NGC | Catalog
CatalogResourcesUltrasound Sample App Data

Ultrasound Sample App Data

Logo for Ultrasound Sample App Data
Holoscan Sample App Data for AI Ultrasound Segmentation for Scoliosis
Latest Version
April 19, 2023
Compressed Size
3.56 MB

Holoscan Sample App Data for AI-based Bone Scoliosis Segmentation


This resource contains the spine ultrasound segmentation model for scoliosis visualization and measurement developed by Richard Brown at King's College London, as well as a sample ultrasound video.


The AI model expects a gray-scale image of 256 x 256 and outputs a semantic segmentation of the same size with two channels representing

  • bone contours with hyperechoic lines (foreground),
  • hyperechoic acoustic shadow (background).

Note: The provided model is in ONNX format. It will automatically be converted into a TensorRT model (.engine) the first time it is processed by a Holoscan application.


  • INPUT__0 - Graysale ultrasound input image
    • shape=[3, 256, 256]
    • dtype=float32
    • range=[0,1]


  • OUTPUT__0 - 2-channel semantic segmentation of ultrasound image (hyperechoic lines, hyperechoic acoustic shadow)
    • shape=[2, 256, 256]
    • dtype=float32
    • range=[0,1]

Video Data

The sample data, originating from T. Ungi et al. (2020), is an ultrasound video with a spine identified by the model.

Note: the .avi file must be converted into a GXF tensor file using the script on GitHub to be used with the VideoStreamReplayer Holoscan operator.


Refer to supplied within, and to the license agreement for use of the sample data.