Automated detection of cerebral microbleeds in patients with Traumatic Brain Injury

In this paper a Computer Aided Detection (CAD) system is presented to automatically detect Cerebral Microbleeds (CMBs) in patients with Traumatic Brain Injury (TBI). It is believed that the presence of CMBs has clinical prognostic value in TBI patients. To study the contribution of CMBs in patient outcome, accurate detection of CMBs is required. Manual detection of CMBs in TBI patients is a time consuming task that is prone to errors, because CMBs are easily overlooked and are difficult to distinguish from blood vessels. This study included 33 TBI patients. Because of the laborious nature of manually annotating CMBs, only one trained expert manually annotated the CMBs in all 33 patients. A subset of ten TBI patients was annotated by six experts. Our CAD system makes use of both Susceptibility Weighted Imaging (SWI) and T1 weighted magnetic resonance images to detect CMBs. After pre-processing these images, a two-step approach was used for automated detection of CMBs. In the first step, each voxel was characterized by twelve features based on the dark and spherical nature of CMBs and a random forest classifier was used to identify CMB candidate locations. In the second step, segmentations were made from each identified candidate location. Subsequently an object-based classifier was used to remove false positive detections of the voxel classifier, by considering seven object-based features that discriminate between spherical objects (CMBs) and elongated objects (blood vessels). A guided user interface was designed for fast evaluation of the CAD system result. During this process, an expert checked each CMB detected by the CAD system. A Fleiss' kappa value of only 0.24 showed that the inter-observer variability for the TBI patients in this study was very large. An expert using the guided user interface reached an average sensitivity of 93%, which was significantly higher (p = 0.03) than the average sensitivity of 77% (sd 12.4%) that the six experts manually detected. Furthermore, with the use of this CAD system the reading time was substantially reduced from one hour to 13 minutes per patient, because the CAD system only detects on average 25.9 false positives per TBI patient, resulting in 0.29 false positives per definite CMB finding.

[1]  Karl J. Friston,et al.  Unified segmentation , 2005, NeuroImage.

[2]  P. Vos,et al.  The reliability of magnetic resonance imaging in traumatic brain injury lesion detection , 2012, Brain injury.

[3]  Olivier Salvado,et al.  Efficient machine learning framework for computer-aided detection of cerebral microbleeds using the Radon transform , 2014, 2014 IEEE 11th International Symposium on Biomedical Imaging (ISBI).

[4]  Jun Liu,et al.  Diffuse axonal injury after traumatic cerebral microbleeds: an evaluation of imaging techniques , 2014, Neural regeneration research.

[5]  Bart M. ter Haar Romeny.  Multi-Scale and Multi-Orientation Medical Image Analysis , 2010 .

[6]  M. L. Lauzon,et al.  Susceptibility-Weighted Imaging is More Reliable Than T2*-Weighted Gradient-Recalled Echo MRI for Detecting Microbleeds , 2013, Stroke.

[7]  Michael Brady,et al.  Improved Optimization for the Robust and Accurate Linear Registration and Motion Correction of Brain Images , 2002, NeuroImage.

[8]  Leo Breiman,et al.  Random Forests , 2001, Machine Learning.

[9]  Eric E. Smith,et al.  MR Imaging Detection of Cerebral Microbleeds: Effect of Susceptibility-Weighted Imaging, Section Thickness, and Field Strength , 2008, American Journal of Neuroradiology.

[10]  D. Werring,et al.  The Microbleed Anatomical Rating Scale (MARS) , 2009, Neurology.

[11]  Guido Gerig,et al.  Three-dimensional multi-scale line filter for segmentation and visualization of curvilinear structures in medical images , 1998, Medical Image Anal..

[12]  Guido Gerig,et al.  3D Multi-scale line filter for segmentation and visualization of curvilinear structures in medical images , 1997, CVRMed.

[13]  Max A. Viergever,et al.  Efficient detection of cerebral microbleeds on 7.0T MR images using the radial symmetry transform , 2012, NeuroImage.

[14]  Hao Chen,et al.  Automatic Detection of Cerebral Microbleeds From MR Images via 3D Convolutional Neural Networks , 2016, IEEE Transactions on Medical Imaging.

[15]  B. Jennett,et al.  Assessment of coma and impaired consciousness. A practical scale. , 1974, Lancet.

[16]  Alan C. Evans,et al.  A nonparametric method for automatic correction of intensity nonuniformity in MRI data , 1998, IEEE Transactions on Medical Imaging.

[17]  E Mark Haacke,et al.  Hemorrhagic shearing lesions in children and adolescents with posttraumatic diffuse axonal injury: improved detection and initial results. , 2003, Radiology.

[18]  Stephen M. Smith,et al.  Improved Optimization for the Robust and Accurate Linear Registration and Motion Correction of Brain Images , 2002, NeuroImage.

[19]  Stephen M. Smith,et al.  Segmentation of brain MR images through a hidden Markov random field model and the expectation-maximization algorithm , 2001, IEEE Transactions on Medical Imaging.

[20]  Steven Warach,et al.  Cerebral Microbleeds : A Field Guide to their Detection and Interpretation , 2012 .

[21]  Stephen M. Smith,et al.  A global optimisation method for robust affine registration of brain images , 2001, Medical Image Anal..

[22]  Susan M. Chang,et al.  Computer-aided detection of radiation-induced cerebral microbleeds on susceptibility-weighted MR images☆ , 2013, NeuroImage: Clinical.

[23]  M. Seghier,et al.  Microbleed Detection Using Automated Segmentation (MIDAS): A New Method Applicable to Standard Clinical MR Images , 2011, PloS one.

[24]  David J. Werring,et al.  Cerebral Microbleeds: Pathophysiology to Clinical Practice , 2011 .

[25]  Marleen de Bruijne,et al.  A computer aided detection system for cerebral microbleeds in brain MRI , 2012, 2012 9th IEEE International Symposium on Biomedical Imaging (ISBI).

[26]  E Mark Haacke,et al.  Semiautomated detection of cerebral microbleeds in magnetic resonance images. , 2011, Magnetic resonance imaging.