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Erschienen in:
Dice Overlap Measures for Objects of Unknown Number: Application to Lesion Segmentation Kapitel lesen Erstes Kapitel lesen

2018 | OriginalPaper | Buchkapitel

Sub-acute and Chronic Ischemic Stroke Lesion MRI Segmentation

verfasst von : Senan Doyle, Florence Forbes, Assia Jaillard, Olivier Heck, Olivier Detante, Michel Dojat

Erschienen in: Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries

Verlag: Springer International Publishing

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Abstract

Automatic segmentation of chronic stroke lesion from magnetic resonance images (MRI) is motivated by the increasing need for reproducible and repeatable endpoints in clinical trials. The task is non-trivial, due to a number of confounding factors, including heterogeneous lesion intensity, irregular shape, and large deformations that render the conventional use of prior probabilistic atlases challenging. In this paper, we introduce a hidden Markov random field model that avails of a novel prior probabilistic vascular territory atlas to describe the natural vascular constraints in the brain. The vascular territory atlas is deformed in a joint registration-segmentation framework to overcome subject-specific morphological variability. T1-w and Flair sequences are used to populate our model, and a variational approach is implemented to find a solution. The performance of our model is demonstrated on two datasets, and compared to manual delineations by expert raters.

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Vorheriges Kapitel Pairwise, Ordinal Outlier Detection of Traumatic Brain Injuries
Nächstes Kapitel Brain Tumor Segmentation Using an Adversarial Network
Fußnoten
1
The pre-processed data, ground truth and segmentation results are available at http://​dx.​doi.​org/​10.​6084/​m9.​figshare.​1585018.
 
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Metadaten
Titel
Sub-acute and Chronic Ischemic Stroke Lesion MRI Segmentation
verfasst von
Senan Doyle
Florence Forbes
Assia Jaillard
Olivier Heck
Olivier Detante
Michel Dojat
Copyright-Jahr
2018
Buch
Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries

Print ISBN: 978-3-319-75237-2

Electronic ISBN: 978-3-319-75238-9

Copyright-Jahr: 2018

DOI
https://doi.org/10.1007/978-3-319-75238-9_10