Pipeline for the Creation of Surface-basedAveraged Brain Atlases

Anja Kuß Hans-Christian Hege Sabine Krofczik Jana BornerZuse Institut Berlin

Takustraße 714195 Berlin, Germany

Freie Universität BerlinKönigin-Luise-Str. 28/3014195 Berlin, Germany

kuss@zib.dehege@zib.de

sabine@krofczik.deboerner@neurobiologie.fu-berlin.de

ABSTRACTDigital atlases of the brain serve as a spatial reference frame which can be used to relate data from differentimage modalities and experiments. In this paper we describe a standardized pipeline for the creation of extendablesurface-based anatomical insect brain atlases from 3D image data of a population of individuals. The pipelineconsists of the major steps imaging and preprocessing, segmentation, averaging, surface reconstruction, and surfacesimplification. At first, 3D image data sets from confocal microscopy are resized, stitched, and initially displayedusing standard image processing and visualization tools. Then brain structures, such as neuropils and neurons, arelabeled by means of manual segmentation and line extraction algorithms. The averaging step comprises affine andelastic registration and a mean shape selection strategy. Finally non-manifold surfaces of the labeled and alignedstructures are reconstructed using a generalized surface reconstruction algorithm. These surfaces are simplifiedand adapted to further needs by decimation and retriangulation. The chosen methods of each step are adequate fora variety of data. We propose an iterative application of the pipeline in order to build the atlas in a hierarchicalfashion, integrating successively more levels of detail. The approach is applied in several different neurobiologicalresearch fields.

Keywords: Digital neuroanatomy, brain atlas, surface reconstruction.

1. INTRODUCTIONOne specific aim of neuroscience is to analyze learn-ing and behavior. Understanding such neural functionsis based on knowledge about the wiring of the brainand functional properties of individual neurons as wellas parts of the nervous system. Therefore anatomicaland functional properties of neural networks are inves-tigated.

Particularly suited for such analysis are insect brains,since they are easy to access and brain substructuresas well as important single neurons are already knownand identified. Furthermore, due to the small size ofthe structures one is able to examine them as a wholedown to a level of single neurons. Much knowledge isextracted by comparing anatomical structures and their

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functions of individuals at several development stagesor from individuals that equal or differ genetically.

In the last decade 3D imaging methods, like confo-cal microscopy or transmission electron tomography,have become standard together with sophisticatedstaining methods. Nowadays they provide largeamounts of data that has to be organized, analyzedand explored. Almost a revolution from the biologicalpoint of view are population-averaged standard atlasesthat for the first time in biology provide spatial refer-ences. These allow the researchers to relate data fromdifferent experiments and different data modalitiesas well as to accumulate information with spatialreference. Furthermore they facilitate visualization ofneurobiological data and form a basis for investigatingthe relation between brain morphology, brain function,and genetic factors. In the long term this will help toturn neuroanatomy into a more quantitative science.

We describe a generalized pipeline for the gen-eration of insect brain atlases from 3D image dataof a population of individual brains. We considersurface-based atlases, since these are easier to dealwith than volumetric atlases. The pipeline roughlycomprises imaging, segmentation, averaging, andsurface reconstruction. All methods required within

the pipeline were integrated in the open visualizationplatform Amira [SWH04].

This paper is organized as follows: In section 2 wegive an overview about existing brain atlases. Sec-tion 3 describes the concept and the steps of the at-las generation pipeline. Applications of the procedureand the resulting atlas are presented in Section 4. Wediscuss the limits of our approach and conclude in Sec-tion 5.

2. RELATED WORKFor the human brain a variety of digital population-based atlases have been introduced and continuouslyenhanced. Techniques for the generation of such brainatlases, for instance, are described in [GMT00] and[TT01]. They focus on the development of registra-tion and averaging strategies using cerebral CT andMRT scans. A processing environment that enablesresearchers to combine and execute established meth-ods is presented in [RMT03]. It has been used suc-cessfully to build average intensity atlases with fullyautomated processing. The general steps for atlas gen-eration of human brains are similar to the ones for thegeneration of insect brain atlases. Nevertheless imag-ing modalities and application dependent objectives re-quire the development of more specific and appropri-ate techniques.

Meanwhile invertebrate brain atlases also doemerge. In [RZM∗02] and [RBMM01] the generationof atlases for the fruitfly Drosophila melanogaster andthe honeybee Apis mellifera are described. Besidesgeneration methods supplemental information onneurobiological backgrounds and application fieldsof insect brain atlases can be found in [BRR∗05]and [PH04]. Web interfaces (http://www.neurofly.de;http://www.neurobiologie.fu-berlin.de/BeeBrain) al-low downloading and interactive viewing of currentlyexisting models. The analysis of insect brains is stillan explorative research field. So far little attentionhas been paid to the integration and sequencing ofall steps needed for a complete atlas generation intoone single visualization platform. The insertionof a taxonomic description of brain structures andthe usage of advanced visualization and interactiontechniques also remain challenging tasks.

3. ATLAS GENERATION PIPELINEThe proposed pipeline for the generation of brain at-lases is composed as follows: First the image data ispreprocessed and initially visualized. In a second stepthe structures of interest are segmented and a label isassigned to each of them. The averaging takes placein step three. Therefor all labeled data sets have tobe registered into a common reference and a mean ormedian has to be derived. Finally three-dimensionalsurface models of the averaged brain structures are re-

Figure 1. Schematic overview of the pipeline for the gen-eration of surface-based insect brain atlases. After imag-ing and preprocessing (1), structures are segmented (2).The averaging of individuals (3) is followed by a surface

reconstruction(4).

constructed. Figure 1 shows a schematic overview ofthe proposed pipeline.

The underlying methods of each step have to fulfillseveral requirements. They must be applicable to thepresent imaging data and provide robust results. Thetechniques shall allow for modifications of final andintermediate data. For example the insertion and dele-tion of structures shall be supported. In addition themethods have to consider the user’s skills and wishes.Finally the received results shall enable further pro-cessing.

We propose an iterative application of the pipeline.First coarse structures like neuropils are reconstructedand an initial atlas is generated. Using the same worksteps, more detailed information such as substructuresof neuropils or neurons is integrated. Thereby the ini-tial standard serves as a reference frame for the regis-tration procedure and the final visualization. This ap-proach allows for a hierarchical organization of data,which in turn establishes a basis for the representationof the natural hierarchical organization of brain struc-tures.

In the following we will describe the steps of thepipeline by means of brain data of the honeybee. Sec-tion 4 shows that the pipeline and its results are muchmore generally applicable.

Imaging and PreprocessingPresently insect brains such as the fly brain or thehoneybee brain are imaged with confocal laser scan-ning microscopy. After preparation including dissec-tion and staining, the size of a honeybee brain (~2.5×1.6 × 0.8 mm) extends the maximum field of view ofthe microscope with a common configuration. There-fore multiple stacks are used to acquire entire brains.Typically 2 × 3 partially overlapping tiles, each using

Figure 2. Slice view of a honeybee brain confocal mi-croscopy image in xy-, yz-, and xz-plane. The data set

is composed of six separately acquired image stacks.

between 80 and 120 slices of 512×512 pixels in planeand a thickness of 8 µm are imaged.

For the combination of the image stacks landmarksthat define corresponding points are placed manually.Afterward the stacks are merged using a conventionalpyramidal blending. The landmarks are used to definethe regions that will be blended. The scanning tech-nique usually causes a shortening of distance in thez-direction. This is compensated by applying a linearscaling to the data [BRR∗05].

By means of slicing or direct volume rendering orthe combination of the two a first insight into the editeddata is gained and the image and preprocessing qualitycan be checked. A slice view of a complete bee braindata set can be found in Figure 2.

SegmentationIn the next step anatomically distinct structures of thebrain are segmented and assigned unique labels. Theresults are stored in so-called label fields in which eachvoxel represents a coding for a particular brain struc-ture. These label fields will be used in the followingaveraging step and form the basis of the surface recon-struction. For several insects a coarse division andnomenclature of the brain into neuropils already hasbeen defined and can be used as a guideline for thesegmentation. In the honeybee brain we currently dis-tinguish 22 major compartments.

The development of fully automatic segmentationmethods is impeded by several difficulties. For in-stance, the borders of neuropils are often fuzzy, thuslacking strong gray value gradients. Gray values ofstructures highly depend on the chosen staining. Ad-ditionally they vary within the regions and betweendifferent individuals. Furthermore research in neuro-biology still remains explorative aiming at discoveringnew structures and correlations. So frequently there islittle a priori information and results are strongly influ-enced by the user’s knowledge and experience.

Figure 3. Segmentation of neuropils in the honeybeebrain. Anatomical structures of interest were segmentedand assigned individual labels. Here they are overlaid

their corresponding gray value data set.

For the segmentation of neuropils we provide simplebut robust, and user-friendly tools, which support man-ual segmentation. The user can choose from freehandboundary drawing, interval thresholding, and propagat-ing contours based on a level set method [WnZ03]. Forthe latter a small circle is placed on mouse click andthen the contour of the circle is extended as the usermoves the mouse. All tools require the adjustment ofonly few parameters and allow for an interactive cor-rection of the segmentation results. Their efficiencyis improved by the integration of interpolation and ex-trapolation of label segmentation between slices. Atany time structures can be added, removed or refinedeasily within the provided editor.

Figure 3 shows an example segmentation of honey-bee brain neuropils.

AveragingFrom the preprocessed and labeled data sets of indi-viduals an atlas representing the brain characteristicsof the analyzed species has to be derived. For this pur-pose an average of the sample is determined. This isusually done by computing the mean or median. Ingeneral the procedure comprises a registration of thedata sets to a common reference and a subsequent av-eraging step.

We use an iterative averaging scheme which is basedon the work presented in [RBMM01]. First global dif-ferences in position, orientation, and size of all datasets are compensated and an initial average image isgenerated. Using a nonrigid registration the data setsare then registered to the initial reference. This leadsto a new less blurred average image to which the trans-formed images are registered again, and so forth.

Affine registration. The procedure starts with anaffine registration of the individual images to an arbi-trarily chosen template. Usually a subjective choice ofa data set such as the original individual brain whichappears most average to the user works as an adequate

first reference. In order to allow for standardizationwe select the individual brain which has the smallestdeviation from the mean volume over all componentsin the reconstruction as it has been used in [RZM∗02].Our affine registration implementation is based on themultiresolution search method presented in [SHH97].

Label and intensity averaging. Having registeredall original individual images, a mean or median canbe determined from them. Here the computation of thevoxelwise arithmetic average of data values forms themost intuitive and general approach. For label fields,which are non-numerical images, a non-arithmetic ave-rage is needed. We do use a method that has beenproposed in [BRR∗05]. It selects the label that occursmost frequently and represents at least t percent of thevalid voxels. A result voxel is termed ‘undecided’ ifthere is no unique most frequent label.

Elastic registration. Due to the remaining localshape varieties an average image solely based on aglobal affine registration might be inaccurate resultingin blurred average intensities and average labels witha large quantity of undecided voxels. Applicationof elastic registration methods helps to reduce thisinaccuracy.

Our pipeline contains a non rigid registrationmethod described in [RZM∗02]. The algorithmcomputes an individual rigid transformation for eachlabeled brain structure. These transformations arerelatively small due to the prior global alignment.For each voxel within a segmented substructure thetransformation vector is calculated based on theper-structure alignment. Afterward the piece-wisedefined vector field between the substructures iscomponent-wise interpolated. This is done by usinga heat transfer equation: at locations where thevectors are known the ‘temperature’, which meansthe component of the transformation vector, is keptfixed and in-between the resulting equilibrium state iscomputed.

We also offer a multiresolution and multigrid cubicB-spline registration based on the developments pub-lished in [RBMM01] and [RSH∗99]. This approachpays more attention to fine details than the above out-lined method.

In general, elastic registration is a delicate step,since the transformation might eliminate not onlypreparation artifacts like mechanical deformations, butalso anatomical variability. If one is interested in this,elastic registration should be employed particularlyconservatively.

Convergence. For some applications an average re-sulting from the affine registration step may be suffi-cient. It preserves possibly relevant shape differencesand requires a comparatively lower computation time.

However, to minimize the number of undecided voxelsand to avoid disappearance of small structures the ap-plication of subsequent non-rigid registration often isnecessary. Here the fraction of undecided voxels in theaverage label images and a visual observation coupledwith an entropy analysis define the choice of the algo-rithm and the termination of the averaging procedure.In [RBMM01] a maximum iteration number of fourhas been proposed. No significant decrease in entropyand the number of undecided voxels could be observedin subsequent registration steps.

Surface generationIn this step surfaces are created that separate regionswith different labels. If more than two differentlylabelled regions meet in space, complex topologicalsituations may arise and the resulting separating sur-face in general is not a manifold. In order to gener-ate such non-manifold surfaces from label fields, weutilize a generalized surface reconstruction algorithm[HSSZ97]. It produces interfaces between all neigh-boring voxels of different type, nicely stitched together.Like standard marching-cubes, it works by processingeach hexahedral cell (dual to the voxel grid) individu-ally and utilizing tables that contain triangulations fordifferent equivalence classes. An equivalence class isa set of cells with labeled corners, where the cells canbe transformed into each other by discrete rotations,reflections, and/or label permutations. While in themarching-cubes case 1 + 13 such topologically dis-tinct classes exist, for more than 2 labels on a cellmore classes may exist. The number of equivalenceclasses can be computed by group theoretical means[BLS05, Heg98]: employing e.g. all before mentionedsymmetry groups simultaneously, for 1 to 8 differentlabels on the eight corners of a cell there are 1, 13, 44,66, 43, 15, 3, 1 different classes. Employing less sym-metry groups, these numbers are significantly higher.Practically we use pre-computed tables only for con-figurations with up to three different labels. Morecomplex cases occur rarely and the corresponding tri-angulations are computed on-the-fly. Figure 4(a) de-picts the outer average surfaces of the major substruc-tures of the honeybee brain. For alternative algorithmssee [BDS∗03] and [BRvL∗05].

The number of triangles resulting from the sur-face generation algorithm typically has to be reduced.Since down-sampling the labeled input data may resultin loss of tiny but relevant structures, we generate thesurface in high resolution and simplify it afterwards,using the method presented in [GH97]. Figures 4(b)and 4(c) show detailed views after reducing thenumber of triangles to 20 % and 5 %, respectively,of the original number of triangles. The simplifiedsurface finally is remeshed in Figure 4(d), using

(a) (b)

(c) (d)

Figure 4. Generation of surfaces separating different biological structures. (a) outer average surfaces of the majorsubstructures of the honeybee brain, (b) after reducing the number of triangles to 20%, (c) after reducing the number

of triangles to 5%, and (d) after remeshing the simplified surface.

an implementation based on algorithms proposedin [SG03].

A surface editor enables the modification of the re-sulting surface. It provides the subdivision, collapse,removement or translation of selected triangles. Ad-ditional patches can be defined. Structures can be se-lected and their associated triangles can be saved asindividual surfaces. These tools are mainly used forfinal visualization purpose. They also support furtherprocessing such as the insertion of new structures andthe subdivision of existing ones.

Data integration

The resulting atlas represents a group of structureswhich can be regarded as a certain level of a hierarchy.On the next level substructures as for example singleneurons or the glomeruli are added. The integrationof additional data follows the above process. Againpreprocessing, segmentation, averaging and surface re-construction are needed in order to insert more detailinto the existing atlas. We exemplary describe the ap-

proach for the integration of a projection neuron. SeeFigure 5 for the visualization of some work steps.

Image acquisition and preprocessing. Projectionneurons (PN) are stained and imaged using confocalmicroscopy. Typically 2×2 tiled stacks of 350 sec-tions each with 1024×1024 voxels are scanned. Theintegration of the PN also requires an acquisition ofits surrounding neuropil regions which are needed forthe registration. Therefore a generic counterstainingis applied and imaging is done according to the aboveprotocol.

To avoid loss of detail usually neither resamplingnor merging of the PN image stacks takes place. Incontrast aiming at lower computation times in the reg-istration step the size of the neuropil data is reducedand the stacks are composed into one data set. Neu-ron images usually contain only parts of the brain andsubsequent steps of the pipeline have to be restrictedto these parts. For this purpose several regions in thelabel image of the atlas have to be removed and thevolume has to be cropped.

(a) (b)

(c) (d)

Figure 5. Neuron integration. (a) Segmentation of a neuronal part in one of the four commonly acquired image stacks.(b) Combined display of surface reconstructions of neuropils surrounding the considered neuron. The individual (light)and the standard neuropils (dark) differ in size and orientation. (c) After affine registration of the individual to thestandard neuropils, the neuron (arrows) sticks out the atlas. Nonrigid registration can compensate for this inaccuracy.

(d) View of a complete bee brain with a selection of integrated neurons

Neuron segmentation. The neuropils belonging tothe considered neuron are segmented as described inSection Segmentation. For the segmentation of theneuron itself more specific methods are needed. Dueto staining intensity variations, the existence of finestructures close to the resolution limit, and a partly in-sufficient resolution in z-direction fully automatic seg-mentation has not been achieved yet. In our pipelinepromising results can be obtained by means of lineextraction algorithms [SED∗04, WnZ03]. Therebythe user manually sets branching or end points thusroughly tracing the graph like structures of the neuron.The centerline and thickness of the neuronal segmentbetween these points are then detected automaticallyfor example by minimizing an energy functional.

Registration of surrounding neuropils. For the inte-gration of the neuron into the atlas the individual neu-ropils and the corresponding neuropils of the standardare registered using the same methods as for comput-

ing the standard. Afterward the resulting transforma-tion is applied to the neuron data.

Surface reconstruction. Using a thickness-annotated graph, which represents the neuron, asurface reconstruction can be easily obtained. Ourpipeline contains an implementation of the methoddescribed in [WnZ03]. Here the neuronal surface isinitially described as a union of a set of spheres andcylinders. Applying an implicit surface algorithm,consistent and closed surfaces are generated that canbe used for volume or surface rendering.

Computational performanceThe computation time for the generation of an atlashighly depends on the number of individual data setsand the chosen registration strategy. An averaged at-las composed of ten individual brains can be createdwithin one week. The following measurements applyto a single data set of size 650 × 400 × 120. Align-ment and merging of the stacks took about two min-

utes. Manual segmentation of the neuropils requiredbetween two and six hours depending on the numberof neuropils and the user’s knowledge. Affine registra-tion needed one hour. Non-rigid registration accordingto [RZM∗02] required one hour whereas the methodpresented in [RBMM01] used up to 12 hours. Labelvoting and intensity averaging required about two min-utes. The surface was generated within 38 seconds andproduced 1.2 · 106. triangles. The simplification downto 2 · 104 triangles required 168 seconds and the finalremeshing took about 3.5 minutes. The computationtimes for the steps of the integration of neuron dataare similar. Speed-up can be observed during registra-tions. This results from the reduction of the data toonly concerning neuropils. All techniques were testedon a current PC (3.0GHz Intel Pentium 4 processor).

4. APPLICATIONSUsing the proposed pipeline, a standard atlas of thehoneybee brain has been generated [BRR∗05]. Thisstandard initially composed of 22 neuropils, is cur-rently extended and refined. In [KMK∗04] progresshas been made in the investigation of the temporal dy-namics of olfactory coding, and the neuronal wiringwithin different integration areas of the olfactory path.Here experimental data were acquired by stimulatingthe antennae with a range of odors and recording the re-sponse from single projection neurons. Individual neu-rons and accordant neuropils were reconstructed andcollected into the framework of the standard atlas ofthe honeybee brain. Thus morphological as well asphysiological properties of brain structures have beenintegrated. This work directly supports the analysis ofneuronal circuits and explores the role of single neu-rons within the network.

The standardized atlas of the fly brain(see [RZM∗02] for details) is upgraded by an av-erage model of the thoracic-abdominal nervoussystem, which contains deeply analyzed insect motorpattern generators and most of the insect individuallyidentified neurons. Multiple stained whole-mountpreparations of the thoracic ganglia are averaged (seeFigure 6 for first results). Afterward morphologicaldetails such as projections of legs, wings and halteresare added to the standard. On the next level neuropilstructures are subdivided based on functional issuesand finally groups of identified neurons are integratedinto the atlas.

Parts of the pipeline have also been used for the gen-eration of a standard atlas out of ten individual locust(Schistocerca gregaria) brains. Here neuropils belong-ing to the first hierarchy of the brain structure ontol-ogy have been averaged. The atlas is intended to forma spatial reference for the analysis of the central com-plex, its substructures, and its nervous system down tosingle neurons [KSH05].

Figure 6. Atlas of fly thoracic ganglion. Shown are theaverage surface reconstruction of three individual fly tho-racic ganglions and an overlaid individual surface recon-

struction (grid)

The most time-consuming step in geometry recon-struction from image data is image segmentation.Using atlases, fully automatic segmentation of imagedata from an unknown subject can be performed.In [RBMMJ04] confocal microscopy images of 20 in-dividual bee brains have been segmented by nonrigidregistration to an atlas. The results were compared toa manually generated gold standard and showed a veryhigh accuracy for most of the considered structures.

5. CONCLUSION AND FUTUREWORK

We presented a standardized pipeline for the creationof population-averaged atlases from 3D image data.The atlases are surface-based and extendable. Ourpipeline consists of a sequence of steps, of which somerequire fine-tuned and sophisticated image and geome-try processing algorithms. The proposed pipeline hasbeen used to create a honeybee ‘standard brain’ and itis applied in a variety of other biological fields now.

Atlases resulting from the proposed procedure serveas spatial references, into which data from other ex-periments can be integrated – while maintaining theirspatial context. Thus a natural organization of infor-mation is achieved that is well suited for visually sup-ported analysis. Utilization of such reference models,augmented with empirical data of diverse provenance,greatly facilitates many scientific investigations. Ex-amples in neurobiology are investigations of relationsbetween brain morphology, brain function, and geneticfactors.

In the future we will also associate ontological in-formation with the spatially referenced substructures.This will provide not only a visual representation ofthe natural ontology of biological structures, but alsowill enable new navigation techniques.

ACKNOWLEDGEMENTSThis work was supported by the German ResearchFoundation (DFG). The authors want to thank TorstenRohlfing, Angela Kurylas and Jürgen Rybak for help-ful information and fruitful discussions. Thanks to JanSahner and Tino Weinkauf for their company.

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