Flexible Structure

The datasets are presented in a searchable list instead of predefined project structure.

Semantic Search

Use the semantic search to find datasets you need.

Organize and Sharing

Create folders and produce a data structure you like. Share it and collaborate.

Controlled Access

Choose the access level for each dataset individually.

Application Ready

Use the full featured API to connect to the repository from your software.

Standard File Formats

DICOM, ITK images, CDISC, STL, Statismo and ontology based meta-data.

Our Competences

Storing medical image data requires special knowledg. SICAS offers a unique combination of competence in acquiring and storing medical imgaes, in processing and visualising data for research and applications in medicine.


Anonymisation of patient information and image features like face soft tissue.

Data Storage

Certified, efficient, and innovative data center based in Switzerland.

Research expertise

Sound expertise and broad experience in research support and collaboration management.


Acquisition of hig quality image data and curation by experts.


Controlled distribution of medical image data.


Anatomy based search to find the correct images.

Get Started

With the Website


Go the to the Upload Page and add a dataset to the upload queue


Unpublished Data

After upload, your dataset is listed on the unpublished page


Modifiy Meta-Data

Add value to your data by adding meta-data



Follow the steps and set meta-info, permission and related data to publish the data


Published Data

Your uploaded and published data is listed in MyData


Search For Data

Use -Symbol (@Body) to find anatomical structures


Organize by Folder

Copy data into your folder structure


Download a Folder

Select the folder to retrieve your collected data


With the API

Please consider developing on the demo (https://demo.virtualskeleton.ch) server before you interact with the live system.

API Reference Python 3 API Connector

Chunked file upload

Python code

## import libs
from pathlib import Path
import connectVSD
## connect to demo
api = connectVSD.VSDConnecter()
## define filepath
fp = Path('C:' + os.sep, 'test', 'test.nii')
## Define chunk size to eg. 8 MB if you dont want to use the default 4MB
chunk = 1024 * 4096 * 2
## upload using the chunkFileUpload
obj = api.chunkFileUpload(fp, chunksize = chunk)
## print the selfUrl of the generated object

Console output

uploading part 1 of 3
uploaded part 1 of 3
uploading part 2 of 3
uploaded part 2 of 3
uploading part 3 of 3
uploaded part 3 of 3

Download a folder

Python code

from pathlib import Path
import connectVSD
# connect to the demo API
api = connectVSD.VSDConnecter()
## search for the test folder and retrieve the folder object
folder = api.getFolderByName('test') 
for obje in folder.containedObjects:
    ## get each object in the folder
    obj = api.getObject(obje['selfUrl'])
    ## define the filepath of the downloaded file
    fp = Path(obj.name).with_suffix('.zip')
    ## download the file
    api.downloadZip(obj.downloadUrl, fp)




Institutions using SMIR