Context-aware convolutional neural networks for stroke sign detection in non-contrast CT scans

Aneta Lisowska*, Alison O’Neil, Vismantas Dilys, Matthew Daykin, Erin Beveridge, Keith Muir, Stephen McLaughlin, Ian Poole

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contribution

30 Citations (Scopus)
366 Downloads (Pure)

Abstract

Detection of acute stroke signs in non-contrast CT images is a challenging task. The intensity and texture variations in pathological regions are subtle and can be confounded by normal physiological changes or by old lesions. In this paper we investigate the use of contextual information for stroke sign detection. In particular, the appearance of the contralateral anatomy and the atlas-encoded spatial location are incorporated into a Convolutional Neural Network (CNN) architecture. CNNs are trained separately for the detection of dense vessels and of ischaemia. The network performance is evaluated on 170 datasets by cross-validation. We find that atlas location is important for dense vessel detection, but is less useful for ischaemia, whereas bilateral comparison is crucial for detection of ischaemia.

Original languageEnglish
Title of host publicationMedical Image Understanding and Analysis
Subtitle of host publicationMIUA 2017
EditorsMaría Valdés Hernández, Víctor González-Castro
PublisherSpringer
Pages494-505
Number of pages12
ISBN (Electronic)9783319609645
ISBN (Print)9783319609638
DOIs
Publication statusPublished - 22 Jun 2017
Event21st Annual Conference on Medical Image Understanding and Analysis 2017 - Edinburgh, United Kingdom
Duration: 11 Jul 201713 Jul 2017

Publication series

NameCommunications in Computer and Information Science
PublisherSpringer
Volume723
ISSN (Print)1865-0929
ISSN (Electronic)1865-0937

Conference

Conference21st Annual Conference on Medical Image Understanding and Analysis 2017
Abbreviated titleMIUA 2017
Country/TerritoryUnited Kingdom
CityEdinburgh
Period11/07/1713/07/17

ASJC Scopus subject areas

  • General Computer Science
  • General Mathematics

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