Holoscan Sample App Data for AI-based Endoscopy Tool Tracking

This resource contains the convolutional LSTM model for tool tracking in laparoscopic videos by Nwoye et. al [1], and a sample surgical video.

\n
[1] Nwoye, C.I., Mutter, D., Marescaux, J. and Padoy, N., 2019. Weakly supervised convolutional LSTM approach for tool tracking in laparoscopic videos. International journal of computer assisted radiology and surgery, 14(6), pp.1059-1067
\n

Model

The AI model for instrument tracking in endoscopy is an LSTM model which given an RGB image of 854 x 480 provides

per-instrument detection probability,
per-instrument detected tooltip location,
semantic segmentation of the instruments.\nEach pixel stores 7 labels.

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

data_ph:0 - Input RGB image ( batchsize, height, width, channels)\n
- shape=[1, 480, 854, 3]
- dtype=float32
- range=[0, 255]
\n
cellstate_ph:0 - LSTM hidden state tensor 0\n
- shape=[1, 60, 107, 7]
- dtype=float32
\n
hiddenstate_ph:0 - LSTM hidden state tensor 1\n
- shape=[1, 60, 107, 7]
- dtype=float32
\n

Outputs

Model/net_states:0 - LSTM output state tensor. Output of frame N is input for frame N + 1.\n
- shape=[1, 60, 107, 7]
- dtype=float32
\n
Model/net_states:0 - LSTM hidden state tensor. Output of frame N is input for frame N + 1.\n
- shape=[1, 60, 107, 7]
- dtype=float32
\n
probs:0 - Per instrument detection probability\n
- shape=[1, 7]
- dtype=float32
- range=[0,1]
\n
Localize/scaled_coords:0 - Per-instrument (x,y) detected tooltip location\n
- shape=[1, 2, 7]
- dtype=float32
\n
Localize_1/binary_masks:0 - image with per instrument segmentation mask. Each pixel stores 7 labels\n
- shape=[1, 60, 107, 7]
- dtype=float32
\n

Video Data

The sample data, kindly provided by Research Group Camma, IHU Strasbourg and University of Strasbourg, is a surgical video that includes all 7 instruments classes supported by the tool tracking model. It's in a raw H264 format.

Note: the .h264 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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Weakly supervised convolutional LSTM approach for tool tracking in laparoscopic videos. International journal of computer assisted radiology and surgery, 14(6), pp.1059-1067](https://link.springer.com/article/10.1007/s11548-019-01958-6)\n\n## Model\n\nThe AI model for instrument tracking in endoscopy is an LSTM model which given an RGB image of `854 x 480` provides\n- per-instrument detection probability,\n- per-instrument detected tooltip location,\n- semantic segmentation of the instruments.\nEach pixel stores 7 labels.\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- `data_ph:0` - Input RGB image ( batchsize, height, width, channels)\n - `shape=[1, 480, 854, 3]`\n - `dtype=float32`\n - `range=[0, 255]`\n- `cellstate_ph:0` - LSTM hidden state tensor 0\n - `shape=[1, 60, 107, 7]`\n - `dtype=float32`\n- `hiddenstate_ph:0` - LSTM hidden state tensor 1\n - `shape=[1, 60, 107, 7]`\n - `dtype=float32`\n\n### Outputs\n\n- `Model/net_states:0` - LSTM output state tensor. Output of frame `N` is input for frame `N + 1`.\n - `shape=[1, 60, 107, 7]`\n - `dtype=float32`\n- `Model/net_states:0` - LSTM hidden state tensor. Output of frame `N` is input for frame `N + 1`.\n - `shape=[1, 60, 107, 7]`\n - `dtype=float32`\n- `probs:0` - Per instrument detection probability\n - `shape=[1, 7]`\n - `dtype=float32`\n - `range=[0,1]`\n- `Localize/scaled_coords:0` - Per-instrument (x,y) detected tooltip location\n - `shape=[1, 2, 7]`\n - `dtype=float32`\n- `Localize_1/binary_masks:0` - image with per instrument segmentation mask. Each pixel stores 7 labels\n - `shape=[1, 60, 107, 7]`\n - `dtype=float32`\n\n## Video Data\n\nThe sample data, kindly provided by [Research Group Camma](http://camma.u-strasbg.fr), [IHU Strasbourg](https://www.ihu-strasbourg.eu/) and [University of Strasbourg](https://en.unistra.fr/), is a surgical video that includes all 7 instruments classes supported by the tool tracking model. It's in a raw H264 format.\n\n_Note: the .h264 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","Endoscopy Sample App Data",["D",1681919746426],"publisher","NVIDIA","latestVersion","collectionCounts","imageUrl","/api/image/nvidia/clara-holoscan/resources/holoscan_endoscopy_sample_data","rawImageUrl","https://avatars.githubusercontent.com/u/119712212","canGuestDownload","latestVersionSizeInBytes","licenseTerms","isMultinodeEnabled","isMultiArchitecture","bias","explainability","privacy","safetyAndSecurity","featureLabels",[],"displayLabels",[293,294,295,296,297],"policyLabels",[],"systemLabels",[],"products",[],"hasNimMetadata",{"_262":317,"_299":318,"_301":319,"_303":87},{"_262":314,"_299":315,"_301":316,"_303":87},{"_262":310,"_299":311,"_301":312,"_303":87},{"_262":306,"_299":307,"_301":308,"_303":87},{"_262":298,"_299":300,"_301":302,"_303":87},"Holoscan","value","pltfm_clara_holoscan","filter",{"_304":305,"_299":300},"category","field","platform","Healthcare","indus_healthcare",{"_304":309,"_299":307},"industry","DL","soln_dl",{"_304":313,"_299":311},"technology","Clara","pltfm_clara",{"_304":305,"_299":315},"AI","soln_ai",{"_304":313,"_299":318},{"_169":1492,"_171":1493},"ngcApps",[1445],"csaApiBaseUrl","https://www.nvidia.com/ai-agents/csa-agent/dcsm","datadogRum",{"_1426":1427,"_1428":1429,"_1430":1431,"_1432":1433,"_1434":331,"_109":355,"_1435":1436,"_1437":60,"_1438":90,"_1439":90,"_1440":1441,"_1442":90,"_1443":1444},"amplitude",{"_1423":1424,"_1027":-7,"_1425":35},"disableJs","environment","prod","featureFlagsBlob","JTdCJTIydGVzdEZsYWclMjIlM0FmYWxzZSUyQyUyMnNob3dTZWFyY2hMYWJlbFNldEZpbHRlcnMlMjIlM0FmYWxzZSUyQyUyMmNoaW5hTmltRGlzdHJpYnV0b3JzUmVkZXNpZ24lMjIlM0F0cnVlJTJDJTIyZW5hYmxlQ3NhV2lkZ2V0JTIyJTNBZmFsc2UlMkMlMjJlbmFibGVQcm9kdWN0R3JvdXBpbmdCeVN1YnNjcmlwdGlvbiUyMiUzQWZhbHNlJTJDJTIyY29udGFpbmVyU2Nhbm5pbmdSZWRlc2lnbk0xJTIyJTNBdHJ1ZSU3RA==","featureFlagsGuarded","isDevLikeEnv","isPlaywright","isMobileDevice","categorizedOrgs",{"_1417":1418,"_1419":1420,"_1421":1422},"currentOrgContext","currentContextRoles",[],"enterpriseOrgs",[],"locale","en","ngcApiOrigin","ngcHelmOrigin","origin","https://catalog.ngc.nvidia.com","usesMocks","theme","year",2026,"1.7.3","defaultSearchSuggestions",{"_1302":147,"_1303":1304,"_1305":1306,"_1307":1308},"announcements",[],"consentState",{"_393":1292,"_1293":90,"_1294":35,"_1295":35,"_1296":1297},"consentConfig",{"_375":376,"_377":378,"_379":380,"_381":382,"_383":384,"_385":386,"_387":388,"_389":390,"_391":392,"_393":394},"showContactAdmin",1783943401653,[],[],[369,370,371,372,373,374],{"_49":63,"_51":73,"_53":164,"_55":-5,"_57":-5,"_59":60},{"_49":63,"_51":70,"_53":164,"_55":-5,"_57":-5,"_59":60},{"_49":63,"_51":68,"_53":164,"_55":-5,"_57":-5,"_59":60},{"_49":63,"_51":64,"_53":164,"_55":-5,"_57":-5,"_59":60},{"_49":61,"_51":62,"_53":164,"_55":-5,"_57":-5,"_59":60},{"_49":50,"_51":52,"_53":164,"_55":-5,"_57":-5,"_59":60},"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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This cookie is used to distinguish unique users by assigning a randomly generated number as a client identifier. It is included in each page request in a site and used to calculate visitor, session and campaign data for the sites analytics reports. By default it is set to expire after 2 years, although this is customisable by website owners.","research.nvidia.com","_uetvid","This is a cookie utilised by Microsoft Bing Ads and is a tracking cookie. It allows us to engage with a user that has previously visited our website.","s_pltp","This cookie is typically associated with Adobe Analytics (formerly SiteCatalyst). It is used to identify the previous link clicked within the site, which helps in analyzing site usage patterns.","s_ecid","Adobe Analytics cookie containing the the Adobe Experience Cloud 1st party visitor id if the Analytics AMCV visitor id is not found.","This cookie name is used as a long-lived unique identifier of the visitor on the website. 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	Files	Compressed Size
20230222	4	47.27 MB	March 17, 2023 UTC
20230128	8	895.41 MB	January 30, 2023 UTC
20221121	8	909.27 MB	November 29, 2022 UTC
20220608Selected	9	909.36 MB	June 17, 2022 UTC