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","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",[286,287,288,289,290,291],"policyLabels",[],"systemLabels",[],"products",[],"hasNimMetadata",{"_255":314,"_293":315,"_295":316,"_297":87},{"_255":311,"_293":312,"_295":313,"_297":87},{"_255":308,"_293":309,"_295":310,"_297":87},{"_255":304,"_293":305,"_295":306,"_297":87},{"_255":300,"_293":301,"_295":302,"_297":87},{"_255":292,"_293":294,"_295":296,"_297":87},"ML","value","soln_ml","filter",{"_298":299,"_293":294},"category","field","technology","Holoscan","pltfm_clara_holoscan",{"_298":303,"_293":301},"platform","Healthcare","indus_healthcare",{"_298":307,"_293":305},"industry","DL","soln_dl",{"_298":299,"_293":309},"Clara","pltfm_clara",{"_298":303,"_293":312},"AI","soln_ai",{"_298":299,"_293":315},{"_165":1297,"_167":1298},"ngcApps",[1250],"csaApiBaseUrl","https://www.nvidia.com/ai-agents/csa-agent/dcsm","datadogRum",{"_1231":1232,"_1233":1234,"_1235":1236,"_1237":1238,"_1239":328,"_109":356,"_1240":1241,"_1242":59,"_1243":90,"_1244":90,"_1245":1246,"_1247":90,"_1248":1249},"amplitude",{"_1228":1229,"_867":-7,"_1230":35},"disableJs","environment","prod","featureFlagsBlob","JTdCJTIydGVzdEZsYWclMjIlM0FmYWxzZSUyQyUyMnNob3dTZWFyY2hMYWJlbFNldEZpbHRlcnMlMjIlM0FmYWxzZSUyQyUyMmVuYWJsZUNzYVdpZGdldCUyMiUzQWZhbHNlJTJDJTIyZW5hYmxlUHJvZHVjdEdyb3VwaW5nQnlTdWJzY3JpcHRpb24lMjIlM0FmYWxzZSUyQyUyMmNvbnRhaW5lclNjYW5uaW5nUmVkZXNpZ25NMiUyMiUzQWZhbHNlJTdE","featureFlagsGuarded","isDevLikeEnv","isPlaywright","isMobileDevice","categorizedOrgs",{"_1222":1223,"_1224":1225,"_1226":1227},"currentOrgContext","currentContextRoles",[],"enterpriseOrgs",[],"manageableExternalUserGroups",[],"locale","en","ngcApiOrigin","ngcHelmOrigin","origin","https://catalog.ngc.nvidia.com","usesMocks","theme","year",2026,"now",1786463369618,"1.11.0","defaultSearchSuggestions",{"_1107":143,"_1108":1109,"_1110":1111,"_1112":1113},"announcements",[],"consentState",{"_393":1097,"_1098":35,"_1099":35,"_1100":90,"_1101":1102},"consentConfig",{"_375":376,"_377":378,"_379":380,"_381":382,"_383":384,"_385":386,"_387":388,"_389":390,"_391":392,"_393":394},"showContactAdmin",[],[],[369,370,371,372,373,374],{"_49":63,"_51":74,"_53":160,"_55":-5,"_57":-5,"_58":59},{"_49":63,"_51":70,"_53":160,"_55":-5,"_57":-5,"_58":59},{"_49":63,"_51":68,"_53":160,"_55":-5,"_57":-5,"_58":59},{"_49":63,"_51":64,"_53":160,"_55":-5,"_57":-5,"_58":59},{"_49":60,"_51":61,"_53":54,"_55":56,"_57":16,"_58":59},{"_49":50,"_51":52,"_53":54,"_55":56,"_57":16,"_58":59},"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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	Files	Compressed Size
20230222Selected	4	21.89 MB	March 17, 2023 UTC
20221031	8	581.67 MB	November 29, 2022 UTC
v1.1	9	581.64 MB	September 12, 2022 UTC