Enhancing infarct segmentation performance using domain-specific attention in acute ischemic stroke

V. Manikanda Krishnan, Srinivasa Rao Kundeti, Arun H. Shastry, Shankar Prasad Gorthi

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

Abstract

Ischemic stroke infarct tissues are not salvageable. The infarct volume calculated from a segmented infarct region is an important parameter required to decide on the optimal treatment workflow. Deep learning continues to demonstrate the significance of end-to-end training with limited use of apriori knowledge (such as domain-aware feature engineering) in learning medical imaging tasks. Incorporating prior domain-specific knowledge introduces better inductive bias in learning tasks with low data availability, thereby improving performance. Several techniques have been used for segmentation of infarct region ranging from traditional approaches like region growing to deep learning approaches with limited use of domain-specific knowledge. This paper incorporates domain-specific knowledge into deep neural networks to restrict the region of interest thereby improving the performance of infarct segmentation. Incorporating domain-specific knowledge improve the performance by 17%.

Original languageEnglish
Title of host publicationMedical Imaging 2020
Subtitle of host publicationImage Processing
EditorsIvana Isgum, Bennett A. Landman
PublisherSPIE
ISBN (Electronic)9781510633933
DOIs
Publication statusPublished - 2020
EventMedical Imaging 2020: Image Processing - Houston, United States
Duration: 17-02-202020-02-2020

Publication series

NameProgress in Biomedical Optics and Imaging - Proceedings of SPIE
Volume11313
ISSN (Print)1605-7422

Conference

ConferenceMedical Imaging 2020: Image Processing
CountryUnited States
CityHouston
Period17-02-2020-02-20

All Science Journal Classification (ASJC) codes

  • Electronic, Optical and Magnetic Materials
  • Biomaterials
  • Atomic and Molecular Physics, and Optics
  • Radiology Nuclear Medicine and imaging

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