{"id": "package:8202fc7d-af5e-45b2-8452-69bd0a0b3cf8", "name": "GroundTruth.tif", "self_uri": "https://services.scicrunch.io/sparc/drs/v1/objects/8202fc7d-af5e-45b2-8452-69bd0a0b3cf8", "size": 431726, "created_time": "2025-12-12T04:11:44,243412Z", "updated_time": "2025-12-12T04:12:11,929027Z", "version": "1", "mime_type": "image/tiff", "checksums": [{"checksum": "57d992f50357e6cc6deb073704deacce", "type": "sha256"}], "access_methods": [{"type": "s3", "access_url": {"url": "s3://sparc-prod-aod-discover-publish50-use1/521/files/derivative/sub-SR008/sam-SR008-17/GroundTruth.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."}}