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Neuroimage Clin. 2018 Aug 4;20:498-505. doi: 10.1016/j.nicl.2018.08.002. eCollection 2018.

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

Author information

1
Department of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, CA, USA.
2
Department of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, CA, USA; UCSF/UC Berkeley Graduate Group in Bioengineering, USA.
3
Department of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, CA, USA; Department of Neurology, University of California San Francisco, San Francisco, CA, USA.
4
Department of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, CA, USA; UCSF/UC Berkeley Graduate Group in Bioengineering, USA. Electronic address: janine.lupo@ucsf.edu.

Abstract

Background and purpose:

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.

Materials and methods:

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.

Results:

The initial algorithm's base sensitivity was maintained at 86.7%. FP's were reduced to inter-rater variations and segmentation results were in 98% agreement with ground truth labelling. There was an approximate 5-fold reduction in the time users spent evaluating CMB burden with the algorithm versus without computer aid. The Intra-class Correlation Coefficient for inter-rater agreement was 0.97 CI[0.92,0.99].

Conclusions:

This development serves as a valuable tool for routine evaluation of CMB burden and data labelling to improve CMB classification with machine learning. The algorithm is available to the public on GitHub (https://github.com/LupoLab-UCSF/CMB_labeler).

KEYWORDS:

Algorithm; Automated; Brain tumor; Cerebral microbleeds; Lesion; Machine learning; Magnetic resonance imaging; Radiation therapy; Susceptibility weighted imaging; Vascular injury

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