Estimation of the Principal Ischaemic Stroke Growth Directions for Predicting Tissue Outcomes

Christian Lucas*, Linda F. Aulmann, André Kemmling, Amir Madany Mamlouk, Mattias P. Heinrich

*Korrespondierende/r Autor/-in für diese Arbeit

Abstract

Convolutional neural networks (CNN) have been widely used for the medical image analysis of brain lesions. The estimates of traditional segmentation networks for the prediction of the follow-up tissue outcome in strokes are, however, not yet accurate enough or capable of properly modeling the growth mechanisms of ischaemic stroke. In our previous shape space interpolation approach, the prediction of the follow-up lesion shape has been bounded using core and penumbra segmentation estimates as priors. One of the challenges is to define well-suited growth constraints, as the transition from one to another shape may still result in a very unrealistic spatial evolution of the stroke. In this work, we address this shortcoming by explicitly incorporating vector fields for the spatial growth of the infarcted area. Since the anatomy of the cerebrovascular system defines the blood flow along brain arteries, we hypothesise that we can reasonably regularise the direction and strength of growth using a lesion deformation model. We show that a Principal Component Analysis (PCA) model computed from the diffeomorphic displacements between a core lesion approximation and the entire tissue-at-risk can be used to estimate follow-up lesions (0.74 F1 score) for a well-defined growth problem with accurate input data better than with the shape model (0.62 F1 score) by predicting the PCA coefficients through a CNN.

OriginalspracheEnglisch
TitelBrainLes 2019: Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries
Redakteure/-innenAlessandro Crimi, Spyridon Bakas
Seitenumfang11
Band11992 LNCS
Herausgeber (Verlag)Springer, Cham
Erscheinungsdatum19.05.2020
Seiten69-79
ISBN (Print)978-3-030-46639-8
ISBN (elektronisch)978-3-030-46640-4
DOIs
PublikationsstatusVeröffentlicht - 19.05.2020
Veranstaltung22nd International Conference on Medical Image Computing and Computer-Assisted Intervention - Shenzhen, China
Dauer: 13.10.201917.10.2019
Konferenznummer: 232939

Strategische Forschungsbereiche und Zentren

  • Zentren: Zentrum für Künstliche Intelligenz Lübeck (ZKIL)
  • Querschnittsbereich: Intelligente Systeme

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