de Bruijne, Marleen

van der Lijn, Fedde

van Opbroek, Annegreet

English

Copenhagen University Research Information System

u n i ve r s i t y  o f  co pe n h ag e n  Københavns UniversitetAutomated brain-tissue segmentation by multi-feature SVM classificationvan Opbroek, Annegreet; van der Lijn, Fedde; de Bruijne, MarleenPublished in:The MICCAI Grand Challenge on MR Brain Image Segmentation (MRBrainS13)Publication date:2013Document versionPublisher's PDF, also known as Version of recordDocument license:UnspecifiedCitation for published version (APA):van Opbroek, A., van der Lijn, F., & de Bruijne, M. (2013). Automated brain-tissue segmentation by multi-featureSVM classification. In The MICCAI Grand Challenge on MR Brain Image Segmentation (MRBrainS13) TheMIDAS JournalDownload date: 03. Feb. 2020Automated Brain-Tissue Segmentation byMulti-Feature SVM ClassificationAnnegreet van Opbroek1, Fedde van der Lijn1 and Marleen de Bruijne1,21 Biomedical Imaging Group Rotterdam, Departments of Medical Informatics andRadiology, Erasmus MC - University Medical Center Rotterdam, the Netherlands2 Department of Computer Science, University of Copenhagen, DenmarkAbstract. We present a method for automated brain-tissue segmen-tation through voxelwise classification. Our algorithm uses manually la-beled training images to train a support vector machine (SVM) classifier,which is then used for the segmentation of target images. The classifica-tion incorporates voxel intensities from a T1-weighted scan, an IR scan,and a FLAIR scan; features to encode the voxel position in the image;and Gaussian-scale-space features and Gaussian-derivative features atmultiple scales to facilitate a smooth segmentation.An experiment on data from the MRBrainS13 brain-tissue-segmentationchallenge showed that our algorithm produces reasonable segmentationsin a reasonable amount of time.1 IntroductionThe segmentation of brain images can provide useful information about neuro-generative diseases such as dementia and multiple sclerosis, which is useful bothin research and clinical practice. Manual segmentation of brain images is a te-dious task however, which is why a variety of methods have been developed forautomated segmentation.Three types of automated brain-tissue-segmentation techniques can be distin-guished: techniques that use manually segmented images to train a segmentationalgorithm, techniques that require no manually segmented training images, andtechniques that use atlases. Methods that fall in the first category are usuallybased on supervised classification. In supervised classification labeled trainingdata is used to extract features and train a classifier, such as a k-nearest neigh-bor (kNN) classifier [1], a random decision forest [2], or a support vector machine(SVM) [3]. The labeled training data used in the classification is usually obtainedwith the same scanner as the target data, but Van Opbroek et al. [3] proposea transfer-learning SVM that can deal with labeled training data from otherscanners.Methods that do not require manually labeled training data include clusteringalgorithms such as the fuzzy c-means algorithm [4] and mean-shift clustering [5].A third option is to obtain a segmentation with the help of manually con-structed atlases, which can be used to automatically select and label trainingdata from a target image [6], to initialize an expectation-maximization algo-rithm [7], or to improve parameter estimation in classification with an EM algo-rithm [8].In this paper we present a brain-tissue-segmentation framework based onsupervised voxelwise classification with an SVM classifier, which is a state-of-the-art machine-learning classifier. In contrast to other techniques [1][2][4][5] oursegmentation scheme uses Gaussian-scale-space features and Gaussian-derivativefeatures next to the image intensities, to facilitate a smooth segmentation of thedifferent tissues. The performance of our method has been tested on 12 imagesfrom the brain-tissue segmentation challenge MRBrainS133.2 Material and Methods2.1 MRBrainsS13 Training and Test DataTraining and test images have been acquired at the UMC Utrecht in the Nether-lands and concern patients with diabetes and matched controls (age>50) withvarying degrees of atrophy and white-matter lesions. From all subjects a T1-weighted scan, a T1-weighted inversion recovery (IR) scan, and a fluid atten-uated inversion recovery (FLAIR) scan have been obtained, all with 0.958 ×0.958 × 3.0 mm3 voxel size. The three sequences have been registered and theimages have been bias corrected.Five images were provided to train a segmentation algorithm. The train-ing images were manually segmented into background and eight tissues: corticalgray matter (GM), basal ganglia, white matter (WM), white-matter lesions,cerebrospinal fluid (CSF) in the extracerebral space, the ventricles, the cerebel-lum, and the brainstem. Also, twelve test images were provided which had to besegmented into background, gray matter (cortical gray matter + basal ganglia),white matter (white matter + white-matter lesions), and cerebrospinal fluid(CSF in the extracerebral space + ventricles). Segmentation of the cerebellumand the brainstem was not included in the evaluation.2.2 PreprocessingAll images were normalized by a range-matching procedure that scaled the voxelswithin the mask so that the 4th percentile was mapped to zero, and the 96thpercentage to one. Since the images of the challenge were already bias corrected,no further bias correction was performed.2.3 Brain SegmentationFor the test images brain masks were created with multi-atlas segmentation. Asatlases we used the five T1-weighted training scans, both in the original andin a left-right-flipped version. Brain masks were obtained for these ten atlases3 http://mrbrains13.isi.uu.nlby binarizing the training labels (including the brainstem and the cerebellum).Each atlas image was registered to the unlabeled test images using Niftyreg [9] bycomputing an affine transformation, followed by a non-rigid deformation usinga 5mm B-spline grid and normalized mutual information. A final mask wascomputed using STEPS [10]. This method deforms both atlas images and labels,selects per voxel location the five most similar atlases (based on local normalizedcross correlation), and fuses their labels using STAPLE [11].2.4 FeaturesFrom each of the three sequences, T1, IR, and FLAIR, 10 features were extracted:– The intensity– The intensity after convolution with a Gaussian kernel with σ = 1, 2, 3 mm3– The gradient magnitude of the intensity after convolution with a Gaussiankernel with σ = 1, 2, 3 mm3– The laplacian of the intensity after convolution with a Gaussian kernel withσ = 1, 2, 3 mm3.The spatial information was incorporated by adding three spatial features: thex, y, and z coordinate of a voxel, divided by respectively the length, width, andheight of the brain. This resulted in a total of 33 features. All features werescaled to zero mean and unit standard deviation.2.5 SVM ClassificationWe performed supervised voxelwise classification with a soft-margin SVM [12].An SVM learns a decision function f(x) = v · φ(x) + v0, where the modelparameters v and v0 are determined from the training data, and φ is a mappingto project a sample xi into a feature space φ(xi). This mapping defines a kernelfunction K(xi,xj) = 〈φ(xi), φ(xj)〉 by means of the kernel trick.The model parameters v and v0 are determined from the training data byoptimizing the following criterionminv12‖v‖2 + CN∑i=1ξi (1)s.t. yivTφ(xi) + v0 ≥ 1− ξiξi ≥ 0 .The first term, ‖v‖2 maximizes the margin around the decision function, andC∑Ni=1 ξi minimizes the number of training samples that are either misclassifiedor lie within the margin. C is a parameter to trade off between maximizing themargin and minimizing∑i ξi where a sample receives a value ξi > 1 if it ismisclassified, a value 0 < ξi ≤ 1 if it is correctly classified but lies within themargin, and a value ξi = 0 otherwise.We performed six-class classification to classify cortical GM, basal ganglia,WM, white-matter lesions, CSF in the extracerebral space, and the ventricles.Since SVMs are designed for two-class classification, the classification was ex-tended to multi-class classification by one-versus-one classification which meansthat a total of 15 SVMs were trained to distinguish between the six classes.For the SVM classification an implementation in LIBSVM [13] was used.Classification Parameters To enable for non-linear decision functions a radialbasis kernel was chosen. A suitable value for the SVM parameter C and the kernelparameter γ were determined in a grid-search experiment on the five trainingimages in which the total classification error was minimized. This resulted invalues of C = 8 and γ = 0.01.The SVM classifier was trained on a total of 10 000 samples per training im-age, which were randomly selected from inside the provided brain mask excludingthe cerebellum and the brain stem.2.6 PostprocessingPostprocessing involved fusing the voxels segmented as cortical gray matter andbasal ganglia, white matter and white-matter lesions, and CSF in the extracere-bral space and the ventricles. Subsequently a closing algorithm was performedon the regions segmented as CSF, by a dilation of 1 voxel, followed by an erosionof 1 voxel. This was done to reduce the amount of voxels in the CSF that weresegmented as WM or GM.2.7 Outcome MeasuresSegmentation results on the 12 test images were compared to the manual seg-mentations based on three measures:– The DICE overlap [14]– The modified Hausdorff distance (MHD) [15]– The absolute volume difference (AVD) [16].3 ResultsTable 3 shows the mean and standard deviation (std) of the scores. The evalua-tion measures were calculated for 5 tissues: GM, WM, CSF, brain (WM+GM),and all intracranial structures (WM+GM+CSF). For the DICE score our algo-rithm scored best on white-matter segmentation, with a mean DICE coefficientof 88.3%, and slightly lower for gray matter, with a mean DICE of 84.5%. Themost errors were made in the CSF, which had a mean DICE score of only 78.0%.Also on the other two scores, MHD and AVD, white matter and gray matterhad a better score than CSF.DICE (%) MHD (mm) AVD (%)Structure Mean Std Mean Std Mean StdGray Matter 84.51 1.44 1.97 0.34 6.92 3.09White Matter 88.30 0.98 2.41 0.50 6.79 5.19Cerebrospinal fluid 78.00 5.43 3.31 0.80 25.98 18.49Brain 95.05 0.53 2.79 0.82 3.51 1.61All Intracranial Structures 95.86 1.32 4.02 1.20 5.67 3.14Table 1. Results on 12 test images: mean and standard deviation (std) for GM, WM,CSF, brain (WM+GM), and all intracranial structures (WM+GM+CSF).(a) Img 3, T1 (b) Img 3, IR (c) Img 3, FLAIR (d) Img 3, Segm(e) Img 8, T1 (f) Img 8, IR (g) Img 8, FLAIR (h) Img 8, Segm(i) Img 10, T1 (j) Img 10, IR (k) Img 10, FLAIR (l) Img 10, SegmFig. 1. T1, IR, FLAIR images and segmentations (Segm) of 3 of the 12 test images.Image 3 in (a)-(d) contains a large amount of lesions, image 8 in (e)-(h) was given theoverall best score, image 10 in (i)-(l) was given the overall worst score.Example segmentations for slices from images 3, 8, and 10 are presented inFig. 1. Fig. 1(a),(e),(i) show the T1-weighted images, Fig. 1(b),(f),(j) show theT1-weighted IR scans, Fig. 1(c),(g),(k) show the FLAIR scans, and Fig. 1(d),(h),(l) show the segmentations. Image 3 in Fig. 1(a)-(d) has a very large amount oflesions, Image 8 in fig. 1(e)-(h) is the image that overall scored the best, andimage 10 in Fig. 1(i)-(l) overall scored worst.For all images the determined brain mask was too large in the front, ascan be seen in the segmentations in Fig. 1(d),1(h),(l). In most images this ledto voxels in the front of the image being erroneously classified as either whitematter or gray matter tissue. This effect is most prominent in Fig. 1(l), whereWM and GM clusters have appeared in the CSF, but it can also been seen inthe segmentations in Fig. 1(d),1(h). In images with a large amount of lesions,as in Image 3, lesion voxels were sometimes erroneously classified as CSF, as isshown in Fig. 1(d).4 DiscussionWe have presented an algorithm for automated brain extraction and brain-tissuesegmentation. The brain-extraction algorithm is based on multi-atlas segmenta-tion with the STEPS [10] algorithm; the tissue classification is based on voxelwiseSVM classification. In the voxelwise classification T1, IR, and FLAIR intensities,spatial features, Gaussian-scale-space features and Gaussian-derivative featureswere used.Our algorithm produced reasonable segmentations which were generally quitesmooth without further spatial regularization because of the use of the Gaussian-scale-space features and the Gaussian-derivative features. In some slices however,mainly around the basal ganglia, the segmentations were not completely smooth,which was caused by the low contrast between the basal ganglia and the sur-rounding white matter.The largest errors were made in the segmentation of the CSF in the extrac-erebral space, which was mainly because the determined brain mask was toobig in the frontal lobe due to a slight misregistration of the atlases. As a result,skull voxels were incorporated in the brain mask, which were sometimes seg-mented as white or gray matter. As a post-processing step a closing algorithmwas performed on the CSF tissue, which segmented some of these voxels as CSF.We believe that refining the masks by including a background class in the SVMclassification may improve the results.Other weaknesses of our algorithm are a slight under segmentation of thebasal ganglia, and the misclassification of voxels in the center of large white-matter lesions that are close to the ventricles, which were erroneously segmentedas CSF. This second problem could be resolved by excluding the lesions fromthe tissue segmentation and including a separate lesion-classification step. Thisseparate classification step allows for the exclusion of spatial features from thefeature set, which can be very misleading features for lesion segmentation whenonly a small number of training images is available.The total runtime of our algorithm per test image was 10 times 8 minutes toperform the registrations for the image mask, 25 seconds to determine the imagefeatures, 1.5 minutes to train the SVM classifiers (note that this only needs tobe done once for segmentation of all images), and 35 minutes for classificationof the test image. The registrations for the image mask were computed on acluster with AMD Opteron 2216 2.4GHz nodes without multi-threading, therest was implemented in Matlab and computed on a machine with an Intel XeonE5620 2.40 GHz CPU. For a voxelwise classification 35 minutes is relatively long,which is due to the fact that a total of 15 one-vs-one SVMs were calculated. Thecalculation time could be drastically reduced by training on only three labels:gray matter, white matter, and CSF. In a cross-validation experiment on thetraining set this only slightly increased the mean classification error from 13.9%to 14.3%, but decreased the calculation time with approximately a factor of 5.To conclude, the proposed multi-feature SVM classification produces rea-sonable segmentations in a reasonable amount of time. We believe that if theregistrations of the training masks to the target images could be improved, anda separate lesion-segmentation algorithm could be included in the segmentation,the resulting segmentations would come close to those of human observers.5 AcknowledgmentsThis research is financed by The Netherlands Organization for Scientific Research(NWO).References1. Anbeek, P., Vincken, K., Van Bochove, G., Van Osch, M., van der Grond, J.,et al.: Probabilistic segmentation of brain tissue in MR imaging. Neuroimage27(4) (2005) 7952. 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Automated brain-tissue segmentation by multi-feature SVM classification

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