Implementing duct tracking of bronchi and blood vessels in 3D medical data can significantly help in examining
these body structures and reduce time needed for correct diagnosis. Nature of the problem involved may require
some operator interaction when selecting duct branches of interest or correcting possible mistakes in duct path
segmentation. Noise, badly discernible duct walls, branching or extreme duct curvatures, that can be found in
computer tomography data, make the segmentation challenging. There are more ways to display segmented ducts,
as overlay over original data or unrolled to planar tube, allowing easier measurements of length, width or observation
of tube anomalies

Horák, Martin

Pelikán, Josef

Petříček, Martin

DSpace at University of West Bohemia

POSTER: Duct tracking in 3D medical dataMartin PetrˇícˇekFaculty of Mathematics and PhysicsCharles University, PragueMalostranské nám. 25, 11800 Praha 1,Czech Republicmartin@petricek.netJosef PelikánFaculty of Mathematics and PhysicsCharles University, PragueMalostranské nám. 25, 11800 Praha 1,Czech RepublicJosef.Pelikan@mff.cuni.czMartin HorákDepartment of Radiology,Bulovka Hospital, PragueBudínova 2, 180 81 Praha 8 - LibenˇCzech Republicmartin.horak@volny.czABSTRACTImplementing duct tracking of bronchi and blood vessels in 3D medical data can significantly help in examiningthese body structures and reduce time needed for correct diagnosis. Nature of the problem involved may requiresome operator interaction when selecting duct branches of interest or correcting possible mistakes in duct pathsegmentation. Noise, badly discernible duct walls, branching or extreme duct curvatures, that can be found incomputer tomography data, make the segmentation challenging. There are more ways to display segmented ducts,as overlay over original data or unrolled to planar tube, allowing easier measurements of length, width or observa-tion of tube anomalies.Keywords: Image segmentation, visualization, 3D medical data, computer tomography.1 INTRODUCTIONExamination of human body with computer tomogra-phy or magnetic resonance allows to see inside a pa-tient. Unlike X-ray imaging these measurements pro-vide full 3D data, which requires specialized programto view or visualize them.In this paper we specialize on duct tracking. Thisinclude ducts in respiratory system (trachea, bronchi),blood vessels (veins, arteries) and possibly gastroin-testinal tract.First type are bronchi - tubular structures in respira-tory system, usually filled with air, although they canbe relatively commonly blocked by various inhaled ob-jects. Bronchi are probably easier for segmentation, asthey usually contain only air and are the simplest to seg-ment.Second type of ducts in human body are blood vessels- veins and arteries. They are used to transport bloodin human body. Sometimes a contrast agent, such asUltravist, is applied to enhance the density of blood inveins. This make them more contrast and thus morevisible, as we can see in example in Figure 2. In theimage without contrast agent the artery blends with sur-rounding tissues of similar density. With contrast agentapplied, the artery is clearly visible. Quite common fea-ture that can be found in blood vessels is thrombus. ThePermission to make digital or hard copies of all or part ofthis work for personal or classroom use is granted withoutfee provided that copies are not made or distributed for profitor commercial advantage and that copies bear this notice andthe full citation on the first page. To copy otherwise, or re-publish, to post on servers or to redistribute to lists, requiresprior specific permission and/or a fee.Copyright UNION Agency – Science Press, Plzen, Czech Re-publicFigure 1: Following ducts in CT data can be tedious.thrombus has almost the same density as blood, so it isgenerally visible only with help of contrast agent.Third type is gastrointestinal tract (small and largeintestine). These are the hardest to segment. They cancontain liquid or solid content, or even pockets of gas,which can make segmentation problematic, as they havehighly varying densities. Intestines have thin, badly vis-ible walls, the curvature and circumference is varyinglargely as the intestine continues through the body andthe intestines often touch themselves as they are foldedin the abdominal area. Example of slice showing partof gastrointestinal tract can be seen in Figure 3Poster paper proceedings 21 ISBN 978-80-86943-99-2Figure 2: Blood vessel (aorta) - left image is slice fromdata without contrast agent, right image is from datawith contrast agent (Ultravist) appliedFigure 3: Image from gastrointestinal tract with visi-ble pockets of gas, example of very hard segmentationproblem2 PROBLEM DECOMPOSITIONWe have analyzed the problem and decomposed it intomultiple consecutive steps.Figure 4: Problem decomposition into separate stepsFirst optional step is preprocessing. It may improvethe segmentation and the resulting image if some filtersto reduce noise are used, as CT and MRI tend to be gen-erally noisy. Three dimensional variant of Averagingusing a rotating mask [SHB93] with 3×3×3 masks in5×5×5 area can be used, as it preserve edges. Its dis-advantage is computational intensity, because for eachvoxel, brightness dispersion σ2 needs to be computedfor each rotated mask. Median filtering is faster, for3×3×3 mask by factor of 7.30, for 5×5×5 by factorof 1.65. Disadvantage of median filtering can be round-ing of sharp corners, but that might not be noticeable inmost anatomy images. Both noise-removing filters canbe run on series of slices as 2D filters, or extended tofully 3D filter.As the image is usually relatively much larger thanthe area of interest, it is possible to reduce time neededfor preprocessing. Preprocessing can be run only on asmall area around the starting point and then on neigh-bor areas, as the segmentation continues and tracks theduct through the body. As result, the computationallyexpensive filter will be run only on areas that are closeto the area of interest.Second step is entering point of interest. Basically,there are many ducts in human body and user must picksome starting point inside the duct in which he is inter-ested and direction in which to track it.For some types of images and few specific cases, thestarting point can be recognized automatically and maybe offered as a preselection (like position of trachea onimage from chest area).Third step is the segmentation. The hard part is find-ing the duct walls. Once duct walls are identified,finding the axis (centerline) and inside volume is quitestraightforward.Segmentation and visualization will be discussedwith greater detail in next two chaptersFourth step is visualization. There are more possi-ble ways for showing resulting ducts. Either they canbe highlighted in 3D view of original data or they canbe unwinded and displayed separately. Unwinded ductstill remain a 3D image, though.Fifth and the last step is correction. As ducts in bodyoften have branches, user have to pick one of the ways.Also, there will be cases in which the segmentation wasincorrect and algorithm has left the duct. These shouldbe corrected. The algorithm should continue follow-ing the proper duct after adjustment, basically runningagain on part of the duct from third step with modifiedparameters. This requires functions in visualization partthat enable user to specify the corrections.Another possibility is to segment entire duct tree,forking the segmentation at every duct split. Entirebronchi system can be segmented and then displayedor measured this way.3 SEGMENTATIONSegmentation is still work in progress. Most promisingare Active Shape models, also called "snakes": [CT92],[dBvGVN03], or its variant, Active Appearance Mod-els [CET98]. There are many variations to these meth-ods: [XP97], [CC93], [OBFG94], [Coh91].They are contour models, attracted to image features,such as edges. The contour is usually represented as aspline curve and often there are imposed limits on thedeformation to ensure it maintain "reasonable" shape.Poster paper proceedings 22 ISBN 978-80-86943-99-2Figure 5: Points on the curve are moved towardsstrongest edge, proportional to its strength. The pro-cess is repeated for each point until the curve con-verges to approximate the object contourIn the algorithm, first the initial curve is placed in thedata - for tubes it will be initially a circle, or shape ofthe tube from previous slice. Then, the algorithm step isrepeated, until the model converges. In each step, everypoint is moved towards strongest nearby edge along thenormal of the contour in that point. This results in thecontour following the edge.The model needs to be extended to 3D data and slicesthat are generally not parallel to any major axis or toother slices.Another possible approach is not to use 2D de-formable models on moving slices, but use instead a3D deformable model (possibly trying to fit a cylinderon the duct walls). Advantage of such approach ishaving surface representation directly as the result andwe don’t have to deal with artifacts that can result fromextracting slices of arbitrary orientation from the data.As edge detector is part of the method, we also haveto deal with data anisotropy. Very often the resolutionof the data is not uniform, the voxel size in z axis,corresponding to the gap between image slices, is largerthan in x and y axes. This is usually is problematic formany edge detectors. We may solve it by resamplingthe data, modifying the edge detector, or we can useedge detector that can work with anisotropic data, likemethod proposed by Brejl and Sonka[BS00].More methods [Lak00] will be examined before de-ciding which one will be finally used.For finding the first part of the duct, some other waymust be used, as we have no previous duct slice. Theposition and direction may be somewhat inaccurate, asthe user probably won’t pinpoint exact center of theduct and its precise direction. We can use a variant ofthe method, where we allow tilting of the initial planeand try to minimize length of the curve (assuming mini-mal length is at the tube cross-section, this will compen-Figure 6: Branching of aorta, with contrast agent ap-plied. Left image show situation from above, right im-age shows it from sidep0 = initial positiond0 = initial directiond0 = correct orientation(p0,d0)w0 = f ind duct walls(p0,d0)p0 = duct center(w0)for i = 1..length o f duct dopi = pi−1+di−1di = di−1wi = f ind duct walls(pi,di,wi−1)pi = duct center(wi)di = pi− pi−1Array pi holds positionsArray di hold directionsArray wi holds walls slicesAlgorithm 1: Pseudo-code of segmentation algorithm."find duct walls" is the main algorithm to segment thewalls, given the 2D slice specified by (p,d) and posi-tion of duct walls in previous slice (except the first slice,where the initial duct must be found some other way)sate for inaccurate initial direction). After successfulsegmentation, we can update the position to correspondto the centerline of the duct found. Sometimes it is hardto tell exactly what center is, but we use center of masscalculated from the duct walls for this purpose.Result of duct segmentation from Algorithm 1 willbe the centerline represented by series of points locatedapproximately 1 voxel apart. A duct direction vector isdefined in every such point. It is used as normal vectorof plane for extracting a slice from the data. On each ofthese planes a border will be segmented, using informa-tion from last plane, since the planes are close to eachother. As a result of one plane segmentation, centerlineposition and direction will be updated for next step.By approximating the curves from all slices as a poly-gon and joining them it is possible to build polygonal3D model of the duct. Alternatively, we can use B-splines, NURBS or similar smooth surface representa-tion to get nicer results.Poster paper proceedings 23 ISBN 978-80-86943-99-2Figure 7: Example of one segmentation algorithm run,as specified by Algorithm 1. The segmentation startson bottommost slice and moves upwards.4 VISUALIZATIONThere are two visualization modes, 3D mode and "ductunwinding" mode. In 3D mode, the segmented ductswill be highlighted in some way. Either by coloringthe ducts, or by modifying transparency - so the ductswill be almost opaque, while the rest will be almostcompletely transparent. Both approaches could be com-bined. For the 3D mode, we will use part of MedV3Dframework designed to display volume data.In the second mode, slices are created along the ductcenter. Slices can be relatively small (width of the ductand some small part of surrounding tissue), but numberof slices can be relatively large, if the duct is long. Theyare still displayed as 3D data, but in more comprehen-sible form.Figure 8: Schematic of "Duct unwinding" mode: Duct inoriginal position is shown above, while correspondingunwinded duct is shown below. Length of centerlineremain the same.The deformation caused by unwinding will preservelengths in each of the slices created and also length ofthe duct center will be preserved. Other lengths may bemore or less deformed by the transformation, greatestdeformation is in points farthest from centerline and induct segments with greatest curvature.Also, in this mode, specifying the corrections shouldbe much easier than in ordinary 3D mode, as in caseof bad segmentation, the correct duct leaving the re-sulting image can be easily spotted and corrected. SeeFigure 8 for simplified 2D schematic of how this modewill work. Once the duct is segmented, the data can bemeasured (for example volume of duct between certainpoints, its circumference, length, etc ...), as the voxelsize is calibrated and its dimensions are part of the in-put data.5 CONCLUSION AND FUTURE WORKWe have presented method for segmenting, measuringand displaying ducts in medical 3D images using de-formable models, dealing with problems like anisotropyof the data, noise and irregularities in bodily ducts.The described approach aim to reduce time needed forproper patient diagnosis if bodily ducts are involved.ACKNOWLEDGMENTSThis work is part of MedV3D framework, supportedby GAUK grant No. 359/2006 (Charles University inPrague). Thanks to Martin Horák for helping with med-ical data.REFERENCES[BS00] Brejl, M. and Sonka, M. (2000). Directional 3d edge detec-tion in anisotropic data: detector design and performance assess-ment. Comput. Vis. Image Underst., 77(9):84–110.[CC93] Cohen, L. and Cohen, I. (1993). Finite-element methods foractive contour models and balloons for 2-d and 3-d images. PAMI,15(11):1131–1147.[Coh91] Cohen, L. D. (1991). On active contour models and bal-loons. Computer Vision, Graphics, and Image Processing. ImageUnderstanding, 53(2):211–218.[CT92] Cootes, T. and Taylor, C. (1992). Active shape models- -smart snakes.[CET98] Cootes, T. F., Edwards, G. J., and Taylor, C. J. (1998).Active appearance models. Lecture Notes in Computer Science,1407:484–??[dBvGVN03] de Bruijne, M., van Ginneken, B., Viergever, M., andNiessen, W. (2003). Adapting active shape models for 3d segmen-tation of tubular structures in medical images.[Lak00] Lakare, S. (2000). 3d segmentation techniques for medicalvolumes.[OBFG94] O’Donnell, T., Boult, T., Fang, X., and Gupta, A. (1994).The extruded generalized cylinder: A deformable model for objectrecovery.[SHB93] Sonka, M., Hlavac, V., and Boyle, R. (1993). Image Pro-cessing, Analysis, and Machine Vision. Chapman and Hall, Lon-don, New York.[XP97] Xu, C. and Prince, J. (1997). Gradient vector flow: A newexternal force for snakes. In Proceedings of Computer Vision andPattern Recognition (CVPR ‘97), pages 66–71, San Juan, PuertoRico. IEEE.Poster paper proceedings 24 ISBN 978-80-86943-99-2

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Duct tracking in 3D medical data

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Duct tracking in 3D medical data

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