date: 2020-09-23T16:28:42Z pdf:PDFVersion: 1.6 pdf:docinfo:title: BRAVE-NET: Fully Automated Arterial Brain Vessel Segmentation in Patients With Cerebrovascular Disease xmp:CreatorTool: LaTeX with hyperref package + hypdvips access_permission:can_print_degraded: true subject: Introduction: Arterial brain vessel assessment is crucial for the diagnostic process in patients with cerebrovascular disease. language: en dc:format: application/pdf; version=1.6 pdf:docinfo:creator_tool: LaTeX with hyperref package + hypdvips access_permission:fill_in_form: true pdf:encrypted: false dc:title: BRAVE-NET: Fully Automated Arterial Brain Vessel Segmentation in Patients With Cerebrovascular Disease modified: 2020-09-23T16:28:42Z cp:subject: Introduction: Arterial brain vessel assessment is crucial for the diagnostic process in patients with cerebrovascular disease. pdf:docinfo:subject: Introduction: Arterial brain vessel assessment is crucial for the diagnostic process in patients with cerebrovascular disease. pdf:docinfo:creator: Adam Hilbert meta:author: Adam Hilbert meta:creation-date: 2020-09-23T15:43:16Z created: 2020-09-23T15:43:16Z access_permission:extract_for_accessibility: true Creation-Date: 2020-09-23T15:43:16Z Author: Adam Hilbert producer: dvips + MiKTeX GPL Ghostscript 9.0 pdf:docinfo:producer: dvips + MiKTeX GPL Ghostscript 9.0 pdf:unmappedUnicodeCharsPerPage: 0 dc:description: Introduction: Arterial brain vessel assessment is crucial for the diagnostic process in patients with cerebrovascular disease. Keywords: artificial intelligence (AI), segmentation (image processing), UNET, cerebrovascular disease (CVD), machine learning access_permission:modify_annotations: true dc:creator: Adam Hilbert description: Introduction: Arterial brain vessel assessment is crucial for the diagnostic process in patients with cerebrovascular disease. dcterms:created: 2020-09-23T15:43:16Z Last-Modified: 2020-09-23T16:28:42Z dcterms:modified: 2020-09-23T16:28:42Z title: BRAVE-NET: Fully Automated Arterial Brain Vessel Segmentation in Patients With Cerebrovascular Disease xmpMM:DocumentID: uuid:b24d5fd6-c29c-418d-bbab-7620454c2ece Last-Save-Date: 2020-09-23T16:28:42Z pdf:docinfo:keywords: artificial intelligence (AI), segmentation (image processing), UNET, cerebrovascular disease (CVD), machine learning pdf:docinfo:modified: 2020-09-23T16:28:42Z meta:save-date: 2020-09-23T16:28:42Z Content-Type: application/pdf X-Parsed-By: org.apache.tika.parser.DefaultParser creator: Adam Hilbert dc:language: en dc:subject: artificial intelligence (AI), segmentation (image processing), UNET, cerebrovascular disease (CVD), machine learning access_permission:assemble_document: true xmpTPg:NPages: 14 pdf:charsPerPage: 3806 access_permission:extract_content: true access_permission:can_print: true meta:keyword: artificial intelligence (AI), segmentation (image processing), UNET, cerebrovascular disease (CVD), machine learning access_permission:can_modify: true pdf:docinfo:created: 2020-09-23T15:43:16Z