This resource contains a segmentation model for the identification of polyps during colonoscopies trained on the Kvasir-SEG dataset , using the ColonSegNet model architecture , as well as a sample surgical video.
 Jha, Debesh, Pia H. Smedsrud, Michael A. Riegler, Pål Halvorsen, Thomas de Lange, Dag Johansen, and Håvard D. Johansen, "Kvasir-seg: A segmented polyp dataset" Proceedings of the International Conference on Multimedia Modeling, pp. 451-462, 2020.
 Jha D, Ali S, Tomar NK, Johansen HD, Johansen D, Rittscher J, Riegler MA, Halvorsen P. Real-Time Polyp Detection, Localization and Segmentation in Colonoscopy Using Deep Learning. IEEE Access. 2021 Mar 4;9:40496-40510. doi: 10.1109/ACCESS.2021.3063716. PMID: 33747684; PMCID: PMC7968127.
Given an RGB image of
512 x 512, this model provides semantic segmentation of the polyps. Each pixel stores a confidence score of [0,1] for polyp presence.
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- Input RGB image (batchsize, height, width, channels)
shape=[1, 512, 512, 3]
output_old- Segmentation output with per-pixel confidence [0,1].
shape=[1, 512, 512]
The sample data, provided by Simula Research Laboratory, is an .mp4 video of a colonoscopy scene with a polyp identified by the model.
Note: the .mp4 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.
Refer to the license agreement for use of the sample data.