{"id": "package:20fa1305-3ca0-4861-8123-7482b93c811a", "name": "896-5_F2.tif", "self_uri": "https://services.scicrunch.io/sparc/drs/v1/objects/20fa1305-3ca0-4861-8123-7482b93c811a", "size": 50407188, "created_time": "2022-02-24T21:13:26,639713Z", "updated_time": "2022-02-24T21:13:28,820360Z", "version": "2", "mime_type": "image/tiff", "checksums": [{"checksum": "704910d4bae97d17ffb5d0f4478fff63e652134aa2ef85551a6f359e9a979bd8", "type": "sha256"}], "access_methods": [{"type": "s3", "access_url": {"url": "s3://prd-sparc-discover50-use1/226/files/primary/sub-10896/sam-4/896-5_F2.tif"}, "region": "us-east-1"}], "dataset": {"id": "226", "doi": "DOI:10.26275/eefp-azay", "title": "High-throughput segmentation of rat unmyelinated axons by deep learning", "description": "Transmission electron microscopy (TEM) images and segmentation of nerve fibers", "abstract": "To  automatically  identify unmyelinated axons in nerve tissues.  The manual segmentation of unmyelinated fibers, that are the majority of fibers in the nervous system, takes too much time. The correct identification of fibers is very important for the development of neuromodulation strategies. Rat vagus and pelvic nerves tranmission electron microscopy (TEM) images were used to create a new  prototype of a high-throughput processing pipeline for automated segmentation of unmyelinated fibers. The new prototype saved about 80% of efforts when compared with manual segmentation. This new tool will enable fast and accurate characterization of unmyelinated fibers and help to better understand the nervous system for improvement of neuromodulation strategies."}}