Holoscan Sample App Data for AI-based Bone Scoliosis Segmentation

Overview

This resource contains the spine ultrasound segmentation model for scoliosis visualization and measurement developed by Richard Brown at King's College London, as well as a sample ultrasound video.

Model

The AI model expects a gray-scale image of 256 x 256 and outputs a semantic segmentation of the same size with two channels representing

bone contours with hyperechoic lines (foreground),
hyperechoic acoustic shadow (background).

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 - Graysale ultrasound input image\n
- shape=[3, 256, 256]
- dtype=float32
- range=[0,1]
\n

Outputs

OUTPUT__0 - 2-channel semantic segmentation of ultrasound image (hyperechoic lines, hyperechoic acoustic shadow)\n
- shape=[2, 256, 256]
- dtype=float32
- range=[0,1]
\n

Video Data

The sample data, originating from T. Ungi et al. (2020), is an ultrasound video with a spine identified by the model.

Note: the .avi 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 LICENSE.md supplied within, and to the license agreement for use of the sample data.

","resourceVersions",[237,238,239,240],"resourceVersionsTotal","hasMoreVersions","versionMetadata",{"_212":213,"_87":-5,"_43":214,"_215":-5,"_216":217,"_90":-5,"_218":219,"_220":48,"_221":26,"_222":-5,"_223":26,"_224":-5,"_225":26,"_226":227,"_228":26,"_229":227,"_230":26,"_231":227,"_232":26,"_233":227,"_234":26,"_235":236},"versionNotFound","isLoggedIn","latestVersionPath","/orgs/nvidia/clara-holoscan/resources/holoscan_ultrasound_sample_data/-","version","-","imageReference","cliDownloadCommand","ngc registry resource download-version nvidia/clara-holoscan/holoscan_ultrasound_sample_data:20240801","helmFetchCommand","viewAllVersionsPath","/orgs/nvidia/clara-holoscan/resources/holoscan_ultrasound_sample_data/-/version-history","accessCheckResult",{"_211":131},"hasRelatedCollections","context",{"_81":82,"_83":84},1788608597453,[],[],[203,204,205,206,207,208,209],{"_39":54,"_41":63,"_43":56,"_45":57,"_47":150,"_49":50},{"_39":54,"_41":61,"_43":56,"_45":57,"_47":62,"_49":50},{"_39":54,"_41":149,"_43":56,"_45":57,"_47":210,"_49":50},{"_39":54,"_41":59,"_43":56,"_45":57,"_47":148,"_49":50},{"_39":54,"_41":55,"_43":56,"_45":57,"_47":58,"_49":50},{"_39":51,"_41":52,"_43":44,"_45":46,"_47":53,"_49":50},{"_39":40,"_41":42,"_43":44,"_45":46,"_47":48,"_49":50},57,"allowed","versionId","20240801","UPLOAD_COMPLETE","releaseType","createdDate",["D",1722540830064],"totalSizeInBytes",3182982,"totalFileCount","isSigned","malwareScanStatus","isScanned","releaseNotes","hasReleaseNotes","setup","","hasSetup","quickStartGuide","hasQuickStartGuide","advanced","hasAdvanced","performance","hasPerformance","policies",[],{"_74":187,"_242":247,"_215":-5,"_221":26,"_223":26,"_235":246,"_225":26},{"_74":213,"_242":213,"_215":-5,"_221":26,"_223":26,"_235":246,"_225":26},{"_74":244,"_242":244,"_215":-5,"_221":26,"_223":26,"_235":245,"_225":26},{"_74":241,"_242":241,"_215":-5,"_221":26,"_223":26,"_235":243,"_225":26},"20220608","name",[],"20230222",[],[],"Latest (20240801)","nvidia/clara-holoscan/holoscan_ultrasound_sample_data","nvidia/clara-holoscan/resources/holoscan_ultrasound_sample_data","holoscan_ultrasound_sample_data","Holoscan Sample App Data for AI Ultrasound Segmentation for Scoliosis","# Holoscan Sample App Data for AI-based Bone Scoliosis Segmentation\n\n## Overview\n\nThis resource contains the spine ultrasound segmentation model for scoliosis visualization and measurement developed by [Richard Brown at King's College London](https://www.kcl.ac.uk/people/richard-brown-1), as well as a sample ultrasound video.\n\n## Model\n\nThe AI model expects a gray-scale image of `256 x 256` and outputs a semantic segmentation of the same size with two channels representing\n- bone contours with hyperechoic lines (foreground),\n- hyperechoic acoustic shadow (background).\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` - Graysale ultrasound input image\n - `shape=[3, 256, 256]`\n - `dtype=float32`\n - `range=[0,1]`\n\n### Outputs\n\n- `OUTPUT__0` - 2-channel semantic segmentation of ultrasound image (hyperechoic lines, hyperechoic acoustic shadow)\n - `shape=[2, 256, 256]`\n - `dtype=float32`\n - `range=[0,1]`\n\n## Video Data\n\nThe sample data, originating from [T. Ungi et al. (2020)](https://ieeexplore.ieee.org/document/9034149), is an ultrasound video with a spine identified by the model.\n\n_Note: the .avi 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 `LICENSE.md` supplied within, and to the [license agreement](https://developer.nvidia.com/clara-holoscan-EULA) for use of the sample data.","Ultrasound Sample App Data",["D",1722540831536],"/api/image/nvidia/clara-holoscan/resources/holoscan_ultrasound_sample_data","bias","explainability","privacy","safetyAndSecurity",[],[],[],[],[],{"_155":1238,"_157":1239},"ngcApps",[1191],"csaApiBaseUrl","https://www.nvidia.com/ai-agents/csa-agent/dcsm","datadogRum",{"_1172":1173,"_1174":1175,"_1176":1177,"_1178":1179,"_1180":276,"_186":302,"_1181":1182,"_1183":50,"_1184":131,"_1185":131,"_1186":1187,"_1188":131,"_1189":1190},"amplitude",{"_1169":1170,"_814":-7,"_1171":26},"disableJs","environment","prod","featureFlagsBlob","JTdCJTIydGVzdEZsYWclMjIlM0FmYWxzZSUyQyUyMnNob3dTZWFyY2hMYWJlbFNldEZpbHRlcnMlMjIlM0FmYWxzZSUyQyUyMmVuYWJsZUNzYVdpZGdldCUyMiUzQWZhbHNlJTJDJTIyZW5hYmxlUHJvZHVjdEdyb3VwaW5nQnlTdWJzY3JpcHRpb24lMjIlM0FmYWxzZSUyQyUyMmNvbnRhaW5lclNjYW5uaW5nUmVkZXNpZ25NMiUyMiUzQWZhbHNlJTdE","featureFlagsGuarded","isDevLikeEnv","isPlaywright","isMobileDevice","categorizedOrgs",{"_1163":1164,"_1165":1166,"_1167":1168},"currentOrgContext","currentContextRoles",[],"enterpriseOrgs",[],"manageableExternalUserGroups",[],"ngcApiOrigin","ngcHelmOrigin","origin","https://catalog.ngc.nvidia.com","usesMocks","theme","year",2026,"now",1788608597268,"1.15.0","defaultSearchSuggestions",{"_1054":227,"_1055":1056,"_1057":1058,"_1059":1060},"announcements",[],"consentState",{"_340":1044,"_1045":131,"_1046":26,"_1047":26,"_1048":1049},"consentConfig",{"_322":323,"_324":325,"_326":327,"_328":329,"_330":331,"_332":333,"_334":335,"_336":337,"_338":339,"_340":341},"showContactAdmin",1788608597269,[],[],[316,317,318,319,320,321],{"_39":54,"_41":63,"_43":60,"_45":-5,"_47":-5,"_49":50},{"_39":54,"_41":61,"_43":60,"_45":-5,"_47":-5,"_49":50},{"_39":54,"_41":59,"_43":60,"_45":-5,"_47":-5,"_49":50},{"_39":54,"_41":55,"_43":60,"_45":-5,"_47":-5,"_49":50},{"_39":51,"_41":52,"_43":60,"_45":-5,"_47":-5,"_49":50},{"_39":40,"_41":42,"_43":60,"_45":-5,"_47":-5,"_49":50},"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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