{"id": "package:1fb4d27f-d4ce-4dab-8c6f-972000a34966", "name": "Prediction3D.tif", "self_uri": "https://services.scicrunch.io/sparc/drs/v1/objects/1fb4d27f-d4ce-4dab-8c6f-972000a34966", "size": 632464, "created_time": "2025-12-12T04:25:36,411124Z", "updated_time": "2025-12-12T04:26:39,750126Z", "version": "1", "mime_type": "image/tiff", "checksums": [{"checksum": "8c29754f7a3c58af2e658fca675e2be3", "type": "sha256"}], "access_methods": [{"type": "s3", "access_url": {"url": "s3://sparc-prod-aod-discover-publish50-use1/521/files/derivative/sub-SR010/sam-SR010-7/Prediction3D.tif"}, "region": "us-east-1"}], "dataset": {"id": "521", "doi": "DOI:10.26275/wfud-am1l", "title": "Automated 3D segmentation of human vagus nerve fascicles and epineurium from micro-computed tomography images using anatomy-aware neural networks", "description": "Data associated with our publication on automated 3D segmentation of microCT images of human vagus nerves.", "abstract": "Microcomputed tomography (microCT) of embalmed cadaveric human vagus nerves stained with phosphotungstic acid. Fascicle and nerve boundaries were manually segmented in a subset of images to train 2D and 3D U-nets. The remaining images were automatically segmented with the trained U-nets. We quantified the performance of each U-net using established image analysis metrics and anatomically relevant metrics."}}