picai-prep 2.1.6

PICAI Preprocessing

Preprocessing Utilities for 3D Medical Image Archives

This repository contains standardized functions to process 3D medical images and image archives —with its processing strategy being geared towards clinically significant prostate cancer (csPCa) detection in MRI. It is used for the official preprocessing pipeline of the PI-CAI challenge.

Supported Conversions

• DICOM Archive → MHA Archive
• MHA Archive → nnU-Net Raw Data Archive
• nnU-Net Raw Data Archive → nnDetection Raw Data Archive

Note: the MHA Archive → nnU-Net Raw Data Archive conversion includes resampling sequences to a shared voxel spacing (per sample). Optionally, this step can resample all samples to a uniform voxel spacing and/or take a centre crop.

Installation

picai_prep is pip-installable:

pip install picai_prep

Usage

Our preprocessing pipeline consists of four independent stages: DICOM Archive → MHA Archive → nnU-Net Raw Data Archive → nnDetection Raw Data Archive. All three conversion steps between these four stages can be performed independently. See below for documentation on each step.

DICOM Archive → MHA Archive

Conversion from DICOM Archive → MHA Archive is controlled through a configuration file, which lists all DICOM sequences. This configuration file specifies how different sequences should be selected from the available DICOM sequences. An excerpt of the format is given below:

"mappings": {
    "t2w": {
        "SeriesDescription": ["t2_tse_tra"]
    },
},
"options": {
	"num_threads": 4,
	"verify_dicom_filenames": True,
	"allow_duplicates": False
},
"archive": [
    {
        "patient_id": "ProstateX-0000",
        "study_id": "07-07-2011-NA-MR prostaat kanker detectie WDSmc MCAPRODETW-05711",
        "path": "ProstateX-0000/07-07-2011-NA-MR prostaat kanker detectie WDSmc MCAPRODETW-05711/3.000000-t2tsesag-87368"
    },
]

This excerpt includes all available options:

num_threads: int, default: 4
	number of threads to use for multiprocessing
verify_dicom_filenames: bool, default: True
	explicitly verify dicom filenames as a sanity check
allow_duplicates: bool, default: False
	when multiple series apply to a mapping, convert all

Full configuration file for this excerpt, can be found here. It can also be generated, as follows:

from picai_prep.examples.dcm2mha.sample_archive import generate_dcm2mha_settings

generate_dcm2mha_settings(
    archive_dir="/path/to/picai_public_images/",
    output_path="/path/to/picai_public_images/dcm2mha_settings.json"
)

Using this configuration file, the DICOM Archive → MHA Archive conversion can be performed using Python:

from picai_prep import Dicom2MHAConverter

archive = Dicom2MHAConverter(
    input_dir="/input/path/to/dicom/archive",
    output_dir="/output/path/to/mha/archive",
    dcm2mha_settings="/path/to/dcm2mha_settings.json",
)
archive.convert()

Or from the command line:

python -m picai_prep dcm2mha --input /input/path/to/dicom/archive --output /output/path/to/mha/archive --json /path/to/dcm2mha_settings.json

For more examples of DICOM Archive structures, see examples/dcm2mha/.

MHA Archive → nnU-Net Raw Data Archive

Conversion from the MHA Archive format to the nnU-Net Raw Data Archive format is controlled through a configuration file, which lists all input sequences (and optionally, annotations). This configuration file specifies which sequences should be selected from the available (MHA) sequences. An excerpt of the format is given below:

"dataset_json": {
    "task": "Task100_test",
    ...
},
"preprocessing": {
    "matrix_size": [20, 160, 160],
    "spacing": [3.0, 0.5, 0.5]
},
"archive": [
    {
        "patient_id": "ProstateX-0000",
        "study_id": "07-07-2011",
        "scan_paths": [
            "ProstateX-0000/ProstateX-0000_07-07-2011_t2w.mha",
            "ProstateX-0000/ProstateX-0000_07-07-2011_adc.mha",
            "ProstateX-0000/ProstateX-0000_07-07-20111_hbv.mha"
        ],
        "annotation_path": "ProstateX-0000_07-07-2011.nii.gz"
    },
]

Full configuration file for this except, can be found here. It can also be generated, as follows:

python -m picai_prep mha2nnunet_settings --structure picai_archive --input /input/images/ --annotations /input/labels/csPCa_lesion_delineations/human_expert/resampled --json /workdir/mha2nnunet_settings.json

Or from Python:

from picai_prep.examples.mha2nnunet.picai_archive import generate_mha2nnunet_settings

generate_mha2nnunet_settings(
    archive_dir="/input/images/",
    annotations_dir="/input/labels/csPCa_lesion_delineations/human_expert/resampled
    output_path="/workdir/mha2nnunet_settings.json",
)

The --annotations (command line) or annotations_dir (Python) parameter will check if the annotation is present in the specified folder. If not, the item will be skipped.

Using this configuration file, the MHA Archive → nnU-Net Raw Data Archive conversion can be performed using Python:

from picai_prep import MHA2nnUNetConverter

archive = MHA2nnUNetConverter(
    scans_dir="/input/path/to/mha/archive",
    annotations_dir="/input/path/to/annotations",  # defaults to input_path
    output_dir="/output/path/to/nnUNet_raw_data",
    mha2nnunet_settings="/path/to/mha2nnunet_settings.json",
)
archive.convert()

Or from the command line:

python -m picai_prep mha2nnunet --input /input/path/to/mha/archive --annotations /input/path/to/annotations --output /output/path/to/nnUNet_raw_data --json /path/to/mha2nnunet_settings.json

Or using a Docker container:

docker run -v /path/to/picai_data:/input \
           -v /path/to/nnUNet_raw_data:/output/ \
           picai_nnunet python -m picai_prep mha2nnunet \
           --input /input/images \
           --annotations /input/labels/csPCa_lesion_delineations/human_expert/resampled \
           --output /output/nnUNet_raw_data \
           --json /input/mha2nnunet_settings.json

For more examples of MHA Archive structures, see examples/mha2nnunet/.

nnU-Net Raw Data Archive → nnDetection Raw Data Archive

For certain applications, the nnU-Net and nnDetection raw data archive formats can be converted to each other. Conversion from the nnDetection to nnU-Net structure can always be performed (see nnDetection's documentation). However, conversion from nnU-Net to nnDetection structure requires object instances to be non-connected and non-overlapping, such that they can be correctly identified as separate, individual objects. If this assumption holds true, nnU-Net Raw Data Archive → nnDetection Raw Data Archive conversion can be performed using Python:

from picai_prep import nnunet2nndet

nnunet2nndet(
    nnunet_raw_data_path="/input/path/to/nnUNet_raw_data/Task100_test",
    nndet_raw_data_path="/output/path/to/nnDet_raw_data/Task100_test",
)

Or from the command line:

python -m picai_prep nnunet2nndet --input /input/path/to/nnUNet_raw_data/Task100_test --output /output/path/to/nnDet_raw_data/Task100_test

What is a 'DICOM Archive'?

With a DICOM archive we mean a dataset that comprises the scans as DICOM (.dcm) files, such as the ProstateX dataset. Typically, such an archive is structured in the following way:

/path/to/archive/
├── [patient UID]/
    ├── [study UID]/
        ├── [series UID]/
            ├── slice-1.dcm
            ...
            ├── slice-n.dcm

In a DICOM archive multiple sequences (such as axial T2-weighted scans) can exist, and each patient can have multiple studies. A single study can even have multiple instances of the same sequence, for example a repeated transversal T2-weighted scan when the first scan experienced motion blur artefacts.

What is an 'MHA Archive'?

With an MHA archive we mean a dataset that comprises the scans as MHA (.mha) files, such as the PI-CAI dataset. In case of the PI-CAI Challenge: Public Training and Development Dataset, the archive is structured in the following way (after extracting the zips):

/path/to/archive/
├── [patient UID]/
    ├── [patient UID]_[study UID]_[modality].mha
    ...

For the PI-CAI dataset, the available modalities are t2w (axial T2-weighted scan), adc (apparent diffusion coefficient map), hbv (calculated high b-value scan), sag (sagittal T2-weighted scan) and cor (coronal T2-weighted scan).

Reference

If you are using this codebase or some part of it, please cite the following article:

● A. Saha, J. J. Twilt, J. S. Bosma, B. van Ginneken, D. Yakar, M. Elschot, J. Veltman, J. J. Fütterer, M. de Rooij, H. Huisman, "Artificial Intelligence and Radiologists at Prostate Cancer Detection in MRI: The PI-CAI Challenge (Study Protocol)", DOI: 10.5281/zenodo.6667655

BibTeX:

@ARTICLE{PICAI_BIAS,
    author = {Anindo Saha, Jasper J. Twilt, Joeran S. Bosma, Bram van Ginneken, Derya Yakar, Mattijs Elschot, Jeroen Veltman, Jurgen Fütterer, Maarten de Rooij, Henkjan Huisman},
    title  = {{Artificial Intelligence and Radiologists at Prostate Cancer Detection in MRI: The PI-CAI Challenge (Study Protocol)}}, 
    year   = {2022},
    doi    = {10.5281/zenodo.6667655}
}

Managed By

Diagnostic Image Analysis Group, Radboud University Medical Center, Nijmegen, The Netherlands

Contact Information

• Joeran Bosma: [e-mail]
• Stan Noordman: [e-mail]
• Anindo Saha: [e-mail]
• Henkjan Huisman: [e-mail]