Vessel3DDL

Automated Multiscale 3D Feature Learning for Vessels Segmentation in Thorax CT Images

Data

The VESSEL 12 data may be downloaded from: https://grand-challenge.org/site/vessel12/ and should be stored at ./Data/VESSEL12/

├── Data
│    └── VESSEL12
│        ├── VESSEL12_01-05
│        ├── VESSEL12_01-20_Lungmasks
│        ├── VESSEL12_06-10
│        ├── VESSEL12_11-15
│        ├── VESSEL12_16-20
│        └── VESSEL12_ExampleScans
│            ├── Annotations
│            ├── Lungmasks
│            └── Scans
├── LICENSE
├── README.md
└── scripts
    ├── config.py
    ├── config.pyc
    ├── LearnClassifier
    ├── LearnDictionary
    ├── UseClassifier
    └── utils

Structure

The entire processing pipeline for the VESSEL12 data is set up in the config.py file.

LearnDictionary

Execute the scripts in following order:

  1. ExtractPatches.py
  2. LearnDictionary.py

    LearnClassifier

    Execute the scripts in following order:

  3. ExtractXy_multithread.py
  4. ConcatenateXy.py
  5. TrainClassifier.py or MakeMeasurements.py

    Usage

    Once the dictionary and classifier are learned, they can by uses on a given volume.
    Execute the scripts in following order:

  6. UseClassifier.py
  7. ViewResults.py

Citation

Please cite these paper in your publications if it helps your research:

@inproceedings{konopczynski2019automated,
    title={Automated multiscale 3D feature learning for vessels segmentation in Thorax CT images},
    author={Konopczy{\'n}ski, Tomasz and Kr{\"o}ger, Thorben and Zheng, Lei and Garbe, Christoph S and Hesser, J{\"u}rgen},
    booktitle={2016 IEEE Nuclear Science Symposium, Medical Imaging Conference and Room-Temperature Semiconductor Detector Workshop (NSS/MIC/RTSD)},
    pages={1--3},
    year={2019},
    organization={IEEE}
}

Link to the paper: