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",[239,240,241,242],"resourceVersionsTotal","hasMoreVersions","versionMetadata",{"_214":192,"_90":-5,"_44":215,"_216":-5,"_217":218,"_93":-5,"_219":220,"_221":222,"_223":26,"_224":-5,"_225":26,"_226":-5,"_227":26,"_228":229,"_230":26,"_231":229,"_232":26,"_233":229,"_234":26,"_235":229,"_236":26,"_237":238},"version","20230222","imageReference","cliDownloadCommand","ngc registry resource download-version nvidia/clara-holoscan/holoscan_ultrasound_sample_data:20230222","helmFetchCommand","viewAllVersionsPath","/orgs/nvidia/clara-holoscan/resources/holoscan_ultrasound_sample_data/20230222/version-history","accessCheckResult",{"_213":143},"hasRelatedCollections","context",{"_84":85,"_86":87},[],[],[207,208,209,210,211,212],{"_40":55,"_42":65,"_44":57,"_46":58,"_48":66,"_50":51},{"_40":55,"_42":63,"_44":57,"_46":58,"_48":64,"_50":51},{"_40":55,"_42":62,"_44":57,"_46":58,"_48":61,"_50":51},{"_40":55,"_42":60,"_44":57,"_46":58,"_48":61,"_50":51},{"_40":52,"_42":53,"_44":159,"_46":-5,"_48":-5,"_50":51},{"_40":41,"_42":43,"_44":45,"_46":47,"_48":49,"_50":51},"allowed","versionId","UPLOAD_COMPLETE","releaseType","createdDate",["D",1679067813517],"totalSizeInBytes",3735121,"totalFileCount",4,"isSigned","malwareScanStatus","isScanned","releaseNotes","hasReleaseNotes","setup","","hasSetup","quickStartGuide","hasQuickStartGuide","advanced","hasAdvanced","performance","hasPerformance","policies",[],{"_77":249,"_244":250,"_216":-5,"_223":26,"_225":26,"_237":248,"_227":26},{"_77":247,"_244":247,"_216":-5,"_223":26,"_225":26,"_237":248,"_227":26},{"_77":192,"_244":192,"_216":-5,"_223":26,"_225":26,"_237":246,"_227":26},{"_77":243,"_244":243,"_216":-5,"_223":26,"_225":26,"_237":245,"_227":26},"20220608","name",[],[],"20240801",[],"-","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",3182982,"bias","explainability","privacy","safetyAndSecurity",[],[],[],[],[],{"_164":1439,"_166":1440},"ngcApps",[1392],"csaApiBaseUrl","https://www.nvidia.com/ai-agents/csa-agent/dcsm","datadogRum",{"_1374":1375,"_1376":1377,"_1378":1379,"_1380":1381,"_1382":280,"_191":304,"_1383":1384,"_1372":51,"_1385":143,"_1386":143,"_1387":1388,"_1389":143,"_1390":1391},"amplitude",{"_1370":1371,"_1372":51,"_976":-7,"_1373":26},"disableJs","environment","prod","featureFlagsBlob","JTdCJTIydGVzdEZsYWclMjIlM0FmYWxzZSUyQyUyMnNob3dTZWFyY2hMYWJlbFNldEZpbHRlcnMlMjIlM0FmYWxzZSUyQyUyMmNoaW5hTmltRGlzdHJpYnV0b3JzUmVkZXNpZ24lMjIlM0F0cnVlJTJDJTIyZW5hYmxlQ3NhV2lkZ2V0JTIyJTNBZmFsc2UlMkMlMjJlbmFibGVQcm9kdWN0R3JvdXBpbmdCeVN1YnNjcmlwdGlvbiUyMiUzQWZhbHNlJTJDJTIyY29udGFpbmVyU2Nhbm5pbmdSZWRlc2lnbk0xJTIyJTNBZmFsc2UlN0Q=","featureFlagsGuarded","isDevLikeEnv","isPlaywright","isMobileDevice","categorizedOrgs",{"_1364":1365,"_1366":1367,"_1368":1369},"currentOrgContext","currentContextRoles",[],"enterpriseOrgs",[],"locale","en","ngcApiOrigin","ngcHelmOrigin","origin","https://catalog.ngc.nvidia.com","usesMocks","theme","year",2026,"1.4.0","defaultSearchSuggestions",{"_1251":229,"_1252":1253,"_1254":1255,"_1256":1257},"announcements",[],"consentState",{"_343":1241,"_1242":143,"_1243":26,"_1244":26,"_1245":1246},"consentConfig",{"_325":326,"_327":328,"_329":330,"_331":332,"_333":334,"_335":336,"_337":338,"_339":340,"_341":342,"_343":344},"showContactAdmin",1783313685412,[],[],[318,319,320,321,322,323],{"_40":55,"_42":65,"_44":159,"_46":-5,"_48":-5,"_50":51},{"_40":55,"_42":63,"_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":324,"_50":51},5,"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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