A user-guided tool for semi-automated cerebral microbleed detection and volume segmentation: Evaluating vascular injury and data labelling for machine learning

NeuroImage. Clinical
Melanie A MorrisonJanine M Lupo

Abstract

With extensive research efforts in place to address the clinical relevance of cerebral microbleeds (CMBs), there remains a need for fast and accurate methods to detect and quantify CMB burden. Although some computer-aided detection algorithms have been proposed in the literature with high sensitivity, their specificity remains consistently poor. More sophisticated machine learning methods appear to be promising in their ability to minimize false positives (FP) through high-level feature extraction and the discrimination of hard-mimics. To achieve superior performance, these methods require sizable amounts of precisely labelled training data. Here we present a user-guided tool for semi-automated CMB detection and volume segmentation, offering high specificity for routine use and FP labelling capabilities to ease and expedite the process of generating labelled training data. Existing computer-aided detection methods reported by our group were extended to include fully-automated segmentation and user-guided CMB classification with FP labelling. The algorithm's performance was evaluated on a test set of ten patients exhibiting radiotherapy-induced CMBs on MR images. The initial algorithm's base sensitivity was maintained at 86.7%. ...Continue Reading

Citations

Sep 24, 2020·Radiotherapy and Oncology : Journal of the European Society for Therapeutic Radiology and Oncology·Melanie A MorrisonJanine M Lupo
Feb 19, 2021·Scientific Reports·Anthony G ChesebroAdam M Brickman

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Methods Mentioned

BETA
feature extraction
mIP

Software Mentioned

MATLAB
FSL
IMAGINE

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