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
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]
OUTPUT__0- 2-channel semantic segmentation of ultrasound image (hyperechoic lines, hyperechoic acoustic shadow)
shape=[2, 256, 256]
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
convert_video_to_gxf_entities.py script on GitHub to be used with the
VideoStreamReplayer Holoscan operator.
LICENSE.md supplied within, and to the license agreement for use of the sample data.