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Int. J. Radiation Oncology Biol. Phys., Vol. 68, No. 2, pp. 522–530, 2007Copyright © 2007 Elsevier Inc.

Printed in the USA. All rights reserved0360-3016/07/$–see front matter

doi:10.1016/j.ijrobp.2007.01.038

HYSICS CONTRIBUTION

AUTOMATIC DELINEATION OF ON-LINE HEAD-AND-NECK COMPUTEDTOMOGRAPHY IMAGES: TOWARD ON-LINE ADAPTIVE RADIOTHERAPY

TIEZHI ZHANG, PH.D., YUWEI CHI, PH.D., ELISA MELDOLESI, M.D. AND DI YAN, D.SC.

Department of Radiation Oncology, William Beaumont Hospital, Royal Oak, MI

Purpose: To develop and validate a fully automatic region-of-interest (ROI) delineation method for on-lineadaptive radiotherapy.Methods and Materials: On-line adaptive radiotherapy requires a robust and automatic image segmentationmethod to delineate ROIs in on-line volumetric images. We have implemented an atlas-based image segmentationmethod to automatically delineate ROIs of head-and-neck helical computed tomography images. A total of 32daily computed tomography images from 7 head-and-neck patients were delineated using this automatic imagesegmentation method. Manually drawn contours on the daily images were used as references in the evaluationof automatically delineated ROIs. Two methods were used in quantitative validation: (1) the dice similaritycoefficient index, which indicates the overlapping ratio between the manually and automatically delineated ROIs;and (2) the distance transformation, which yields the distances between the manually and automaticallydelineated ROI surfaces.Results: Automatic segmentation showed agreement with manual contouring. For most ROIs, the dice similaritycoefficient indexes were approximately 0.8. Similarly, the distance transformation evaluation results showed thatthe distances between the manually and automatically delineated ROI surfaces were mostly within 3 mm. Thedistances between two surfaces had a mean of 1 mm and standard deviation of <2 mm in most ROIs.Conclusion: With atlas-based image segmentation, it is feasible to automatically delineate ROIs on the head-and-neck helical computed tomography images in on-line adaptive treatments. © 2007 Elsevier Inc.

Region-of-interest delineation, Deformable image registration, Adaptive radiotherapy, Image-guided radiotherapy.

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INTRODUCTION

ose escalation has been shown to be effective in improv-ng the outcome of cancer radiotherapy (RT) at various sites1–6). However, collateral damage to normal tissue limitshe maximal dose that can be safely delivered (7). RTlanning uses computed tomography (CT) images, whichepresent a snapshot of the anatomic geometry at the time ofhe treatment planning CT scan. Because of setup variationsnd anatomic changes, a considerable motion margin muste applied to compensate for patients’ anatomic variations.onsequently, a large volume of normal tissue is irradiatedy approximately the same radiation intensity as that re-eived by the target. In recent years, image-guided RTIGRT) has become an important technique in tightening thelanning target volume margin (8, 9). On-line imagingodalities, such as ultrasonography, megavoltage CT (10),

Reprint requests to: Tiezhi Zhang, Ph.D., Department of Radi-tion Oncology, William Beaumont Hospital, 3601 W. Thirteenile Rd., Royal Oak, MI 48073. Tel: (248) 551-6583; Fax: (248)

51-3784. E-mail: tiezhi.zhang@beaumont.eduConflict of interest: none.

cknowledgments—We want to thank Dr. Wolfgang Tomé of theniversity of Wisconsin at Madison and Dr. Todd McNutt of John

opkins University for helping on the Pinnacle Scripting.

522

one-beam CT (CBCT) on-gantry (11), and CT-on-rail (12),rovide on-line volumetric images of the patient duringGRT sessions.

Currently, in IGRT sessions, daily on-line images areegistered into planning CT images by rigid-body imageegistration with six degrees of freedom. Translational po-ition errors may be corrected by shifting the treatmentable. Methods have also been proposed to correct or par-ially correct rotational error by rotating the collimator, theantry, and/or the table (13–15). However, when the shapesr relative positions of the target and organ-at-risk (OAR)hange, simple rigid-body correction techniques may not beufficient for high-precision RT.

On-line adaptive plan adjustment using daily anatomiceometry and position setup may compensate for target andAR interfraction variation. A plan adjustment method haseen proposed to modify the beam aperture and to deform

This research was partially supported by the Department ofefense, Prostate Cancer Research Program, under award number81XWH-07-0083.Received June 9, 2006, and in revised form Dec 18, 2006.

ccepted for publication Jan 16, 2007.

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523Auto-segmentation of on-line HN images ● T. ZHANG et al.

uence map in intensity-modulated RT (16). It might alsoe possible to reoptimize the intensity-modulated RT plann-line by using high-performance computing in dose cal-ulation and plan optimization. On-line plan adjustmentan, in principle, take account of all setup variation, as wells anatomic geometry changes. It may eventually become aractical form of IGRT.However, the major obstacle that hinders the clinical

mplementation of on-line adaptive RT is on-line region-f-interest (ROI) delineation. In three-dimensional treat-ent planning, ROI contouring is a tedious and time-

onsuming task for physicians. It is impractical toanually contour all ROIs with the patient on the table.fast, robust, and automatic ROI delineation method is

eeded for on-line adaptive treatments.Atlas-based image segmentation might be a solution

or on-line plan adjustments (17–19). Figure 1 shows therocedure of atlas-based image segmentation. An atlas isreference image data set with previously contoured

OIs. In on-line adaptive RT, the planning CT image andontours are used as the atlas. The planning images areonrigidly registered to the daily on-line images. ROIs onhe planning image are mapped onto daily images usingoxel-matching information from deformable image reg-stration. The key component in atlas-based image seg-entation is deformable image registration. In this study,e implemented a fast variational-based deformable im-

ge registration algorithm (20, 21). The algorithm wassed in the atlas-based segmentation of daily head-and-eck (HN) CT images. Automatically delineated ROIsere quantitatively validated through a comparison withanual contours.

METHODS AND MATERIALS

atient dataA conventional helical CT scanner (Tomoscan SR7000, Philipsedical System, Shelton, CT) was used for image acquisition. SevenN cancer patients were enrolled in the study. In addition to the

reatment planning CT images, 3–6 weekly CT images were acquiredor each patient during the RT course. A total of 32 weekly imagesere acquired from the patients. All images had a slice thickness of

Reference Images and Manual Contours of ROIs

Target Images

Deformable Image Registration

Transforming Reference Contours

ig. 1. Flow chart of atlas-based image segmentation. Atlas (ref-rence images with manual contours) was registered to targetmages. Regions of interests of atlas were mapped to target imagesubsequently.

–3 mm. The resolution was 512 � 512, and the pixel size was 0.973 w

m in the transverse plane. Weekly images were used as surrogatesf on-line images in the study. Unlike treatment planning CT, nontravenous contrast was administered in the weekly CT acquisitions.

anual ROI delineationA commercial treatment planning system (Pinnacle, Philipsedical System, Madison, WI) was used in the contouring ofOIs. A physician manually contoured all ROIs on the planning

mages, which included the gross tumor volume (GTV), mandible,rainstem, parotids, and lymph nodes. Another physician repeatedhe contouring on all planning and on-line images independently.ecause of the lack of contrast and other medical information, theTV was not contoured on the on-line images.All ROIs were dumped into binary ROI masks for data process-

ng. A binary ROI mask M is a three-dimensional matrix withoxels labeled 1 inside and 0 outside the ROI. It is defined as

M(x) ��1 ⇔ x � O,

0, elswhere,(1)

here O is a spatial domain that manifests the ROI volume, and xs a voxel vector.

eformable image registrationDeformable image registration maximizes the similarity be-

ween the reference image R and the floating image T by warpinghe floating image and, also, keeps the displacement vector fieldsmooth. A general form of the objective function of grayscale-ased deformable image registration is given by the followingquation (22):

F(u f) � D(R, T(u f)) � �S(u f), � � 0 (2)

here D is the measure of similarity with respect to the imagentensity, S is the smoothness regularizer, uf is the displacementector field, and � is a regulation parameter that controls theelative weights between the two components in the function F.

We chose the sum of the square difference between the twomages as the measure of similarity D and the sum of the squareradient of voxel displacement as the smoothness regularizer S,hich are defined as

D � �x��

�R(x) � T(x � u f)�2dx, (3)

S � �x��

��u f

�x�2dx, (4)

here � is the domain of the registration. We used a variational-ased optimization scheme, in which the local minimum of thebjective function was obtained by solving Euler-Lagarange equa-ions (21, 22).

Principally, deformable image registration searches the globalinimum of the objective function. Even though locally R and T

ave different values, the optimization algorithm will still be ableo find the best global matching between the images. Thus, theeformable image registration algorithm is applicable to the im-ges with different voxel intensities, such as CT images with and

ithout contrast or helical CT images vs. CBCT images.

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524 I. J. Radiation Oncology ● Biology ● Physics Volume 68, Number 2, 2007

Multiresolution approaches have been widely used in deform-ble image registration to alleviate the problem of local minima.e used a multigrid approach to register the image pair with

oarse to fine grids at different registration levels (23). As well asaintaining the smoothness of displacement field, the existence of

he smoothness regularizer S also limits the range of deformation.he algorithm can be reluctant to match the regions with lowontrast and large deformation. To remedy this problem, we resethe displacement fields to zero when registration was completed atach registration level and used the deformed images as newoating images in the next registration level. The displacementelds were saved before resetting.

utomatic image segmentationDeformable image registration transforms floating images with

he displacement map uf. Thus, we used the planning CT images asoating images and the on-line images as reference images ineformable image registration, so that the displacement map uf

ould be directly used to transform ROI masks on planning imagesithout reversion. The ROI masks on planning images Mp were

ransformed onto the on-line images according to

Md (x) � Mp(x � u f), (5)

here Md is the ROI masks on daily on-line images.We used a Pinnacle Script to generate binary masks of reference

OIs. The reference ROI masks were transformed onto on-linemages using displacement maps from deformable image registra-ion. As a consequence, target images were segmented automati-ally. We used a custom-developed algorithm to convert the ROIasks into Pinnacle’s ROI file format so that the ROIs can be

isplayed in Pinnacle.

ice similarity coefficient indexThe dice similarity coefficient (DSC) index (24) has been

idely used in the evaluation of deformable image registrationesults. In this study, manually contoured ROIs were compared toutomatically contoured ROIs using the DSC index. The DSC

Fig. 2. Evaluation of differences between manually and atransformation. (Left) Reference (manual) ROI mask; (Mvoxels of target (automatic) ROI mask. Nearest distancecontoured ROI surface was obtained by overlaying dista

ndex is defined as c

DSC �2a

2a � b � c�

2n{O1 � O2}

n{O1} � n{O2}(6)

here O1 is the set of voxels of manual (reference) ROI; O2 is theet of voxels of automatic (test) ROI; a, representing properelineation, is the number of voxels common to both data sets; b ishe number of voxels unique to O1; c is the number of voxelsnique to O2; and n{O} is the number of voxels in O. The DSConformality index approaches 1 when the reference ROI and testOI overlap exactly.

istance transformationA distance map is an image in which the value of each voxel is

he distance from the pixel to a given set or object. A Euclideanistance transformation (DT) is an algorithm that computes anuclidean distance map from a binary image representing this setf voxels (25). In this study, two DTs were performed on theinary masks of manually contoured ROIs. The first DT yielded aistance map with Euclidean voxel distances outside the ROIurfaces. Then, the ROI masks were reversed so that the ROIolumes were labeled with 0 inside and 1 outside. A second DT ofhe reversed ROI masks yielded the Euclidean voxel distancesnside the ROI. The two distance maps were combined withositive distances outside and negative distances inside the ROI.he nearest distances between the reference ROI surfaces and

arget ROI surfaces were obtained by overlaying the target ROIurface voxels onto the distance matrix of the reference ROI. Theistance value at each corresponding surface voxel position washe nearest distance of this surface voxel to the surface of theeference ROI. In this study, the manually contoured ROIs weresed as references and the automatically generated ROIs were useds the target ROIs. The DT validation method is shown schemat-cally in Fig. 2.

Three-dimensional DT transformations were performed on bi-ary ROI masks. However, for some ROIs, the ends of the manualontour on the transverse CT slices were not well defined in themages and involved large human variations. To avoid that, theuperior end of the brainstem and the inferior end of the spinal

tically delineated regions of interest (ROIs) by distanceistance map of reference ROI mask; and (Right) surface

een manually contoured ROI surface and automaticallyaps of reference ROI onto target ROI surface mask.

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525Auto-segmentation of on-line HN images ● T. ZHANG et al.

RESULTSeformable image registrationDaily CT images and planning CT images were used as

eference images and floating images in deformable imageegistration, respectively. The results of deformable image

Fig. 3. Deformable image registration of head-and-nec(Middle) after deformable image registration. (Right) Pgreen, floating images).

Fig. 4. (Top) Manually drawn contours on planningdelineated regions of interest on on-line images. Contotomography images by fully automatic atlas-based imagtumor volumes in on-line images. Red indicates gross tu

spinal cord; and blue, mandible.

egistration are shown in Fig. 3. Each image registrationrocess took 10–15 min using a Dell computer workstationith a 3.0-GHz Intel Xeon CPU and 8-G memory. As

hown in Fig. 3, the images were different before imageegistration and highly similar after registration. Minor dis-

puted tomography images. Images (Left) before andstration images by tiled views (gray, reference images;

uted tomography images and (Bottom) automaticallyon-line images transformed from planning computedentation. Note the consistency of delineation of gross

olume; light blue, nodes; purple, parotid glands; green,

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526 I. J. Radiation Oncology ● Biology ● Physics Volume 68, Number 2, 2007

repancies likely stemmed from the intrinsic differencesetween the image pairs. For example, the dental fillingrtifacts in the image pairs caused difficulty in registration.

utomatic segmentationWith the deformation information from deformable image

egistration, the daily images were automatically delineatedccording to Eq. 5. Figure 4 shows examples of the manuallyphysician) contoured atlas and automatically contoured on-ine images at a treatment day. Qualitatively, the automaticallyelineated ROIs on on-line images were consistent with man-ally delineated ROIs on planning images.

uantitative validationAnother physician also contoured daily CT images. Theanually contoured ROIs were used as references. Auto-atically generated contours were compared with the cor-

esponding manual contours. The discrepancies between theanually and automatically generated contours might have

een caused by image registration errors, as well as humanariation. Figure 5 shows an example of the three-dimen-ional surfaces of the manually and automatically delineatedOIs of the same image data set.The discrepancies between the manual and automatic

ontours were evaluated with the DSC index. The human-elineated ROIs on the daily images were used as refer-nces. The DSC indexes of each ROI in each daily imageata set were calculated (Fig. 6).

Discrepancies between the manual and automatic delin-ation were also measured by DT. Figure 7 shows theistograms of the distances between the manually and au-omatically contoured ROI surfaces for a typical patient.he distances between the two surfaces were centered close

o zero, and the variations were mostly within 2–3 mm.able 1 lists the mean values and standard deviations of allOI discrepancies for all patients. The discrepancies be-

ween the manual and automatic ROIs all had a mean of 1–3m and standard deviation within 2 mm.

utomatic delineation of GTVAutomatic delineation of the GTV is also necessary for

n-line adaptive treatment. One major advantage of atlas-basedmage segmentation is that the boundaries of the ROIs are notecessary located on image features such as the edges. Figure 4hows the automatically delineated GTVs on the on-line images.

However, manual delineation of targets in HN cancer mayot be consistent (26, 27). In some cases, because of its lowontrast to surrounding normal tissue, the GTV can be difficulto be delineated on the on-line images without additionalnformation, such as positron emission tomography and mag-etic resonance imaging. Moreover, the on-line CT imagessed in this study were acquired without intravenous contrast.herefore, the GVT was difficult for the physician to contourith the limited information provided. Thus, automatic seg-entation of the GTV was not compared with the manual

ontours, as was done for the other ROIs in the present study.hysicians visually inspected the GTV contours on the on-line

mages and accepted the automatic segmentations of the GTV.

DISCUSSION

In this study, we implemented and validated an automatictlas-based image segmentation method to delineate the ROIsf daily HN CT images. Automatic segmentation transformshe contours on planning images into the contours on dailymages. Atlas-based image segmentation has many featureshat are suitable for on-line adaptive treatments. These include

. Atlas-based image segmentation incorporates a prioriknowledge about the shape and image characteristics ofthe ROIs.

. Atlas-based image segmentation is able to contour the ROIwith a boundary that is not located on the edge. The position

ig. 5. Three-dimensional surface rending of manual (red) andutomatic (blue) regions of interest. (Top) All regions of interestxcept for nodes. (Bottom) Nodal regions of interest. Overlaidurfaces show overall agreement between regions of interest gen-rated by computer and human.

of contours can be interpolated from surrounding structures.

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527Auto-segmentation of on-line HN images ● T. ZHANG et al.

. Atlas-based image segmentation is able to contour allROIs at once.

The second feature is crucially important for on-linedaptive treatment. For RT planning, some ROIs do not

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Fig. 6. Dice similarity coefficient (DSC) indexes for aregions of interests were used as references in evaluatio

ave significant contrast against the background. Physicians u

elineate these through the use of previous knowledge ofhe shape and relative position of the ROIs to more prom-nent structures. Atlas-based image segmentation mimicshe knowledge of the anatomy and radiology experts. It

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528 I. J. Radiation Oncology ● Biology ● Physics Volume 68, Number 2, 2007

haracteristics in the segmentation process. The bound-ry of some structures, such as the GTV, does not lie onhe image edge. Atlas-based image segmentation maynterpolate ROI boundaries from nearby structures withore obtrusive contrasts.Deformable image registration relies on image quality. In

he present study, we used CT images from a fan-beam CTcanner. CT-on-rail provides daily helical CT images. Oururrent automatic ROI delineation method can be directlypplied to IGRT by CT-on-rail. Still in its infant stage,BCT on-gantry is becoming a popular on-line imagingodality for IGRT. The image quality of CBCT is inferior

o that of a fan-beam CT scanner. Both scatters and approx-

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ig. 7. Histograms of surface distances between manual regions ofnterest and automatic regions of interest. ROI � regions ofnterest.

Table 1. Mean followed by standard devautomatically

Pt.No.

Totalimages

Brain stem(mm)

Spinal cord(mm)

Man(m

1 3 �0.61 �0.36 �1.20 1.07

2 5 �1.33 0.522.30 1.25

3 4 �0.65 0.571.47 1.07

4 4 �0.94 0.061.96 1.22

5 5 0.40 0.51 �2.07 1.14

6 6 �0.13 0.76 �1.63 1.08

7 5 0.10 0.771.88 1.08

All 32 �0.45 0.401.79 1.13

Abbreviations: Pt. No. � patient number; N

* Nodes of Patient 5 were included in gross tumo

mate image reconstruction cause significant image arti-acts and distortions. More importantly, the signal/noiseatio is dramatically low compared with that of regularan-beam CT images. Thus, additional improvementsay be necessary to make the algorithm immune to the

efects of CBCT.Dental filling artifacts often exist in some HN CT images.

igure 8 shows the automatic segmentation results of HNmages with dental filling artifacts. The influence of thertifacts was constrained to a proximate region of a dentallling. Only the mandible contours on a few slices wereistorted. Other ROIs were not significantly affected. ManyT reconstruction algorithms have been developed to elim-

nate or reduce dental filling artifacts. Implementation ofhese techniques in on-line CT imaging modalities would beeneficial in HN on-line adaptive treatments.Deformable image registration does not guarantee point-

o-point matching. Point-to-point matching is very difficulto validate because the true information is unknown. Previ-us studies warped the image data set with predeterminedisplacement fields and then registered the warped imageata set back to their original shapes (18, 21). In reality,owever, organ shape changes are far more complex thanny deformation model can simulate. Fortunately, exactoint-to-point matching is unnecessary in on-line re-plan-ing, in which only the ROI boundaries are used. Additionalnvestigations are necessary for off-line adaptive RT, inhich point-to-point information is mandatory to obtain

ccumulated doses.We validated atlas-based segmentation through a com-

arison with manual delineation. The discrepancies betweenhe reference manual contours and automatic generated con-ours included registration errors, as well as human varia-ions. This quantitative validation method is only valid

of discrepancies between manually andted contours

Right parotid(mm)

Left parotid(mm)

Lymph nodes(mm)

0.07 0.54 1.721.51 1.76 3.320.77 2.24 1.622.91 2.72 3.270.53 1.54 2.351.80 2.62 4.910.51 0.91 0.961.83 2.02 2.310.09 0.72 NA*1.42 1.79 NA*1.46 1.81 1.362.28 2.41 2.730.37 0.14 0.821.60 1.75 2.350.54 1.13 1.471.91 2.15 3.15

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529Auto-segmentation of on-line HN images ● T. ZHANG et al.

hen the ROI can be accurately defined by a human, suchs has been done for the kidney (28). We have performedepeat manual delineation by two physicians on a few dataets. This limited study showed that all the ROIs, except forhe GTV, can be reliably delineated using daily CT imagesithout contrast. Larger scale of studies should be per-

ormed to quantify the component of interphysician varia-ions in the overall discrepancies.

Automatic delineation of the GTV is extremely importantor on-line adaptive treatment. We have experienced diffi-ulty in the manual delineation of the GTV in daily imagesithout intravenous contrast. As a consequence, automati-

Fig. 8. (Top) Manual and (Bottom) automatic delineaAutomatic delineation of mandible in third slice distorte

ally generated GTV contours were not quantitatively val- a

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1

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daptive RT.

CONCLUSION

Atlas-based image segmentation can automatically delin-ate the ROIs on daily CT images. Quantitative validationsemonstrated that this method was robust in contouringOIs in daily HN fan-beam CT images. Atlas-based image

egmentation can be a key component in IGRT by way ofn-line replanning, as well as for other on-line adaptive

of head-and-neck images with dental filling artifacts.mage artifacts.

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