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",[],[274,275,276,277,278,279],[],[],[],{"_88":298,"_123":299,"_124":300,"_126":127},{"_88":295,"_123":296,"_124":297,"_126":127},{"_88":292,"_123":293,"_124":294,"_126":127},{"_88":288,"_123":289,"_124":290,"_126":127},{"_88":284,"_123":285,"_124":286,"_126":127},{"_88":280,"_123":281,"_124":282,"_126":127},"ML","soln_ml",{"_128":283,"_123":281},"technology","Holoscan","pltfm_clara_holoscan",{"_128":287,"_123":285},"platform","Healthcare","indus_healthcare",{"_128":291,"_123":289},"industry","DL","soln_dl",{"_128":283,"_123":293},"Clara","pltfm_clara",{"_128":287,"_123":296},"AI","soln_ai",{"_128":283,"_123":299},{"_163":1276,"_165":1277},"ngcApps",[1229],"csaApiBaseUrl","https://www.nvidia.com/ai-agents/csa-agent/dcsm","datadogRum",{"_1210":1211,"_1212":1213,"_1214":1215,"_1216":1217,"_1218":312,"_194":336,"_1219":1220,"_1221":50,"_1222":140,"_1223":140,"_1224":1225,"_1226":140,"_1227":1228},"amplitude",{"_1207":1208,"_848":-7,"_1209":26},"disableJs","environment","prod","featureFlagsBlob","JTdCJTIydGVzdEZsYWclMjIlM0FmYWxzZSUyQyUyMnNob3dTZWFyY2hMYWJlbFNldEZpbHRlcnMlMjIlM0FmYWxzZSUyQyUyMmVuYWJsZUNzYVdpZGdldCUyMiUzQWZhbHNlJTJDJTIyZW5hYmxlUHJvZHVjdEdyb3VwaW5nQnlTdWJzY3JpcHRpb24lMjIlM0FmYWxzZSUyQyUyMmNvbnRhaW5lclNjYW5uaW5nUmVkZXNpZ25NMSUyMiUzQXRydWUlN0Q=","featureFlagsGuarded","isDevLikeEnv","isPlaywright","isMobileDevice","categorizedOrgs",{"_1201":1202,"_1203":1204,"_1205":1206},"currentOrgContext","currentContextRoles",[],"enterpriseOrgs",[],"locale","en","ngcApiOrigin","ngcHelmOrigin","origin","https://catalog.ngc.nvidia.com","usesMocks","theme","year",2026,"1.10.0","defaultSearchSuggestions",{"_1088":236,"_1089":1090,"_1091":1092,"_1093":1094},"announcements",[],"consentState",{"_374":1078,"_1079":140,"_1080":26,"_1081":26,"_1082":1083},"consentConfig",{"_356":357,"_358":359,"_360":361,"_362":363,"_364":365,"_366":367,"_368":369,"_370":371,"_372":373,"_374":375},"showContactAdmin",1784688983853,[],[],[350,351,352,353,354,355],{"_39":54,"_41":62,"_43":63,"_45":-5,"_47":-5,"_49":50},{"_39":54,"_41":60,"_43":63,"_45":-5,"_47":-5,"_49":50},{"_39":54,"_41":59,"_43":63,"_45":-5,"_47":-5,"_49":50},{"_39":54,"_41":55,"_43":63,"_45":-5,"_47":-5,"_49":50},{"_39":51,"_41":52,"_43":63,"_45":-5,"_47":-5,"_49":50},{"_39":40,"_41":42,"_43":44,"_45":46,"_47":229,"_49":50},"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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