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.

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[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.
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[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.
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Model

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.

Inputs

INPUT__0 - Input RGB image (batchsize, height, width, channels)\n
- shape=[1, 512, 512, 3]
- dtype=float32
- range=[0, 255]
\n

Outputs

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

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

License

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

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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.\n\n> [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.\n\n## Model\n\nGiven 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.\n\n_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._\n\n### Inputs\n\n- `INPUT__0` - Input RGB image (batchsize, height, width, channels)\n - `shape=[1, 512, 512, 3]`\n - `dtype=float32`\n - `range=[0, 255]`\n\n### Outputs\n\n- `output_old` - Segmentation output with per-pixel confidence [0,1].\n - `shape=[1, 512, 512]`\n - `dtype=float32`\n - `range=[0, 1]`\n\n## Video Data\n\nThe sample data, provided by [Simula Research Laboratory](https://www.simula.no/), is an .mp4 video of a colonoscopy scene with a polyp identified by the model.\n\n_Note: the .mp4 file must be converted into a GXF tensor file using the `convert_video_to_gxf_entities.py` script on [GitHub](https://github.com/nvidia-holoscan/holoscan-sdk/tree/main/scripts#convert_video_to_gxf_entitiespy) to be used with the `VideoStreamReplayer` Holoscan operator._\n\n## License\n\nRefer to the [license agreement](https://developer.nvidia.com/clara-holoscan-EULA) for use of the sample data.","Colonoscopy Sample App Data",["D",1681919489745],"/api/image/nvidia/clara-holoscan/resources/holoscan_colonoscopy_sample_data","bias","explainability","privacy","safetyAndSecurity",[],[266,267,268,269,270,271],[],[],[],{"_92":290,"_124":291,"_125":292,"_127":128},{"_92":287,"_124":288,"_125":289,"_127":128},{"_92":284,"_124":285,"_125":286,"_127":128},{"_92":280,"_124":281,"_125":282,"_127":128},{"_92":276,"_124":277,"_125":278,"_127":128},{"_92":272,"_124":273,"_125":274,"_127":128},"ML","soln_ml",{"_129":275,"_124":273},"technology","Holoscan","pltfm_clara_holoscan",{"_129":279,"_124":277},"platform","Healthcare","indus_healthcare",{"_129":283,"_124":281},"industry","DL","soln_dl",{"_129":275,"_124":285},"Clara","pltfm_clara",{"_129":279,"_124":288},"AI","soln_ai",{"_129":275,"_124":291},{"_156":1266,"_158":1267},"ngcApps",[1219],"csaApiBaseUrl","https://www.nvidia.com/ai-agents/csa-agent/dcsm","datadogRum",{"_1200":1201,"_1202":1203,"_1204":1205,"_1206":1207,"_1208":304,"_187":330,"_1209":1210,"_1211":51,"_1212":135,"_1213":135,"_1214":1215,"_1216":135,"_1217":1218},"amplitude",{"_1197":1198,"_842":-7,"_1199":26},"disableJs","environment","prod","featureFlagsBlob","JTdCJTIydGVzdEZsYWclMjIlM0FmYWxzZSUyQyUyMnNob3dTZWFyY2hMYWJlbFNldEZpbHRlcnMlMjIlM0FmYWxzZSUyQyUyMmVuYWJsZUNzYVdpZGdldCUyMiUzQWZhbHNlJTJDJTIyZW5hYmxlUHJvZHVjdEdyb3VwaW5nQnlTdWJzY3JpcHRpb24lMjIlM0FmYWxzZSUyQyUyMmNvbnRhaW5lclNjYW5uaW5nUmVkZXNpZ25NMiUyMiUzQWZhbHNlJTdE","featureFlagsGuarded","isDevLikeEnv","isPlaywright","isMobileDevice","categorizedOrgs",{"_1191":1192,"_1193":1194,"_1195":1196},"currentOrgContext","currentContextRoles",[],"enterpriseOrgs",[],"manageableExternalUserGroups",[],"ngcApiOrigin","ngcHelmOrigin","origin","https://catalog.ngc.nvidia.com","usesMocks","theme","year",2026,"now",1788243685768,"1.12.0","defaultSearchSuggestions",{"_1082":228,"_1083":1084,"_1085":1086,"_1087":1088},"announcements",[],"consentState",{"_368":1072,"_1073":135,"_1074":26,"_1075":26,"_1076":1077},"consentConfig",{"_350":351,"_352":353,"_354":355,"_356":357,"_358":359,"_360":361,"_362":363,"_364":365,"_366":367,"_368":369},"showContactAdmin",1788243685769,[],[],[344,345,346,347,348,349],{"_40":55,"_42":66,"_44":63,"_46":-5,"_48":-5,"_50":51},{"_40":55,"_42":64,"_44":63,"_46":-5,"_48":-5,"_50":51},{"_40":55,"_42":60,"_44":63,"_46":-5,"_48":-5,"_50":51},{"_40":55,"_42":56,"_44":63,"_46":-5,"_48":-5,"_50":51},{"_40":52,"_42":53,"_44":63,"_46":-5,"_48":-5,"_50":51},{"_40":41,"_42":43,"_44":63,"_46":-5,"_48":-5,"_50":51},"mainInfoText","We and our third-party partners (including social media, advertising, and analytics partners) use cookies and other tracking technologies to collect, store, monitor, and process certain information about you when you visit our website. 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