{"id": "package:372a2996-70ee-49db-a2ef-2ebe81041b01", "name": "Prediction3D.tif", "self_uri": "https://services.scicrunch.io/sparc/drs/v1/objects/372a2996-70ee-49db-a2ef-2ebe81041b01", "size": 490783, "created_time": "2025-12-12T04:10:29,763607Z", "updated_time": "2025-12-12T04:11:03,235366Z", "version": "1", "mime_type": "image/tiff", "checksums": [{"checksum": "1488093adc3b5fe1a29394efe96d466d", "type": "sha256"}], "access_methods": [{"type": "s3", "access_url": {"url": "s3://sparc-prod-aod-discover-publish50-use1/521/files/derivative/sub-SR008/sam-SR008-11/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."}}