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-0.25.tar.gz
(38.2 kB
view details)
File details
Details for the file dicom_to_cnn-0.25.tar.gz.
File metadata
- Download URL: dicom_to_cnn-0.25.tar.gz
- Upload date:
- Size: 38.2 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 |
178faee5901e713fa723b4e030e9fc6d4354cd7d8186bd85dff281fe93360a4b
|
|
| MD5 |
e78a2b8716a5df55410294258fc204ad
|
|
| BLAKE2b-256 |
f44c866da4f994ad9b79e8af4d4b17a57433924d0eb1ab90522618d9aa8d8643
|
