Non-stationary deep lifting with application to acute brain infarct segmentation

Nadja Gruber, Markus Haltmeier, Annemieke ter Telgte, Johannes Schwab, Elke Gizewski, Malik Galijasevic

Keywords: Deep learning, ischemic brain infarct segmentation, prior, input lifting

TL;DR: Deep learning segmentation of acute brain infarcts using a novel input enhancement strategy combined with a suitable non-stationary prior loss.

Abstract:

We present a deep learning based method for segmenting acute brain infarcts in MRI images using a novel input enhancement strategy combined with a suitable non-stationary loss. The hybrid framework allows incorporating knowledge of clinicians to mimic the diagnostic patterns of experts. More specifically, our strategy consists of an interaction of non-local input transforms that highlight features which are additionally penalized by the non-stationary loss. For brain infarct segmentation, expert knowledge refers to the quasi-symmetry property of healthy brains, whereas in other applications one may include different anatomical priors.
In addition, we use a network architecture merging information from the two complementary MRI maps DWI and ADC.
We perform experiments on a dataset consisting of DWI and ADC images from 100 patients to demonstrate the applicability of proposed method.

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Paper Type: novel methodological ideas without extensive validation

Primary Subject Area: Segmentation

Secondary Subject Area: Detection and Diagnosis

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