Python Library to handle Input / Output conversion in Dicom <=> Convolutional Neural Network
Project description
library-DICOM
Features :
- Description of Series content in a huge dataset of DICOM (output JSON descriptor for each series containings main DICOM tags).
- Conversion Dicom to Nifti
- PET : Conversion Bqml/Counts to SUV and SUL
Roadmap :
- Read RTSS to generate Mask
- Generate RTSS from Mask
- PT / CT fusion in 4D array np array
#Maintainer : Salim Kanoun #Contributors : Thomas Trouillard, Wendy Revailler
To refactor :
- conversion of a nifti mask to a ROI in a DICOM RTSTRUCT
- ROI integration to an existing RTSTRUCT
- generation empty RTSTRUCT from PET,CT or similar set of DICOM images
TODO :
- conversion DICOM RTSTRUCT to mask in nifti format
Release history Release notifications | RSS feed
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
Source Distribution
dicom_to_cnn-0.38.tar.gz
(39.7 kB
view details)
File details
Details for the file dicom_to_cnn-0.38.tar.gz.
File metadata
- Download URL: dicom_to_cnn-0.38.tar.gz
- Upload date:
- Size: 39.7 kB
- Tags: Source
- Uploaded using Trusted Publishing? No
- Uploaded via: twine/3.1.1 pkginfo/1.4.2 requests/2.22.0 setuptools/45.2.0 requests-toolbelt/0.8.0 tqdm/4.40.2 CPython/3.8.5
File hashes
| Algorithm | Hash digest | |
|---|---|---|
| SHA256 |
a9fc8ff6aebc918557df478a65e03721b8956a721c863ea54020c8370e007ff7
|
|
| MD5 |
bc3517eae199addbe053f0a019d3be75
|
|
| BLAKE2b-256 |
1d898e00ab45b1320e8ee3c82399067e5094f0953d2f2da9fdfe3aa88cb655ee
|
