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.","displayName","Colonoscopy Sample App Data",["D",1681919489745],"publisher","NVIDIA","latestVersion","versions","collectionCounts","imageUrl","/api/image/nvidia/clara-holoscan/resources/holoscan_colonoscopy_sample_data","rawImageUrl","https://avatars.githubusercontent.com/u/119712212","canGuestDownload","latestVersionSizeInBytes","licenseTerms","isMultinodeEnabled","isMultiArchitecture","bias","explainability","privacy","safetyAndSecurity","featureLabels",[],"displayLabels",[259,260,261,262,263,264],"policyLabels",[],"systemLabels",[],"products",[],"hasNimMetadata",{"_227":288,"_266":289,"_268":290,"_270":271},{"_227":285,"_266":286,"_268":287,"_270":271},{"_227":282,"_266":283,"_268":284,"_270":271},{"_227":278,"_266":279,"_268":280,"_270":271},{"_227":274,"_266":275,"_268":276,"_270":271},{"_227":265,"_266":267,"_268":269,"_270":271},"ML","value","soln_ml","filter",{"_272":273,"_266":267},"category","label","field","technology","Holoscan","pltfm_clara_holoscan",{"_272":277,"_266":275},"platform","Healthcare","indus_healthcare",{"_272":281,"_266":279},"industry","DL","soln_dl",{"_272":273,"_266":283},"Clara","pltfm_clara",{"_272":277,"_266":286},"AI","soln_ai",{"_272":273,"_266":289},{"_125":1465,"_127":1466},"ngcApps",[1418],"csaApiBaseUrl","https://www.nvidia.com/ai-agents/csa-agent/dcsm","datadogRum",{"_1400":1401,"_1402":1403,"_1404":1405,"_1406":1407,"_1408":302,"_152":326,"_1409":1410,"_1398":56,"_1411":104,"_1412":104,"_1413":1414,"_1415":104,"_1416":1417},"amplitude",{"_1396":1397,"_1398":56,"_998":-7,"_1399":31},"disableJs","environment","prod","featureFlagsBlob","JTdCJTIydGVzdEZsYWclMjIlM0FmYWxzZSUyQyUyMnNob3dTZWFyY2hMYWJlbFNldEZpbHRlcnMlMjIlM0FmYWxzZSUyQyUyMmNoaW5hTmltRGlzdHJpYnV0b3JzUmVkZXNpZ24lMjIlM0F0cnVlJTJDJTIyZW5hYmxlQ3NhV2lkZ2V0JTIyJTNBZmFsc2UlMkMlMjJlbmFibGVQcm9kdWN0R3JvdXBpbmdCeVN1YnNjcmlwdGlvbiUyMiUzQWZhbHNlJTJDJTIyY29udGFpbmVyU2Nhbm5pbmdSZWRlc2lnbk0xJTIyJTNBZmFsc2UlN0Q=","featureFlagsGuarded","isDevLikeEnv","isPlaywright","isMobileDevice","categorizedOrgs",{"_1390":1391,"_1392":1393,"_1394":1395},"currentOrgContext","currentContextRoles",[],"enterpriseOrgs",[],"locale","en","ngcApiOrigin","ngcHelmOrigin","origin","https://catalog.ngc.nvidia.com","usesMocks","theme","year",2026,"1.4.0","defaultSearchSuggestions",{"_1273":196,"_1274":1275,"_1276":1277,"_1278":1279},"announcements",[],"consentState",{"_364":1263,"_1264":31,"_1265":31,"_1266":104,"_1267":1268},"consentConfig",{"_346":347,"_348":349,"_350":351,"_352":353,"_354":355,"_356":357,"_358":359,"_360":361,"_362":363,"_364":365},"showContactAdmin",1782934248133,[],[],[340,341,342,343,344,345],{"_45":60,"_47":70,"_49":120,"_51":-5,"_53":-5,"_55":56},{"_45":60,"_47":68,"_49":120,"_51":-5,"_53":-5,"_55":56},{"_45":60,"_47":67,"_49":120,"_51":-5,"_53":-5,"_55":56},{"_45":60,"_47":61,"_49":120,"_51":-5,"_53":-5,"_55":56},{"_45":57,"_47":58,"_49":120,"_51":-5,"_53":-5,"_55":56},{"_45":46,"_47":48,"_49":120,"_51":-5,"_53":-5,"_55":56},"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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Name	Size	Updated	Actions
colon_exam_720x576.mp4	2.57 MB	March 17, 2023 UTC	curl wget Download
colon.onnx	19.13 MB	March 17, 2023 UTC	curl wget Download
NVIDIA Colonoscopy Software License (9Sept2022).docx	41.6 KB	March 17, 2023 UTC	curl wget Download
NVIDIA-Clara-Holoscan-SDK-EULA.pdf	155.83 KB	March 17, 2023 UTC	curl wget Download