_PT
│       ├── xyz_1.dcm
│       ├── xyz_2.dcm
│       ├── .
│       ├── .
│       ├── .
│       └── xyz_532.dcm
├── S2
│   ├── S2_CT
│   │   ├── xyz_1.dcm
│   │   ├── xyz_2.dcm
│   │   ├── .
│   │   ├── .
│   │   ├── .
│   │   └── xyz_532.dcm
│   └── S2_FDG_NAC_PT
│       ├── xyz_1.dcm
│       ├── xyz_2.dcm
│       ├── .
│       ├── .
│       ├── .
│       └── xyz_532.dcm
├── S3
│   ├── S3_CT.nii
│   └── S3_FDG_NAC_PT.nii
├── S4
│   ├── S4_CT.nii.gz
│   └── S4_FDG_NAC_PT.nii.gz

In all these cases, ORCA can be executed on the directories one by one

orcaz -d S1 -m coreg
orcaz -d S2 -m coreg
orcaz -d S3 -m coreg  

Note: If the necessary naming conventions are not followed, ORCA will not process the data in the directory.

Naming Conventions for files 📝

There is none! Currently orca requires the naming of the subject subfoders inlcude particular names. The patient identifier can be placed in the start of each subfolder

For instance, S1_CT and S1_FDG_NAC_PT, or S1_CT and S1_FDG_AC_PT.
S2_CT and S2_PSMA_NAC_PT.
S3_CT and S3_FACBC_NAC_PT.
S4_CT and S4_DOTA_NAC_PT.
S5_CT and S5_AGNOSTIC_NAC_PT.

Output

After successful completion, the co-registered CT is saved as dicom data in ORCA_CT_DICOM.

Intermediate images and warp files are stored within the ORCA-VXX-YYYY-MM-DD-HH-MM-SS folder

S1
├── CT
├── NAC_FDG_PET
├── ORCA_CT_DICOM
├── ORCA-V01-2023-09-28-00-02-52