Image segmentation in treatment planning for prostate cancer using the region growing technique

Iterative-cuts: longitudinal and scale-invariant segmentation via user-defined templates for rectosigmoid colon in gynecological brachytherapy

Among all types of cancer, gynecological malignancies belong to the fourth most frequent type of cancer among women. In addition to chemotherapy and external beam radiation, brachytherapy is the standard procedure for the treatment of these malignancies. In the progress of treatment planning, localization of the tumor as the target volume and adjacent organs of risks by segmentation is crucial to accomplish an optimal radiation distribution to the tumor while simultaneously preserving healthy tissue. Segmentation is performed manually and represents a time-consuming task in clinical daily routine. This study focuses on the segmentation of the rectum/sigmoid colon as an organ-at-risk in gynecological brachytherapy. The proposed segmentation method uses an interactive, graph-based segmentation scheme with a user-defined template. The scheme creates a directed two-dimensional graph, followed by the minimal cost closed set computation on the graph, resulting in an outlining of the rectum. The graph's outline is dynamically adapted to the last calculated cut. Evaluation was performed by comparing manual segmentations of the rectum/sigmoid colon to results achieved with the proposed method. The comparison of the algorithmic to manual result yielded a dice similarity coefficient value of [Formula: see text], in comparison to [Formula: see text] for the comparison of two manual segmentations by the same physician. Utilizing the proposed methodology resulted in a median time of [Formula: see text], compared to 300 s needed for pure manual segmentation.

Renal perfusional cortex volume for arterial input function measured by semiautomatic segmentation technique using MDCT angiographic data with 0.5-mm collimation

OBJECTIVE

The purpose of this study was to evaluate the usefulness of renal perfusional cortex volume for arterial input function.

MATERIALS AND METHODS

This retrospective study included 45 potential kidney donors--33 patients with aortic dissection and 12 patients with renovascular hypertension--who underwent both MDCT angiography with 0.5-mm collimation and renal (99m)Tc-diethylenetriamine pentaacetic acid (DTPA) scanning using the modified Gates method. Each perfusional cortex volume for the arterial input function and parenchymal volume was measured by semiautomatic segmentation using the region-growing technique. Linear regression analysis and correlation coefficients were used to assess the impact of the cortical volume, parenchymal volume, and renal scanning glomerular filtration rate (GFR) on estimated GFR (eGFR) using a modified Modification of Diet in Renal Disease (MDRD) equation.

RESULTS

The correlation coefficient was higher for the total renal DTPA GFR adjusted for body surface area, weight-adjusted perfusion cortex volume, and adjusted total parenchyma volume in rank (r = 0.712, 0.642, 0.510, respectively, p< 0.0001 for each). The coefficient of the right renal perfusional cortex volume percent with a mean value of 52.1% ± 10.1% was 0.826 (p < 0.0001) for the right renal DTPA GFR percent with a mean value of 51.0% ± 12.1% (range, 22.0-89.5%), although the value for the right renal parenchymal volume percent with a mean value of 49.5% ± 5.5% was 0.764 (p < 0.0001).

CONCLUSION

Weight-adjusted perfusional cortex volume for arterial input function can be measured clinically and may replace renal DTPA scanning using the modified Gates method.

Automated prostate segmentation in whole-body MRI scans for epidemiological studies

The whole prostatic volume (PV) is an important indicator for benign prostate hyperplasia. Correlating the PV with other clinical parameters in a population-based prospective cohort study (SHIP-2) requires valid prostate segmentation in a large number of whole-body MRI scans. The axial proton density fast spin echo fat saturated sequence is used for prostate screening in SHIP-2. Our automated segmentation method is based on support vector machines (SVM). We used three-dimensional neighborhood information to build classification vectors from automatically generated features and randomly selected 16 MR examinations for validation. The Hausdorff distance reached a mean value of 5.048 ± 2.413, and a mean value of 5.613 ± 2.897 compared to manual segmentation by observers A and B. The comparison between volume measurement of SVM-based segmentation and manual segmentation of observers A and B depicts a strong correlation resulting in Spearman's rank correlation coefficients (ρ) of 0.936 and 0.859, respectively. Our automated methodology based on SVM for prostate segmentation can segment the prostate in WBI scans with good segmentation quality and has considerable potential for integration in epidemiological studies.

Concurrent segmentation of the prostate on MRI and CT via linked statistical shape models for radiotherapy planning

PURPOSE

Prostate gland segmentation is a critical step in prostate radiotherapy planning, where dose plans are typically formulated on CT. Pretreatment MRI is now beginning to be acquired at several medical centers. Delineation of the prostate on MRI is acknowledged as being significantly simpler to perform, compared to delineation on CT. In this work, the authors present a novel framework for building a linked statistical shape model (LSSM), a statistical shape model (SSM) that links the shape variation of a structure of interest (SOI) across multiple imaging modalities. This framework is particularly relevant in scenarios where accurate boundary delineations of the SOI on one of the modalities may not be readily available, or difficult to obtain, for training a SSM. In this work the authors apply the LSSM in the context of multimodal prostate segmentation for radiotherapy planning, where the prostate is concurrently segmented on MRI and CT.

METHODS

The framework comprises a number of logically connected steps. The first step utilizes multimodal registration of MRI and CT to map 2D boundary delineations of the prostate from MRI onto corresponding CT images, for a set of training studies. Hence, the scheme obviates the need for expert delineations of the gland on CT for explicitly constructing a SSM for prostate segmentation on CT. The delineations of the prostate gland on MRI and CT allows for 3D reconstruction of the prostate shape which facilitates the building of the LSSM. In order to perform concurrent prostate MRI and CT segmentation using the LSSM, the authors employ a region-based level set approach where the authors deform the evolving prostate boundary to simultaneously fit to MRI and CT images in which voxels are classified to be either part of the prostate or outside the prostate. The classification is facilitated by using a combination of MRI-CT probabilistic spatial atlases and a random forest classifier, driven by gradient and Haar features.

RESULTS

The authors acquire a total of 20 MRI-CT patient studies and use the leave-one-out strategy to train and evaluate four different LSSMs. First, a fusion-based LSSM (fLSSM) is built using expert ground truth delineations of the prostate on MRI alone, where the ground truth for the gland on CT is obtained via coregistration of the corresponding MRI and CT slices. The authors compare the fLSSM against another LSSM (xLSSM), where expert delineations of the gland on both MRI and CT are employed in the model building; xLSSM representing the idealized LSSM. The authors also compare the fLSSM against an exclusive CT-based SSM (ctSSM), built from expert delineations of the gland on CT alone. In addition, two LSSMs trained using trainee delineations (tLSSM) on CT are compared with the fLSSM. The results indicate that the xLSSM, tLSSMs, and the fLSSM perform equivalently, all of them out-performing the ctSSM.

CONCLUSIONS

The fLSSM provides an accurate alternative to SSMs that require careful expert delineations of the SOI that may be difficult or laborious to obtain. Additionally, the fLSSM has the added benefit of providing concurrent segmentations of the SOI on multiple imaging modalities.

Accumulating daily-varied dose distributions of prostate radiation therapy with soft-tissue-based kV CT guidance

Even with daily image guidance based on soft tissue registration, deviations of fractional doses can be quite large due to changes in patient anatomy. It is of interest to ascertain the cumulative effect of these deviations on the total delivered dose. Daily kV CT data acquired using an in‐room CT for five prostate cancer patients were analyzed. Each daily CT was deformably registered to the planning CT using an in‐house tool. The resulting deformation field was used to map the delivered daily dose onto the planning CT, then summed to obtain the cumulative (total delivered) dose to the patient. The delivered cumulative values of prostate D100 on average were only 2.9% less than their planned values, while the PTV D95 were 3.6% less. The delivered rectum and bladder V70s can be twice what was planned. The less than 3% difference between delivered and planned prostate coverage indicates that the PTV margin of 5 mm was sufficient with the soft‐tissue–based kV CT guidance for the cases studied.

PACS number: 87.55.km

Learning image context for segmentation of the prostate in CT-guided radiotherapy

Accurate segmentation of the prostate is the key to the success of external beam radiotherapy of prostate cancer. However, accurate segmentation of the prostate in computer tomography (CT) images remains challenging mainly due to three factors: (1) low image contrast between the prostate and its surrounding tissues, (2) unpredictable prostate motion across different treatment days and (3) large variations of intensities and shapes of the bladder and rectum around the prostate. In this paper, an online-learning and patient-specific classification method based on the location-adaptive image context is presented to deal with all these challenging issues and achieve the precise segmentation of the prostate in CT images. Specifically, two sets of location-adaptive classifiers are placed, respectively, along the two coordinate directions of the planning image space of a patient, and further trained with the planning image and also the previous-segmented treatment images of the same patient to jointly perform prostate segmentation for a new treatment image (of the same patient). In particular, each location-adaptive classifier, which itself consists of a set of sequential sub-classifiers, is recursively trained with both the static image appearance features and the iteratively updated image context features (extracted at different scales and orientations) for better identification of each prostate region. The proposed learning-based prostate segmentation method has been extensively evaluated on 161 images of 11 patients, each with more than nine daily treatment three-dimensional CT images. Our method achieves the mean Dice value 0.908 and the mean ± SD of average surface distance value 1.40 ± 0.57 mm. Its performance is also compared with three prostate segmentation methods, indicating the best segmentation accuracy by the proposed method among all methods under comparison.

[Automatic image segmentation for treatment planning in radiotherapy]

One drawback of the growth in conformal radiotherapy and image-guided radiotherapy is the increased time needed to define the volumes of interest. This also results in inter- and intra-observer variability. However, developments in computing and image processing have enabled these tasks to be partially or totally automated. This article will provide a detailed description of the main principles of image segmentation in radiotherapy, its applications and the most recent results in a clinical context.

The effect of concurrent androgen deprivation and 3D conformal radiotherapy on prostate volume and clinical organ doses during treatment for prostate cancer

In this study, we investigated the shrinking effect of concurrent three-dimensional conformal radiotherapy (3D-CRT) and androgen deprivation (AD) on prostate volume, and its possible impact on the dose received by the rectum and bladder during the course of 3D-CRT. The difference between the prostatic volumes determined on pre-treatment planning CT (PL-CT) and post-treatment CT (PT-CT) following a 3D-CRT course was assessed in 52 patients with localised prostate carcinoma. The changes in mean prostate volume when compared with PL-CT and PT-CT-based measurements were assessed. The pre- and post-treatment mean prostate volumes for the whole study population were 49.7 cm(3) and 41.0 cm(3) (p _ 0.02), respectively. The study cohort was divided into two groups depending on the duration of neoadjuvant androgen deprivation (NAD): 23 patients (44.7%) were designated as "short NAD" (< or =3 months; SNAD) and the remaining 29 (55.3%) as "long NAD" (>3 months; LNAD). Patients on SNAD experienced a significantly greater reduction in prostate volume compared with those on LNAD (14.1% vs 5.1%; p _ 0.03). A significant increase in rectum V(40-60) values in PT-CT compared with PL-CT was demonstrated. LNAD patients had significantly higher rectal V(50-70) values at PT-CT compared with the SNAD group. There was a significant decline in V(30)-V(75) bladder values in PT-CT compared with PL-CT in the SNAD group. In conclusion, a higher prostate volume reduction during 3D-CRT was demonstrated when RT planning was performed within 3 months of NAD. However, this reduction and daily organ motion may lead to an unpredictable increase in rectal doses.

Automatic 3D segmentation of prostate in MRI combining a priori knowledge, Markov fields and Bayesian framework

Accurate localization and contouring of prostate are important issues in prostate cancer diagnosis and/or therapies. Although several semi-automatic and automatic segmentation methods have been proposed, manual expert correction remains necessary. Our paper introduces an original method for automatic 3D segmentation of the prostate gland from Magnetic Resonance Imaging data. We use a statistical shape model as a priori knowledge, and we model gray levels distribution by fitting histogram modes with a Gaussian mixture. Markov fields are used to introduce contextual information regarding voxels neighbourhood. Final labelling optimization is based on Bayesian a posteriori classification, estimated with the Iterative Conditional Mode algorithm (ICM). We compared the accuracy of this method, free from any manual correction, with contours outlined by an expert radiologist. In 6 random cases, including prostates with cancer and benign prostatic hypertrophy (BPH), mean Hausdorff distance (HD) and Overlap Ratio (OR) were 9.94 mm and 0.83, respectively. Beyond fast computing times, this new method showed satisfying results, even at prostate's base and apex.

Combining a deformable model and a probabilistic framework for an automatic 3D segmentation of prostate on MRI

PURPOSE

Accurate localization and contouring of prostate are crucial issues in prostate cancer diagnosis and/or therapies. Although several semi-automatic and automatic segmentation methods have been proposed, manual expert correction remains necessary. We introduce a new method for automatic 3D segmentation of the prostate gland from magnetic resonance imaging (MRI) scans.

METHODS

A statistical shape model was used as an a priori knowledge, and gray levels distribution was modeled by fitting histogram modes with a Gaussian mixture. Markov fields were used to introduce contextual information regarding voxels' neighborhoods. Final labeling optimization is based on Bayesian a posteriori classification, estimated with the iterative conditional mode algorithm.

RESULTS

We compared the accuracy of this method, free from any manual correction, with contours outlined by an expert radiologist. In 12 cases, including prostates with cancer and benign prostatic hypertrophy, the mean Hausdorff distance and overlap ratio were 9.94 mm and 0.83, respectively.

CONCLUSION

This new automatic prostate MRI segmentation method produces satisfactory results, even at prostate's base and apex. The method is computationally feasible and efficient.

[Dosimetric evaluation of an automatic segmentation tool of pelvic structures from MRI images for prostate cancer radiotherapy]

PURPOSE

An automatic segmentation tool of pelvic structures from MRI images for prostate cancer radiotherapy was developed and dosimetric evaluation of differences of delineation (automatic versus human) is presented here.

MATERIALS AND METHODS

CTV, rectum and bladder were defined automatically and by a physician in 20 patients. Treatment plans based on "automatic" volumes were transferred on "manual" volumes and reciprocally. Dosimetric characteristics of PTV (V(95), minimal, maximal and mean doses), rectum (V(50), V(70), maximal and mean doses) and bladder (V(70), maximal and mean doses) were compared.

RESULTS

Automatic delineation of CTV did not significantly influence dosimetric characteristics of "manual" PTV. Rectal V(50) and V(70) were not significantly different; mean rectal dose is slightly superior (43.2 versus 44.4Gy, p=0.02, Student test). Bladder V(70) was significantly superior too (19.3 versus 21.6, p=0.004). Organ-at-risk (OAR) automatic delineation had little influence on their dosimetric characteristics; rectal V(70) was slightly underestimated (20 versus 18.5Gy, p=0.001).

CONCLUSION

CTV and OAR automatic delineation had little influence on dosimetric characteristics. Software developments are ongoing to enable routine use and interobserver evaluation is needed.

Early clinical evaluation of a novel three-dimensional structure delineation software tool (SCULPTER) for radiotherapy treatment planning

Modern radiotherapy treatment planning (RTP) necessitates increased delineation of target volumes and organs at risk. Conventional manual delineation is a laborious, time-consuming and subjective process. It is prone to inconsistency and variability, but has the potential to be improved using automated segmentation algorithms. We carried out a pilot clinical evaluation of SCULPTER (Structure Creation Using Limited Point Topology Evidence in Radiotherapy) - a novel prototype software tool designed to improve structure delineation for RTP. Anonymized MR and CT image datasets from patients who underwent radiotherapy for bladder or prostate cancer were studied. An experienced radiation oncologist used manual and SCULPTER-assisted methods to create clinically acceptable organ delineations. SCULPTER was also tested by four other RTP professionals. Resulting contours were compared by qualitative inspection and quantitatively by using the volumes of the structures delineated and the time taken for completion. The SCULPTER tool was easy to apply to both MR and CT images and diverse anatomical sites. SCULPTER delineations closely reproduced manual contours with no significant volume differences detected, but SCULPTER delineations were significantly quicker (p<0.05) in most cases. In conclusion, clinical application of SCULPTER resulted in rapid and simple organ delineations with equivalent accuracy to manual methods, demonstrating proof-of-principle of the SCULPTER system and supporting its potential utility in RTP.

Automatic segmentation of bladder and prostate using coupled 3D deformable models

In this paper, we propose a fully automatic method for the coupled 3D localization and segmentation of lower abdomen structures. We apply it to the joint segmentation of the prostate and bladder in a database of CT scans of the lower abdomen of male patients. A flexible approach on the bladder allows the process to easily adapt to high shape variation and to intensity inhomogeneities that would be hard to characterize (due, for example, to the level of contrast agent that is present). On the other hand, a statistical shape prior is enforced on the prostate. We also propose an adaptive non-overlapping constraint that arbitrates the evolution of both structures based on the availability of strong image data at their common boundary. The method has been tested on a database of 16 volumetric images, and the validation process includes an assessment of inter-expert variability in prostate delineation, with promising results.

Automatic Segmentation of Pelvic Structures From Magnetic Resonance Images for Prostate Cancer Radiotherapy

PURPOSE

Target-volume and organ-at-risk delineation is a time-consuming task in radiotherapy planning. The development of automated segmentation tools remains problematic, because of pelvic organ shape variability. We evaluate a three-dimensional (3D), deformable-model approach and a seeded region-growing algorithm for automatic delineation of the prostate and organs-at-risk on magnetic resonance images.

METHODS AND MATERIALS

Manual and automatic delineation were compared in 24 patients using a sagittal T2-weighted (T2-w) turbo spin echo (TSE) sequence and an axial T1-weighted (T1-w) 3D fast-field echo (FFE) or TSE sequence. For automatic prostate delineation, an organ model-based method was used. Prostates without seminal vesicles were delineated as the clinical target volume (CTV). For automatic bladder and rectum delineation, a seeded region-growing method was used. Manual contouring was considered the reference method. The following parameters were measured: volume ratio (Vr) (automatic/manual), volume overlap (Vo) (ratio of the volume of intersection to the volume of union; optimal value = 1), and correctly delineated volume (Vc) (percent ratio of the volume of intersection to the manually defined volume; optimal value = 100).

RESULTS

For the CTV, the Vr, Vo, and Vc were 1.13 (+/-0.1 SD), 0.78 (+/-0.05 SD), and 94.75 (+/-3.3 SD), respectively. For the rectum, the Vr, Vo, and Vc were 0.97 (+/-0.1 SD), 0.78 (+/-0.06 SD), and 86.52 (+/-5 SD), respectively. For the bladder, the Vr, Vo, and Vc were 0.95 (+/-0.03 SD), 0.88 (+/-0.03 SD), and 91.29 (+/-3.1 SD), respectively.

CONCLUSIONS

Our results show that the organ-model method is robust, and results in reproducible prostate segmentation with minor interactive corrections. For automatic bladder and rectum delineation, magnetic resonance imaging soft-tissue contrast enables the use of region-growing methods.

Prostate contouring uncertainty in megavoltage computed tomography images acquired with a helical tomotherapy unit during image-guided radiation therapy

PURPOSE

To evaluate the image-guidance capabilities of megavoltage computed tomography (MVCT), this article compares the interobserver and intraobserver contouring uncertainty in kilovoltage computed tomography (KVCT) used for radiotherapy planning with MVCT acquired with helical tomotherapy.

METHODS AND MATERIALS

Five prostate-cancer patients were evaluated. Each patient underwent a KVCT and an MVCT study, a total of 10 CT studies. For interobserver variability analysis, four radiation oncologists, one physicist, and two radiation therapists (seven observers in total) contoured the prostate and seminal vesicles (SV) in the 10 studies. The intraobserver variability was assessed by asking all observers to repeat the contouring of 1 patient's KVCT and MVCT studies. Quantitative analysis of contour variations was performed by use of volumes and radial distances.

RESULTS

The interobserver and intraobserver contouring uncertainty was larger in MVCT compared with KVCT. Observers consistently segmented larger volumes on MVCT where the ratio of average prostate and SV volumes was 1.1 and 1.2, respectively. On average (interobserver and intraobserver), the local delineation variability, in terms of standard deviations [Deltasigma = radical(sigma2MVCT-sigma2KVCT)], increased by 0.32 cm from KVCT to MVCT.

CONCLUSIONS

Although MVCT was inferior to KVCT for prostate delineation, the application of MVCT in prostate radiotherapy remains useful.

Evaluation of image-guided radiation therapy (IGRT) technologies and their impact on the outcomes of hypofractionated prostate cancer treatments: A radiobiologic analysis

PURPOSE

To quantify the mitigation of geometric uncertainties achieved with the application of various patient setup techniques during the delivery of hypofractionated prostate cancer treatments, using tumor control probability (TCP) and normal tissue complication probability.

METHODS AND MATERIALS

Five prostate cancer patients with approximately 16 treatment CT studies, taken during the course of their radiation therapy (77 total), were analyzed. All patients were planned twice with an 18 MV six-field conformal technique, with 10- and 5-mm margin sizes, with various hypofractionation schedules (5 to 35 fractions). Subsequently, four clinically relevant patient setup techniques (laser guided and image guided) were simulated to deliver such schedules.

RESULTS

As hypothesized, the impact of geometric uncertainties on clinical outcomes increased with more hypofractionated schedules. However, the absolute gain in TCP due to hypofractionation (up to 21.8% increase) was significantly higher compared with the losses due to geometric uncertainties (up to 8.6% decrease).

CONCLUSIONS

The results of this study suggest that, although the impact of geometric uncertainties on the treatment outcomes increases as the number of fractions decrease, the reduction in TCP due to the uncertainties does not significantly offset the expected theoretical gain in TCP by hypofractionation.

Accuracy evaluation of image registration and segmentation tools used in conformal treatment planning of prostate cancer

Segmentation and registration tools are commonly used in radiotherapy for target and at risk organs localisation. In this work the performances of three different segmentation tools and of a surface matching registration technique, used on computed tomography (CT) and magnetic resonance (MR) images for the treatment planning of conformal prostate carcinoma, are studied. The accuracy of the segmentation and registration tools was evaluated by phantom experiment and on patient data, respectively. A preliminary estimate of MR image distortion was also performed.

Do differences in target volume definition in prostate cancer lead to clinically relevant differences in normal tissue toxicity?

PURPOSE

Many studies have described the quantitated differences between clinicians in target volume definition in prostate cancer. However, few studies have looked at the clinical effects of this. We aimed to assess the relevance and sequelae of such differences.

METHODS AND MATERIALS

Five experienced radiation oncologists were given the clinical details of 5 patients with early-stage prostate cancer and asked to define the clinical target volume, consisting of the prostate and seminal vesicles (CTV1) and the prostate alone (CTV2), on specified planning CT scans of the pelvis. Planning target volumes (PTV1) were generated by automatic expansion of the CTV1 by a 1-cm margin. The PTV2 was defined as the CTV2. The rectum and bladder were defined by a single experienced clinician for each plan without knowledge of the involved clinician marking the CTVs. The Pinnacle planning system was used to generate the plans, using four-field conformal radiotherapy, to deliver 64 Gy in 32 fractions to the PTV1 followed by a boost of 10 Gy to the PTV2 (Medical Research Council RT01 trial protocol). Dose-volume histograms were generated for the whole bladder and rectum for each plan and the volume receiving a specific percentage of the dose (e.g., V(90)) calculated for 74 Gy, followed by estimates of normal tissue complication probabilities (NTCPs) for the bladder and rectum.

RESULTS

Statistically significant differences were found in the CTV1 and CTV2 and, consequently, the PTV1 among the 5 clinicians (p < 0.0005). Most of the discrepancies occurred at the delineation of the prostatic apex and seminal vesicles, with the smallest variance noted at the rectum-prostate and bladder-prostate interfaces. No statistically significant differences were found among clinicians for the rectal V(90), V(85), V(80), V(70), or V(50) or for the bladder V(85), V(80), V(70), or V(50). A difference was noted among consultants for the bladder V(90) (p = 0.015), although no correlation was found between the bladder V(90) and the size of the outlined volumes. No statistically significant differences were found between the estimates of bladder (p = 0.1) and rectal (p = 0.09) NTCPs.

CONCLUSION

The statistically significant difference in outlined volumes of the CTV1, CTV2, and PTV1 among the 5 clinicians is in keeping with the findings of previous studies. However, the interclinician variability did not result in clinically relevant outcomes with respect to the irradiated volume of rectum and bladder or NTCP. This may have been because the outlined areas in which interclinician differences were smallest (the rectal-prostate and prostate-bladder interfaces) are the areas that have the greatest effect on normal tissue toxicity. For the areas in which the interclinician correlation was lowest (the prostatic apex and distal seminal vesicles), the effects on normal tissue toxicity are smallest. The results of this study suggest that interclinician outlining differences in prostate cancer may have less clinical relevance than was previously thought.

Evaluation of a segmentation procedure to delineate organs for use in construction of a radiation therapy planning atlas

OBJECTIVES

This paper evaluates a semi-automatic segmentation procedure to enhance utilizing atlas based treatment plans. For this application, it is crucial to provide a collection of 'reference' organs, restorable from the atlas so that they closely match those of the current patient. To enable assembling representative organs, we developed a semi-automatic procedure using an active contour method.

METHOD

The 3D organ volume was identified by defining contours on individual slices. The initial organ contours were matched to patient volume data sets and then superimposed on them. These starting contours were then adjusted and refined to rapidly find the organ outline of the given patient. Performance was evaluated by contouring organs of different size, shape complexity, and proximity to surrounding structures. We used representative organs defined on CT volumes obtained from 12 patients and compared the resulting outlines to those drawn by a radiologist.

RESULTS

A strong correlation was found between the area measures of the delineated liver (r = 0.992), lung (r = 0.996) and spinal cord (r = 0.81), obtained by both segmentation techniques. A paired Student's t-test showed no statistical difference between the two techniques regarding the liver and spinal cord (p > 0.05).

CONCLUSION

This method could be used to form 'standard' organs, which would form part of a whole body atlas (WBA) database for radiation treatment plans as well as to match atlas organs to new patient data.