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Colonoscopy Sample App Data

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Holoscan Sample App Data for AI Colonoscopy Segmentation of Polyps



Latest Version



April 19, 2023

Compressed Size

21.89 MB

Holoscan Sample App Data for AI Colonoscopy Segmentation of Polyps

This resource contains a segmentation model for the identification of polyps during colonoscopies trained on the Kvasir-SEG dataset [1], using the ColonSegNet model architecture [2], as well as a sample surgical video.

[1] 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.

[2] 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]
    • dtype=float32
    • range=[0, 255]


  • output_old - Segmentation output with per-pixel confidence [0,1].
    • shape=[1, 512, 512]
    • dtype=float32
    • range=[0, 1]

Video Data

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 script on GitHub to be used with the VideoStreamReplayer Holoscan operator.


Refer to the license agreement for use of the sample data.