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",[294,295,296,297,298],"policyLabels",[],"systemLabels",[],"products",[],"hasNimMetadata",{"_263":318,"_300":319,"_302":320,"_304":88},{"_263":315,"_300":316,"_302":317,"_304":88},{"_263":311,"_300":312,"_302":313,"_304":88},{"_263":307,"_300":308,"_302":309,"_304":88},{"_263":299,"_300":301,"_302":303,"_304":88},"Holoscan","value","pltfm_clara_holoscan","filter",{"_305":306,"_300":301},"category","field","platform","Healthcare","indus_healthcare",{"_305":310,"_300":308},"industry","DL","soln_dl",{"_305":314,"_300":312},"technology","Clara","pltfm_clara",{"_305":306,"_300":316},"AI","soln_ai",{"_305":314,"_300":319},{"_170":1493,"_172":1494},"ngcApps",[1446],"csaApiBaseUrl","https://www.nvidia.com/ai-agents/csa-agent/dcsm","datadogRum",{"_1427":1428,"_1429":1430,"_1431":1432,"_1433":1434,"_1435":332,"_110":356,"_1436":1437,"_1438":60,"_1439":91,"_1440":91,"_1441":1442,"_1443":91,"_1444":1445},"amplitude",{"_1424":1425,"_1028":-7,"_1426":35},"disableJs","environment","prod","featureFlagsBlob","JTdCJTIydGVzdEZsYWclMjIlM0FmYWxzZSUyQyUyMnNob3dTZWFyY2hMYWJlbFNldEZpbHRlcnMlMjIlM0FmYWxzZSUyQyUyMmNoaW5hTmltRGlzdHJpYnV0b3JzUmVkZXNpZ24lMjIlM0F0cnVlJTJDJTIyZW5hYmxlQ3NhV2lkZ2V0JTIyJTNBZmFsc2UlMkMlMjJlbmFibGVQcm9kdWN0R3JvdXBpbmdCeVN1YnNjcmlwdGlvbiUyMiUzQWZhbHNlJTJDJTIyY29udGFpbmVyU2Nhbm5pbmdSZWRlc2lnbk0xJTIyJTNBdHJ1ZSU3RA==","featureFlagsGuarded","isDevLikeEnv","isPlaywright","isMobileDevice","categorizedOrgs",{"_1418":1419,"_1420":1421,"_1422":1423},"currentOrgContext","currentContextRoles",[],"enterpriseOrgs",[],"locale","en","ngcApiOrigin","ngcHelmOrigin","origin","https://catalog.ngc.nvidia.com","usesMocks","theme","year",2026,"1.7.3","defaultSearchSuggestions",{"_1303":148,"_1304":1305,"_1306":1307,"_1308":1309},"announcements",[],"consentState",{"_394":1293,"_1294":35,"_1295":35,"_1296":91,"_1297":1298},"consentConfig",{"_376":377,"_378":379,"_380":381,"_382":383,"_384":385,"_386":387,"_388":389,"_390":391,"_392":393,"_394":395},"showContactAdmin",1783946599861,[],[],[370,371,372,373,374,375],{"_49":63,"_51":74,"_53":165,"_55":-5,"_57":-5,"_59":60},{"_49":63,"_51":70,"_53":165,"_55":-5,"_57":-5,"_59":60},{"_49":63,"_51":68,"_53":165,"_55":-5,"_57":-5,"_59":60},{"_49":63,"_51":64,"_53":165,"_55":-5,"_57":-5,"_59":60},{"_49":61,"_51":62,"_53":54,"_55":56,"_57":58,"_59":60},{"_49":50,"_51":52,"_53":54,"_55":56,"_57":58,"_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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These cookies enable us to provide enhanced website functionality and personalization as well as make the marketing messages we send to you more relevant to your interests. If you do not allow these cookies, then some or all of these services may not function properly.",[667,668,669,670,671,672,673,674,675,676,677,678,679,680,681,682,683,684,685,686,687],[632,633,634,635,636],{"_435":527,"_263":527,"_229":148,"_408":658},{"_435":493,"_263":493,"_229":148,"_408":653},{"_435":646,"_263":646,"_229":148,"_408":647},{"_435":642,"_263":642,"_229":148,"_408":643},{"_435":637,"_263":637,"_229":148,"_408":638},"nvcrm.my.site.com",[639],{"_117":640,"_441":637,"_442":35,"_229":641},"BrowserId","If you know anything about this cookie and its uses that are not specific to a particular site please add your contributions.","nvidia.partners",[644],{"_117":645,"_441":642,"_442":35,"_229":476},"edge","github.com",[648,649],{"_117":652,"_441":646,"_442":35,"_229":651},{"_117":650,"_441":646,"_442":35,"_229":651},"logged_in","This domain is owned by Github. 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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
20221121Selected	8	909.27 MB	November 29, 2022 UTC
20220608	9	909.36 MB	June 17, 2022 UTC