Insight on automated lesion delineation methods for PET data
© Firouzian et al.; licensee Springer. 2014
Received: 12 September 2014
Accepted: 26 November 2014
Published: 14 December 2014
Defining tumour volume for treatment response and radiotherapy planning is challenging and prone to inter- and intra-observer variability. Various automated tumour delineation methods have been proposed in the literature, each having abilities and limitations. Therefore, there is a need to provide clinicians with practical information on delineation method selection.
Six different automated positron emission tomography (PET) delineation methods were evaluated and compared using National Electrical Manufacturer Association image quality (NEMA IQ) phantom data and three in-house synthetic phantoms with clinically relevant lesion shapes including spheres with necrotic core and irregular shapes. The impact of different contrast ratios, emission counts, realisations and reconstruction algorithms on delineation performance was also studied using similarity index (SI) and percentage volume error (%VE) as performance measures.
With the NEMA IQ phantom, contrast thresholding (CT) performed best on average for all sphere sizes and parameter settings (SI = 0.83; %VE = 5.65% ± 24.34%). Adaptive thresholding at 40% (AT40) was the next best method and required no prior parameter tuning (SI = 0.78; %VE = 23.22% ± 70.83%). When using SUV harmonisation filtering prior to delineation (EQ.PET), AT40 remains the best method without prior parameter tuning (SI = 0.81; %VE = 11.39% ± 85.28%).
For necrotic core spheres and irregular shapes of the synthetic phantoms, CT remained the best performing method (SI = 0.83; %VE = 26.31% ± 38.26% and SI = 0.62; %VE = 24.52% ± 46.89%, respectively). The second best method was fuzzy locally adaptive Bayesian (FLAB) (SI = 0.83; %VE = 29.51% ± 81.79%) for necrotic core sphere and AT40 (SI = 0.58; %VE = 25.11% ± 32.41%) for irregular shapes. When using EQ.PET prior to delineation, AT40 was the best performing method without prior parameter tuning for both necrotic core (SI = 0.83; %VE = 27.98% ± 59.58%) and complex shapes phantoms (SI = 0.61; %VE = 14.83% ± 49.39%).
CT and AT40/AT50 are recommended for all lesion sizes and contrasts. Overall, considering background uptake information improves PET delineation accuracy. Applying EQ.PET prior to delineation improves accuracy and reduces coefficient of variation (CV) across different reconstructions and acquisitions.
KeywordsPET Delineation Oncology Radiotherapy Tumour volume Reconstruction
18F-2-Fluoro-2-deoxy-D-glucose (18F-FDG) positron emission tomography (PET) provides information about the metabolic activities of tissue cells and is widely used in cancer management. Cancer cells have increased cellular metabolism of glucose and therefore their 18F-FDG uptake is typically higher than healthy tissue cells . Based on this uptake, the location and extent of cancerous tumours can be determined to support tumour staging, evaluation of treatment response and radiotherapy planning. FDG PET provides complementary information to anatomical imaging modalities such as computed tomography to aid the differentiation between healthy and malignant tissue. It also allows determination of metabolic tumour volume (MTV) and consequently total lesion glycolysis (TLG)  which have shown prognostic and predictive value in oncology ,. PET can also be combined with computed tomography in radiation treatment planning for more accurate gross tumour volume (GTV) definition . In addition, 18F-FDG PET can show metabolic inhomogeneity inside the tumour which can be used to identify areas in the tumour which may benefit from additional radiation .
Currently, tumour volume delineation is typically performed manually by visual interpretation of PET or computed tomography images, which is prone to inter- and intra-observer variability -. The relatively low spatial resolution of PET and noise contribute to this variation by making the lesion boundaries less well defined. Numerous studies have proposed a variety of automatic delineation methods to overcome the subjectivity of tumour volume delineation, with the most commonly used being thresholding a volume of interest (VOI) including the tumour by 40% or 50% of maximum standard uptake value (SUVmax) within the VOI. This method is used in routine clinical practice and provides clinical benefits ,. Other automatic PET delineation methods include contrast-oriented , gradient-based , adaptive thresholding , background-subtracted relative-threshold level  and statistical modelling (FLAB)  and their performance have been compared in several studies -. These studies used either clinical data with histology, computed tomography or manually drawn contours as ground truth or phantom or simulation data with known ground truth but reconstructed with a single reconstruction protocol. They showed that the performance of a delineation method depends on imaging parameters (i.e. reconstruction settings, image noise level, tumour characteristics, contrast) and you need to choose the suitable delineation method accordingly to get the optimum results.
In this study, we used National Electrical Manufacturer Association (NEMA) image quality (IQ)  phantom data acquired with a range of contrasts and emission counts and reconstructed with a range of reconstruction protocols, using the manufactured sphere sizes as ground truth. Additionally more clinically relevant, heterogeneous and irregularly shaped lesions were studied using synthetic phantoms created in-house and reconstructed with the same set of contrasts, emission counts and reconstruction protocols as for the NEMA IQ phantom. Using phantom data enabled us to validate against a real ground truth and therefore assess the performance of the methods more precisely and objectively. We used this phantom data to evaluate a range of automatic tumour delineation methods for PET data which use SUV as the classification feature (first-order feature), independently from the inventors of the methods themselves.
The performance of automatic PET delineation methods based on SUV will be affected by factors known to impact SUV such as scanner type and reconstruction parameters . However, there have been limited studies that considered the impact of these factors on tumour volume assessment . Therefore, an additional objective of this study was to assess quantitatively the effect of those factors on the automatic delineation methods and compare their performance before and after correction for differences in recovery .
In practice, it is very important to consider the clinical objective when selecting the delineation method. For example, in radiotherapy, the aim is to delineate the extent of the tumour as precisely as possible to avoid unnecessary irradiation of healthy tissue. As such, the mean accuracy of the method would be the key performance measure. For tumour response assessment, the objective is accurate determination of change in metabolic volume. As such, it may be preferred to use a method which is consistent across all reconstructions and acquisitions accepting any tendency to over- or under-estimate the true volume. The objective of this study is to provide information to inform the selection of delineation method.
Six different automatic PET delineation methods were evaluated using NEMA IQ phantom and three additional synthetic phantom data sets created in-house.
The NEMA IQ phantom was prepared with an 18F solution to produce a background activity concentration of 5.2 kBq/ml. The six spheres (10-, 13-, 17-, 22-, 28- and 37-mm diameter) were filled to produce a hot sphere-to-background ratio of either 4:1 or 8:1. The phantom was placed in the 64 slice Biograph mCT scanner (Siemens Healthcare, Erlangen, Germany) such that the plane through the centre of the spheres was aligned with the centre of the PET axial field of view and the centre of the lung insert was aligned with the centre of the transaxial field of view. For each concentration ratio, a 1-h listmode acquisition was performed. Using an electrocardiogram (ECG) simulator, the listmode data was gated and divided into ten replicate sinograms for each of 3.0 × 107 and 6.0 × 107 net true coincidences. Each replicate was reconstructed with four different reconstruction protocols representing a range of clinically relevant configurations; namely: 3-dimensional ordinary Poisson ordered subset expectation maximisation (3D OP-OSEM) with 3 iterations, 24 subsets and 5-mm full width at half maximum (FWHM) Gaussian post filter; 3D OP-OSEM + time of flight (TOF) with 2 iterations, 21 subsets, 2-mm FWHM Gaussian post filter; 3D OP-OSEM + point spread function (PSF) with 3 iterations, 24 subsets and 2-mm FWHM Gaussian post filter; 3D PSF + TOF with 2 iterations, 21 subsets and 2-mm FWHM Gaussian post filter. Each reconstruction protocol was performed on both a 200 × 200 and 400 × 400 matrix, giving voxel dimensions of 4.073 × 4.073 mm with a 2.027-mm slice thickness, and 2.036 × 2.036 mm with a 2.027-mm slice thickness, respectively. In total, 6 (spheres) × 2 (contrasts) × 4 (reconstruction protocols) × 3 (emission counts) × 10 (replicates) × 2 (matrix sizes) were acquired, making a total of 2,880 objects to be delineated.
SUV is intended to reduce the effect of patient size and body composition relative to the injected dose of radiotracer and making it possible to compare between different studies and patients ,. However, there are other factors that can introduce bias to the quantification process, such as reconstruction protocol. An additional reconstruction-specific Gaussian filter (EQ.PET filter) was proposed to overcome this bias . The filter size for a given reconstruction protocol is determined by minimising the root mean squared error (RMSE) between the recovery coefficients (RCs) of a NEMA IQ phantom reconstructed with that protocol and those of a common reference . The Gaussian filters aligning the recovery coefficients for each reconstruction protocol to the reference have been applied prior to automatic delineation and the results compared to those generated with no additional filtering. The EQ.PET filter sizes for OP (with 3 iterations, 24 subsets and 5-mm FWHM Gaussian post filter), OP + TOF (with 2 iterations, 21 subsets, 2-mm FWHM Gaussian post filter), PSF (with 3 iterations, 24 subsets and 2-mm FWHM Gaussian post filter) and PSF + TOF (with 2 iterations, 21 subsets and 2-mm FWHM Gaussian post filter) were 4.76, 7.35, 6.52 and 7.41, respectively.
Automated PET delineation methods
For each phantom, each object being delineated was enclosed in a bounding VOI containing no other objects. Six different automatic PET delineation methods were applied to the VOIs, with and without prior EQ.PET filtering, including:
• Thresholding at 40% SUVmax (T40): delineates all voxels with SUVs above or equal to 40% of the maximum SUV inside the selected VOI.
• Thresholding at 50% SUVmax (T50): delineates all voxels with SUVs above or equal to 50% of the maximum SUV inside the selected VOI.
where mSUV 70 is the mean SUV in the region generated by thresholding the VOI at 70% of SUVmax and BG is the mean SUV in a background region. Parameters a and b are calculated using a set of NEMA IQ phantom acquisitions to determine a regression function that best represents the relationship between the optimal threshold for that phantom and its mSUV 70 and BG. In this study, we performed the regression analysis using the NEMA IQ phantom data described in the Physical phantom section.
• Fuzzy locally adaptive Bayesian (FLAB) : an unsupervised statistical method using a fuzzy model within the Bayesian framework. It allows coexistence of voxels belonging to one of the two hard classes and voxels belonging to a ‘fuzzy level’ inside the selected VOI.
For methods which required background uptake information, a spherical background region (20 voxels in diameter) was manually positioned in the body of the phantom away from any spherical inserts or synthetic lesions. All the above methods except FLAB have been developed in house. The FLAB implementation was acquired from the inventors directly and was applied without any modifications in the method itself or its parameter settings.
Given the true position and size of phantom spheres is known for the NEMA IQ phantom, ground truth was created on the PET data by fitting a sphere of the appropriate volume to each of the phantom spheres to create a binary mask. The VOIs generated with the automated methods described above were validated against this ground truth. For the synthetic phantoms, the ground truths were the binary masks used as input to the PET simulator.
where GT is the number of voxels in the ground truth binary mask, PET is the number of voxels in the delineated binary mask produced by the delineation method, Vol PET is the volume of the automatically delineated sphere and Vol true is the mathematically calculated volume in case of the NEMA IQ phantom and the initial mask volume for the synthetic phantoms.
Results and discussion
Automatically delineated VOIs from the NEMA IQ phantom and the synthetic phantoms were compared against ground truth and SI and %VE were calculated for all lesion sizes, clinically relevant reconstruction protocols, contrasts and emission counts. The results are presented with and without applying prior EQ.PET filtering.
Results for NEMA IQ phantom
As it was presented in the results above, effect of applying EQ.PET filter can be different on large and small lesions. The filtering reduces noise in the image and makes it more homogeneous but also reduces contrast between foreground and background. Homogeneity contributes to consistency across all reconstructions and acquisitions by making these images more similar to each other in terms of SUVs. It can also suppress local maxima in background regions, preventing their inclusion in the delineated volume, which can occur with low contrast lesions. EQ.PET typically improved accuracy for large lesions; for example, the AT40 mean %VE decreased from −34.27% to −22.65% for large lesions. For small lesions, EQ.PET typically reduced delineation accuracy; for example, the AT40 mean %VE increased from −1.14% to 79.48% for small lesions. Small lesions often have low image contrast and by further reducing the contrast with EQ.PET, background regions are more likely to be included in the delineation.
Results for synthetic phantoms
The results from the first synthetic phantom with NEMA size spheres are in line with those from the physical NEMA IQ phantom data as we observed that CT also showed the highest SI (0.73). Similarly to what was done for the analysis of the physical NEMA IQ phantom results, the data was divided into two groups of large (17, 22, 28 and 37 mm) and small (7, 10 and 13 mm) spheres. Adding an extra small sphere (7 mm) to the phantom decreased the accuracy for delineating small spheres for all methods with respect to the physical phantom. For example, CT accuracy (SI) for small spheres decreased from 0.73 to 0.54, although it remained the same as the physical phantom for large spheres. After applying the EQ.PET filter, AT40 had the highest SI (0.71) and the mean CV improved approximately 1.6-fold for all sphere sizes and delineation methods. The accuracy (SI) for small spheres decreased from 0.43 to 0.38 and for large spheres increased from 0.80 to 0.84. The effect of contrast and number of emission counts was similar to the physical phantom.
Practical insights on selecting a lesion delineation method
Based on the results presented in this study, CT is the best performing method for all lesion types but its utility is limited by the requirement for prior parameter tuning using phantom data which might not be available in all clinical sites. For the experiments performed on the real NEMA IQ phantom data, the method parameters (a and b) were calculated on the same data as used for the assessment, which could result in an overly optimistic assessment of the performance of the method. However, this was not the case for the simulated phantom data and the performance observed with these datasets was in agreement with the real phantom data. Furthermore, whilst these phantom-tuned parameters perform well on the more complex lesion shapes used in this study, we have not evaluated their performance on heterogeneous lesions (beyond those with a necrotic core), or lesions in a heterogeneous background. AT40 or AT50 show only marginally reduced performance and require no prior tuning. AT performs especially well on small lesions even with low contrast. CT and AT are both using background uptake information which makes them more capable of handling small low contrast lesions. For large high contrast lesions, most methods perform reasonable but with different accuracy levels. Depending on the application and availability, clinicians can decide whether they would like to opt for slightly higher accuracy with prior tuning or not. These findings are consistent with a previous study comparing different PET delineation methods (except FLAB) where they found AT40 and CT are the best performing methods for assessing lesion sizes in comparison to thresholding, relative thresholding , absolute SUV and gradient-based watershed  method . We did not observe the improved performance of FLAB relative to other methods that has been reported by the inventors of the method in a previous study . This may be because the FLAB implementation we used had not been optimised for the reconstruction protocols or types of objects used in this study.
For methods requiring information on background uptake (CT and AT), the background region needs to be defined by the user which might introduce some variations in the results. For this study, it was not the case since the phantom body is a homogeneous structure, but in clinical practice, the background region needs to be selected carefully. For FLAB, the background uptake is estimated from the initial VOI and therefore the results might vary based on different VOI selections. FLAB allows the user to tune various parameters to the data, but in this study, we avoided user interference to minimise any bias in our results. The VOI definition for thresholding methods does not introduce variations in the lesion size but might include false positives if it is too large.
To give a more clinical context to the results, the best performing scenario for small spheres in the NEMA phantom, PSF reconstruction with CT gives a mean %VE of 2.4% ± 31.0%. Of relevance to radiotherapy planning, this translates to a mean volume error of 19.0 ml ± 66.0 ml. For MTV-based response assessment, this translates to a 95% confidence interval of approximately 61% on the volume measurement. For large spheres, the best scenario would be OP-OSEM with T40 which gives a mean %VE of −2.7% ± 7.8% which is translated to a volume error of −305.4 ± 762.4 ml and translates into confidence interval of 15.3%.
Considering background uptake information in the delineation process improves accuracy, especially for small, low contrast and heterogeneous lesions. There was less variation in performance for large, high contrast lesions. CT was the most accurate method on average but requires additional parameter tuning that might not be possible at all clinical sites. The accuracy of AT40 was only marginally lower than CT and requires no additional parameter tuning. Amongst the parameters investigated, lesion size and contrast had the biggest impact on the relative performance of the delineation methods evaluated. The variation in delineated volumes for small lesions, even for the simple NEMA spheres, was generally very high (CV greater than 1) and relatively low (CV smaller than 0.5) for large lesions across the different reconstruction methods. Applying EQ.PET filtering to the data prior to delineation reduced the variation in delineated volume for all lesion sizes (e.g., CV less than 0.15 for lesions greater than 10-mm diameter with AT methods). EQ.PET filtering had a variable effect on delineation accuracy, typically improving the performance for large lesions and reducing it for small lesions.
positron emission tomography
- NEMA IQ:
National Electrical Manufacturer Association image quality
percentage volume error
coefficient of variation
metabolic tumour volume
total lesion glycolysis
gross tumour volume
volume of interest
standard uptake value
fuzzy locally adaptive Bayesian
full width at half maximum
- 3D OP-OSEM:
3-dimensional ordinary Poisson ordered subset expectation maximisation
time of flight
point spread function
root mean squared error
The authors would like to thank Ian Armstrong of the Manchester Royal Infirmary, UK for providing the NEMA IQ phantom data used in this study.
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