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.","Endoscopy Sample App Data",["D",1681919746426],"/api/image/nvidia/clara-holoscan/resources/holoscan_endoscopy_sample_data","bias","explainability","privacy","safetyAndSecurity",[],[277,278,279,280,281],[],[],[],{"_91":297,"_126":298,"_127":299,"_129":130},{"_91":294,"_126":295,"_127":296,"_129":130},{"_91":290,"_126":291,"_127":292,"_129":130},{"_91":286,"_126":287,"_127":288,"_129":130},{"_91":282,"_126":283,"_127":284,"_129":130},"Holoscan","pltfm_clara_holoscan",{"_131":285,"_126":283},"platform","Healthcare","indus_healthcare",{"_131":289,"_126":287},"industry","DL","soln_dl",{"_131":293,"_126":291},"technology","Clara","pltfm_clara",{"_131":285,"_126":295},"AI","soln_ai",{"_131":293,"_126":298},{"_164":1470,"_166":1471},"ngcApps",[1423],"csaApiBaseUrl","https://www.nvidia.com/ai-agents/csa-agent/dcsm","datadogRum",{"_1404":1405,"_1406":1407,"_1408":1409,"_1410":1411,"_1412":311,"_196":335,"_1413":1414,"_1415":51,"_1416":143,"_1417":143,"_1418":1419,"_1420":143,"_1421":1422},"amplitude",{"_1401":1402,"_1007":-7,"_1403":26},"disableJs","environment","prod","featureFlagsBlob","JTdCJTIydGVzdEZsYWclMjIlM0FmYWxzZSUyQyUyMnNob3dTZWFyY2hMYWJlbFNldEZpbHRlcnMlMjIlM0FmYWxzZSUyQyUyMmNoaW5hTmltRGlzdHJpYnV0b3JzUmVkZXNpZ24lMjIlM0F0cnVlJTJDJTIyZW5hYmxlQ3NhV2lkZ2V0JTIyJTNBZmFsc2UlMkMlMjJlbmFibGVQcm9kdWN0R3JvdXBpbmdCeVN1YnNjcmlwdGlvbiUyMiUzQWZhbHNlJTJDJTIyY29udGFpbmVyU2Nhbm5pbmdSZWRlc2lnbk0xJTIyJTNBdHJ1ZSU3RA==","featureFlagsGuarded","isDevLikeEnv","isPlaywright","isMobileDevice","categorizedOrgs",{"_1395":1396,"_1397":1398,"_1399":1400},"currentOrgContext","currentContextRoles",[],"enterpriseOrgs",[],"locale","en","ngcApiOrigin","ngcHelmOrigin","origin","https://catalog.ngc.nvidia.com","usesMocks","theme","year",2026,"1.7.3","defaultSearchSuggestions",{"_1282":235,"_1283":1284,"_1285":1286,"_1287":1288},"announcements",[],"consentState",{"_373":1272,"_1273":143,"_1274":26,"_1275":26,"_1276":1277},"consentConfig",{"_355":356,"_357":358,"_359":360,"_361":362,"_363":364,"_365":366,"_367":368,"_369":370,"_371":372,"_373":374},"showContactAdmin",1783946664183,[],[],[349,350,351,352,353,354],{"_40":55,"_42":65,"_44":159,"_46":-5,"_48":-5,"_50":51},{"_40":55,"_42":64,"_44":159,"_46":-5,"_48":-5,"_50":51},{"_40":55,"_42":62,"_44":159,"_46":-5,"_48":-5,"_50":51},{"_40":55,"_42":60,"_44":159,"_46":-5,"_48":-5,"_50":51},{"_40":52,"_42":53,"_44":159,"_46":-5,"_48":-5,"_50":51},{"_40":41,"_42":43,"_44":45,"_46":47,"_48":188,"_50":51},"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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If you do not allow these cookies, then some or all of these services may not function properly.",[646,647,648,649,650,651,652,653,654,655,656,657,658,659,660,661,662,663,664,665,666],[611,612,613,614,615],{"_414":506,"_91":506,"_90":235,"_387":637},{"_414":472,"_91":472,"_90":235,"_387":632},{"_414":625,"_91":625,"_90":235,"_387":626},{"_414":621,"_91":621,"_90":235,"_387":622},{"_414":616,"_91":616,"_90":235,"_387":617},"nvcrm.my.site.com",[618],{"_251":619,"_420":616,"_421":26,"_90":620},"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",[623],{"_251":624,"_420":621,"_421":26,"_90":455},"edge","github.com",[627,628],{"_251":631,"_420":625,"_421":26,"_90":630},{"_251":629,"_420":625,"_421":26,"_90":630},"logged_in","This domain is owned by Github. 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