Journal ArticleTo better understand the central nervous system, neurobiologists need to reconstruct the underlying neural circuitry from electron microscopy images. One of the necessary tasks is to segment the individual neurons. For this purpose, we propose a supervised learning approach to detect the cell membranes. The classifier was trained using AdaBoost, on local and context features. The features were selected to highlight the line characteristics of cell membranes. It is shown that using features from context positions allows for more information to be utilized in the classification. Together with the nonlinear discrimination ability of the AdaBoost classifier, this results in clearly noticeable improvements over previously used methods

Tasdizen, Tolga

Venkataraju, Kannan Umadevi

English

The University of Utah: J. Willard Marriott Digital Library

AUTOM ATIC M A R K U P O F NEURAL CELL M EM BRANES USING  BO O STED DECISIO N  STUM PSK a n n a n  U m a d e v i  V e n k a ta r a ju h 2 , A n to n io  R . C . P a i v a \  E l i z a b e th  J u r r u s 1'2, T o lg a  T a s d iz e n 2:iSchool of Computing, University of Utah 2Scientific Computing and Imaging Institute, University of Utah "“Electrical and Computer Engineering Department, University of UtahABSTRACTTo better understand the central nervous system, ncurobiolo- gists need to reconstruct the underlying neural circuitry from electron microscopy images. One of the necessary tasks is to segment the individual neurons. For this purpose, wc propose a supervised learning approach to detect the cell membranes. The classifier was trained using AdaBoost, on local and con­text features. The features were selected to highlight the line characteristics o f cell membranes. It is shown that using fea­tures from context positions allows for more information to be utilized in the classification. Together with the nonlinear dis­crimination ability o f the AdaBoost classifier, this results in clearly noticeable improvements over previously used m eth­ods.Index Terms—  Scrial-scction TEM , cell membrane de­tection, AdaBoost, Segmentation, M achine Learning.1. INTRODUCTIONNeuroscientists arc currently developing new imaging tech­niques to better understand the complex structure of the cen­tral nervous system. In particular, researchers arc making ef­forts to map the connectivity of large volumes of individual neurons in order to understand how signals arc communicated across processes. The most extensive study undertaken thus far uses clcctron microscopy to crcatc detailed diagrams of neuronal structure [ 1] and connectivity [2, 3]. The most well known example of neural circuit reconstruction is of the 302 neurons in the C. elegans worm. Even though this is one of the simplest organisms with a nervous system, the manual re­construction proccss took ten years. Human interpretation of data over large volumes of neural anatomy is so labor inten­sive that very little ground truth exists. For this reason, im­age processing and machinc learning algorithms arc needed to automate the proccss and allow analysis o f large datasets by neural circuit reconstruction.Scrial-scction transmission clcctron microscopy (TEM) is the preferred data acquisition technology for capturingThis work was supported by NIH R01 EB005832. NIH EYCK) 15128. EY002576. NEI Vision Core EY014800. USTAR and HHMI.images of large sections of neuronal tissue. Images from TEM span a wide field of view, capturing processes that may wander through a spccimcn and have ail in-planc resolu­tion useful for identifying ccllular features such as synapses. These structures arc critical in understanding neuron activity and function. Images from scrial-scction TEM  arc capturcd by cutting a scction from the spccimcn and suspending it over an clcctron beam which passes through the scction cre­ating a projection which is capturcd as a digital image. Sec figure 2(a) for an example scrial-scction TEM image corre­sponding to a cross-scction of the nematode C. elegans with a resolution of 6nm x6nm x33nm .An accurate mapping o f neuron features begins with the segmentation of the neuron boundaries. Jurrus et al. [4] uses these boundaries to extract the three dimensional connectiv­ity present in similar image volumes. In their method, a con­trast enhancing filter followed by a directional diffusion filter is applied to the raw images to cnhancc and conncct ccllular membranes. The images arc then thrcsholdcd and neuron ccll bodies arc identified using a watershed segmentation method. This method fails when membranes arc weak or there arc too many intraccllular features. This indicates that more adap­tive algorithms need to be developed to segment these struc­tures. For this reason, machinc learning algorithms have been shown as a succcssful alternative for identifying membranes in TEM data. In related work, Jain etal. [5] uses a multilayer convolution neural network to classify pixels as membrane and non-mcmbranc. However, the stain used on the spccimcn highlights ccll boundaries, attenuating intraccllular structures, simplifying the segmentation task. Another succcssful appli­cation of learning applied to TEM is the use of a pcrccptron trained with a set o f predefined image features [6]. However, extensive post processing is required to elose the dctcctcd ccll membranes and remove internal ccllular structures.The method described in this paper improves upon previ­ous work by utilizing context information for classification. By including the features of neighboring pixels as inputs to the classificr, the classificr can utilize the context to deal with membrane disconncctivitics. The features were designed to improve the classification accuracy of elongated structures,978-l-4244-3932-4/09/$25.00 ©2009 IEEE 1039 IS B I2009Fig. 1. Diagram of the proposed method.fa) 0)Fig. 2. Example serial-section TEM images: (a) original and (b) CLAHE enhanced.like the membranes. The non-linear decision boundary is learnt by a classifier trained under the Adaboost framework. The following sections gives the overview of the entire pro­cess, followed by the details on the image enhancement, learning procedures and finally a discussion of the results on an C. elegans dataset.2. M ETHODSWe now describe in detail the proposed method. Figure 1 provides an overview of the fundamental steps.2.1. Image Enhancem entBefore feature extraction, contrast lim ited adaptive histogram equalization (CLAHE) [7] is applied to the raw electron mi­croscopy images. This improves the contrast of the mem­branes, fixes local variations in contrast and overall bright­ness variability between images [4], The decrease in variabil­ity greatly helps the classifier since it reduces the difference between training and testing images. An example of such CLAHE enhancement is shown in figure 2(b).in previous works [4, 5, 6]. The other three features are prop­erties derived from the Gaussian smoothed Hessian matrix.H( x ,  y) = G a *<rr8j:2o2rdyd-j:<rrdj:dyo2rOy2(1)where I  is the (CLAHE enhanced) image, and G a is the Gaussian blurring kernel with standard deviation a. The Hes­sian matrix was used in the context of filtering [8] and seg­menting [6] electron microscopy images. Since membranes are elongated structures the eigenvalues of the smoothened Hessian matrix represent the anisotropic nature of the region around the pixel. The eigenvalue of the principal eigenvector of the Hessian is proportional to the gradient orthogonal to the membrane and the smaller eigenvalue is proportional to the gradient along the cell membrane. The fourth feature is the orientation of the principal eigenvector at that point. The inclusion of the this feature gains significance during learning of the classifier because the neighboring pixels features are also considered.The feature vector for every pixel in the image consists of the concatenated feature values of that pixel and its neigh­bors. The neighborhood is defined by a star shaped stencil with its 8 arms forking out every 45 degrees (figure 3). This neighborhood has the ability to sample a larger context area around the pixel than choosing a complete sample of the local area for the same number of features. We show in the results section that the neighboring pixel features adds relevant in­formation for the classification. The context helps to identify membranes at regions were there are m inor discontinuities, as it allows for the classifier to utilize the context information to “interpolate” the cell membrane. In this regard, the orienta­tion feature plays an important role by imposing a smoothness constraint on the curvature of the membrane.2.2. FeaturesFour features were com puted for each pixel in the image: the pixel intensity, and eigenvalues and orientation of the first eigenvector of the Gaussian smoothed Hessian matrix. The gray value of the pixel is utilized since membranes are usu­ally dark and therefore is useful for segmentation, as verified2.3. ClassifierWe propose to utilize a classifier trained with AdaBoost [9] since such a classifier can model a nonlinear decision bound­ary. AdaBoost is a meta-algorithm that builds the classifier from “weak” classifiers, such as a decision stump. At each round, AdaBoost adds a weak classifier to the set of weak1040Boosting RoundsFig. 4. Scmilog plot o f number of boosting rounds versus the area under the ROC curve for that boosting round.classifiers by training for best classification perform ance ac­cording to samples weights. The sample weights arc varied depending on the classification result o f the previous round, by increasing the weights o f incorrectly classified samples and decreasing the weights o f correctly classified samples. The final classificr is a weighted sum of the weak classifiers according to their accuracy in the training rounds. It has been observed in previous experiments that the obtained classifiers generally do not ovcrfit to training data [10].In this paper, decision stumps arc used for the weak classi­ficr. Decision stumps arc the simplest form o f binary decision trees with just one decision node. The decision stump makes the classification decision based on just the value of a par­ticular feature with rcspcct to a threshold. Given the feature set, desired classification and prior o f the samples, the thresh­old for a particular feature can bc choscn based on the proba­bility distribution functions of membrane and non-mcmbranc classes over the feature values without making any underlying assumption about the distribution o f the feature. This gives the stump of best accuracy compared to the ones built using other metrics like information gain. The AdaBoost mecha­nism along with the decision stump classificr acts as a feature selection mechanism [101.Fig. 5. ROC curves of the classifiers trained with AdaBoost at boosting round 3000 and, for comparison, the ROC for the method by Jurrus et al. [4] is also shown.unbalanced in the order of 1:10 and thus affect the perfor­mance of the classificr. The classificr trained with a balanced dataset (1:1 ratio) had the best accuracy compared to clas­sifiers trained with various ratios o f positive (membrane) and negative (non-mcmbranc) samples, w ith results shown for this case. The negative samples were choscn at random.The feature vectors were generated as described in sec­tion 2.2, with a 7 x 7 neighborhood and Gaussian standard deviation a  =  5. At any location, these parameters yielded 100 features (25 points in the neighborhood x 4 features for every pixel). Initially, the decision stumps were boosted for 10000 rounds and the area under the ROC (averaged over the 5 folds) computed after each round. Wc can observe from figure 4 that the area under the ROC curvc flattens out after around 3000 rounds o f boosting. The corresponding ROCs arc shown in figure 5, and the test images results in Figure 6.Figure 5 clearly shows that the use of neighborhood context combined with proposed feature set yields signifi­cantly better results than thresholding o f the diffusion filter image [4], Moreover, comparing with the results without context inform ation underlines the importance of using the neighborhood for membrane detection.3. RESULTSThe proposed method for cell membrane detection was tested on a C. elegans dataset. The entire volume is made of 149 sliccs o f 662x697  grayscale images. Out of this stack, 5 im­age slices where choscn at random  from the first 50 sliccs and the accuracy o f the method was assessed using 5-fold cross­validation. In each ease, the training was done using four o f the five images and tested on the image that was left out o f training. The ratio o f mcmbranc/non-mcmbranc pixels is4. CO N C LU SIO NThe proposed method utilizes neighborhood context inform a­tion to improve the accuracy of membrane detection. Along with the nonlinear discrimination ability o f the AdaBoost classificr and the Hessian feature set, this results in improved membrane detection compared to previous methods. Thus one can cxpcct a more robust segmentation o f the individ­ual neurons. However, the classificr fails to disccrn certain structures and textures from membranes, which may result in1041Fig. 6. M embrane detection results on the test images of the 5-fold cross-validation: original images (left), and detected membranes (right).over-segmentation of individual neurons. Utilizing additional features that discriminate these regions from membranes may prevent these false positives. Moreover, recent work sug­gests that cascading the classifier predictions as an additional feature set onto another classifier may help connecting up discontinuities in membranes and thereby avoid underseg­mentation f i l l .  Further, considerable post-processing may still be perform ed after applying the proposed pixel classifi­cation to delineate the cells. Future work would address these problems in membrane detection to improve the segmentation accuracy of the individual neurons.5. REFERENCES[1] J. C. Fiala and K. M. Harris, “Extending unbiased stereology of brain ultrastructure to three-dimensional volumes,” J. Am. Med. Inform. Assoc., vol. 8 , no. 1, pp. 1-16, 2001.[2] J.G. White, E. Southgate, J.N. Thomson, and F.R.S Bren­ner, “The structure of the nervous system of the nematode caenorhabditis elegans,” Phil. Trans. Roy. Soc. London Ser. B  B iol Sc i, vol. 314, pp. 1-340, 1986.[3] Kevin L. Briggman and Winfried Denk, “Towards neural circuit reconstruction with volume electron microscopy tech­niques,” Curr. Opin. Neurobiol, vol. 16, no. 5, pp. 562-570, Oct. 2006.[4] E. Jurrus, R.T. Whitaker, B. Jones, R. Marc, and T. Tasdizen, “An optimal-path approach for neural circuit reconstruction,” in Proc. IEEE Int. Sym. on Biomed. Imaging, 2008, pp. 1609­1612.[5] V. Jain, J.F. Murray, F. Roth, S. Turaga, V. Zhigulin, K.L. Brig­gman, M.N. Helmstaedter, W. Denk, and H.S. Seung, “Su­pervised learning of image restoration with convolutional net­works,” in Proc. Int. Conf. on Computer Vision (ICCV), Oct.2007, pp. 1-8.[6 ] Yuriy Mishchenko, “Automation of 3D reconstruction of neu­ral tissue from large volume of conventional serial section transmission electron micrographs,” J. Neurosci. Meth., Sept.2008.[7] Rafael C. Gonzalez and Richard E. Woods, Digital image pro­cessing, Prentice-Hall, 2. ed. edition, 2002.[8 ] T. Tasdizen, R. Whitaker, R. Marc, and B. Jones, “Enhance­ment of cell boundaries in transmission electron microscopy images,” in Proc. Int. Conf. Image Processing (ICIP), 2005, pp. 642-645.[9] Y. Freund and R. E. Schapire, “A decision-theoretic general­ization of on-line learning and an application to boosting,” in Proc. Euro. Conf. on C.ompu. Learning Theory (EuroCOLT), Barcelona, Spain, 1995, Also in: Journal of Computer and System Sciences 55:1, 1997, 119-139.[10] P. Viola and M. Jones, “Rapid object detection using a boosted cascade of simple features,” in Proc. Conf. on Computer Vision and Pattern Recognition (CVPR), 2001, vol. 1, pp. 1—511—I— 518.[11] Zhuowen Tu, “Auto-context and its application to high-level vision tasks,” in Proc. Conf. on Computer Vision and Pattern Recognition (CVPR), jun 2008, pp. 1-8.1042

Automatic markup of neural cell membranes using boosted decision stumps

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Automatic markup of neural cell membranes using boosted decision stumps

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