1

Marco Brambilla , Ph.D

Head Medical Physics Department University Hospital –Novara Secretary General of EFOMP

Biological Target Volume Delineation with 18F-FDG PET/CT for Radiotherapy Planning

Vienna

5-9 October 2015

European Federation of Organizations for Medical Physics

2

3

Biological Target Volume

4

Accurate, robust, reproducible and fast delineation of functional tumour uptake volumes in three dimensions using positron emission tomography (PET) has been identified as a pressing challenge for an increasing number of oncology applications, such as image-guided radiotherapy, diagnosis, prognosis and therapy response assessment

A standardized way of converting PET signals into target

volumes is not yet available. Various basic approaches were reported in the literature to

accurately contour FDG PET based GTVs.

INTRODUCTION

5

OUTLINE METHODS FOR TARGET VOLUME DELINEATION IN 18F-FDG

PET/CT 1. Visual contouring of PET scan and definition of

contours as judged by the experienced physician

2. Absolute Thresholds: Thresholding by a fixed percentage of the

2a. maximum SUV 2b. mean SUV 3b. maximum intensity 3. Adaptive Thresholding algorithms 4. Complex algorithms (Gradient- based, Statistical

methods) Robustness Studies Validation studies

SUV>2.5 40%

Th = f(∅lesione) Th = f(CNRlesione)

6

1. Visual Contouring

Author Reference District Patient n°

Faria SL et al Int J Radiat Oncol Biol Phys 2008;79:1035-8 Lung 32

Schwartz D et al. Head Neck 2005;27:478-487 Head Neck 63

Heron DE et al. Int J Radiat Oncol Biol Phys 2004;60:1419-1424 Head Neck 21

Nestle U et al Int J Radiat Oncol Biol Phys 1999;44:593-597 Lung 34

Kiffer JD et al Lung Cancer 1998;19:167-177 Lung 15

This method is the first one applied and still widely used Easily applicable from a technical point of view

METHODS FOR TARGET VOLUME DELINEATION IN PET

But …

7

Significant differences in GTV delineation between multiple observers (A,B,C,D) contouring on PET/CT fusion, were reported Thus, confirming the need for a delineation protocol.

Riegel et al. Int J Radiat Oncol Biol Phys 2006;65:726-732

A B C D A - 24% 12% 90% B 15% - 12% 79% C 45% 56% - 137% D 5% 8% 1% -

METHODS FOR TARGET VOLUME DELINEATION IN PET

… is affected by a significant interobserver variability

The manual delineation shows a strong interobserver variability of (26.8±6.3)% (range: 15% to 45%)

Hofheinz F et al Nuclearmedizin 2012;51:9-16

8

2a. Absolute SUV (GTV= SUV>2.5)

Author Reference District Patient

N°

Paulino AC et Int J Radiat Oncol Biol Phys 2004; 59:4-5 -----

Nestle U et al J Nucl Med 2005;46:1342-1348 Lung 25

Nestle U et al. Eur J Nucl Med Mol Imag 2007;34:453-462 Lymphnodes 15

Well applicable to most PET and to some RT planning systems

Due to biological and physical factors there are no “normal” values for SUVs to be similarly used in every case. It has been shown that this method often fails, e.g. when the physiological background activity lies above the fixed threshold (Nestle et al 2007)

METHODS FOR TARGET VOLUME DELINEATION IN PET

9

2b. Fixed percentage of mean SUV Threshold SUV= 50% of a sphere of 12-mm diameter

with highest local intensity

Author Reference District Lesion

N° Frings V et al.

Radiology 2014; 60:539-548

GI

137

36% Limits of agreements

High repeatability

METHODS FOR TARGET VOLUME DELINEATION IN PET

A difference in uptake time between scan 1 and 2 of 15

minutes or longer had a minor negative influence on

repeatability.

Comparable repeatability was found between centers.

Repeatability of metabolically active tumor volume measurements with FDG PET/CT in advanced gastrointestinal malignancies: a multicenter study.

10

2b. Fixed percentage of mean SUV

Variable threshold for SUVmean: Threshold SUV= 0.307 x SUVmean + 0.588

Author Reference District Patient

N° Black QC et al. Bayne M et al

Int J Radiat Oncol Biol Phys 2004; 60:1272-1282 Int J Radiat Oncol Biol Phys 2004; 60:1272-1282

Lung Lung

15 26

SUVmean depends on ROI dimensions At least iterative process

Circular reasoning

Bayne et al reported discrepant GTVs using this algorithm to evaluate patient data

ROI must be defined to calculate SUVmean

METHODS FOR TARGET VOLUME DELINEATION IN PET

11

2c. Fixed %Threshold

A few early investigations estimated that a threshold of ~ 40% of maximum uptake approximated tumor volume

Author Reference District Patient N° Erdi YE et al. Radiother Oncol 2002; 62:51-60 Lung 11 Erdi YE et al. Cancer 1997;80:2505-2509 Lung 10

Many clinical studies were undertaken adopting a fixed threshold of maximum uptake comprised between 40% and 50%:

Author Reference District Patient

N° Method

Bassi MC et al Int J Radiat Oncol Biol Phys 2007 Rectum 25 FT 42%

Koshy M et al. Head neck 2005;27:494-502 HN 36 FT 50%

Scarfone et al. J Nucl Med. 2004 Apr;45:543-52 HN 6 FT 50%

Bradley et al Int J Radiat Oncol Biol Phys. 2004;59:78-86 Lung 26 FT 40%

Ciernik IF et al Int J Radiat Oncol Biol Phys 2003;57:853-863 HN Lung Pelvis 39 FT 50%

METHODS FOR TARGET VOLUME DELINEATION IN PET

12

This method is not likely to be accurate over the full range of clinically relevant volumes and target contrast, mainly because:

1. Thresholds depend on target size 2. Thresholds depend on target contrast 3. Thresholds are “scanner specific” since they likely depend on the

spatial resolution characteristics of the scanner used

METHODS FOR TARGET VOLUME DELINEATION IN PET

2c. Fixed %Threshold

Author Reference District

Patient N°

Nivazi M et al.

Radiat Oncol 2013; 8:180

Various

20

At present there is only low concordance between manually derived GTVs and automatically segmented FDG-PET/CT based BTVs indicating the need for further research in order to achieve higher volumetric conformity and therefore to get access to the full potential of FDG-PET/CT for optimization of radiotherapy planning.

manual GTV contouring compared with 38%, 42%, 47% and 50% SUVmax

The highest amount of BTV within GTV was seen with the 38% SUVmax algorithm (49.0%),

13

A fixed threshold (irrespective of its absolute value) is not an adequate methodology to delineate elevated

uptake signal in PET, because of its binary, deterministic nature and lack of robustness versus

varying contrast and noise conditions.

METHODS FOR TARGET VOLUME DELINEATION IN PET

2c. Fixed %Threshold Hatt M et al.J Nucl Med 2011; 52:658

14

Daisne JF et al. Radiother Oncol 2003;69:247-250

3. ATA: Algorithms depending only on contrast

ECAT HR (Siemens)

Th = a + b/(S/B)

Nestle U et al. J Nucl Med 2005;46:1342-1348

Imean= mean intensity of all pixels surrounding the 70% Imax isocontour within the tumor

ECAT Art (Siemens)

ITh = (0.15xImean) + B

METHODS FOR TARGET VOLUME DELINEATION IN PET

15

3. ATA: Thresholding depending on lesion size and contrast

METHODS FOR TARGET VOLUME DELINEATION IN PET

Erdi YE et al. Cancer 1997;80:2505-2509

Ford EC et al. Med Phys 2006;33:4280-4288

Thabs=B + Threl(Smax-B) Threl= 41% for diameter > 12 mm

Davis JB et al. Radiother Oncol 2006

16

3. ATA depending on lesion size and contrast

Discovery LS (GE)

Jentzen W et al. J Nucl Med 2007;48:108-114

METHODS FOR TARGET VOLUME DELINEATION IN PET

Th(%) = 7.8%/V(mL) + 61.7%*B/S + 31.6%

17

SphereID.)TB

((%)TH ×−−×−= 6911172309

T/B ratio

T/B ratio

Sphere ID (mm)

Sphere ID (mm)

Sphere ID > 10 mm

SphereID.)TB

((%)TH ×−−×−= 98011101151

Sphere ID (β3= - 0.94) T/B ratio (β1= -0.55)

T/B ratio (β1= -0.88) Sphere ID (β3= - 0.32)

Sphere ID < 10 mm

Brambilla M et al. Threshold segmentation for PET target volume delineation in radiation treatment planning: the role of target-to-background ratio and target size. Med Phys 2008;

Biograph 16 Hi-Rez PET/CT (Siemens)

3. ATA depending on lesion size and contrast

18

Influence of Image Acquisition Parameters in ATA methods

– Emission scan duration and background activity concentration are both

related to noise in the reconstructed image. If different levels of noise play a role in threshold determination, the calibration curves will be specific for the acquisition protocol

A multivariable approach was adopted to study the dependence of the percentage threshold (TH (%)) used to define the boundaries of 18F-FDG positive tissue on emission scan duration (ESD) and activity at the start of acquisition (Aacq) for different target sizes (Sphere ID) and target-to-background (T/B) ratios.

AIM OF THE STUDY Brambilla M et al. Med Phys 2008;

Threshold segmentation for PET target volume delineation in radiation treatment planning: the role of target-to-background ratio and target size.

CONCLUSIONS Neither ESD, nor Aacq resulted as statistically significant predictors of Thresholds

19

Influence of Image Reconstruction Parameters in AT methods

The degree of convergence of OSEM algorithms in the clinical range of iterations used does not influence TS determination The inclusion of smoothing in the adaptive thresholding algorithm adds to the generalization of the method allowing its use in centres equipped with the same scanner but using a different set of reconstruction parameters.

CONCLUSIONS

Matheoud R, et al. Influence of reconstruction settings on the performance of adaptive thresholding algorithms for FDG-PET image segmentation in radiotherapy planning. J Appl Clin Med Phys 2011

20

Influence of object characteristics in AT methods

CONCLUSIONS Different conditions of attenuation and scatter do not play a significant role in explaining the variance of Threshold in the wide range explored of Target-to-background ratios, target sizes and reconstruction parameters.

Matheoud R et al. Influence of different contributions of scatter and attenuation on the threshold values in contrast-based algorithms for volume segmentation. Phys Med 2011;

21

Influence of object characteristics in adaptive threshold methods

AIM OF THE STUDY

Target definition of moving lung tumors in positron emission tomography: Correlation of optimal activity concentration thresholds with object size, motion extent, and source-to-background ratio

CONCLUSIONS

The authors successfully developed an expression for optimal activity concentration threshold as a function of object volume, motion, and SBR

Lesion movement – Errors associated with lesion masses moving during data acquisition, (e.g.

imaging of lung lesions), are unpredictable and need to be characterized.

Riegel AC et al. et al. Med Phys 2010;37:1742-1752

To develop a functional relationship between optimal activity concentration threshold, tumor volume, motion extent, and TBR using multiple regression techniques by performing an extensive series of phantom scans simulating tumors of varying sizes, TBR, and motion amplitudes.

22

Imaging parameters not explored in adaptive threshold methods

• Activity distribution – The published ATA methods provide reliable volume estimates only if the

imaged activity distribution is homogeneous.

• Spherical vs irregular shape – Most of the published thresholding methods assumed spherical lesions

and the clinical PET volumes calculations are performed using an ellipsoid model. These suppositions are approximation of the irregularly shaped tumors.

23

AT methods Summary

Emission scan duration Background activity concentration

Attenuation and scatter

Reconstruction parameters: N°iteration, smooth

Lesion dimensions

TBR lesion

Lesion movement

TH(%) = f TBR dimension smooth

The incorporation in the scanner-model specific algorithms of the post-reconstruction Gaussian smoothing avoids the need of system-dependent

optimization procedures. This, together with the demonstrated high level of reliability of this threshold based approach may provide robust and reliable tools to aid physicians as an initial guess in segmenting biological volumes on FDG-PET images.

24

4. Complex algorithms: Gradient based segmentation

Ecat HR Plus (Siemens)

Geets X et al. E J Nucl Med Mol Imaging 2007;

a. Image denoising b. Image deblurring c. Gradient based segmentation

METHODS FOR TARGET VOLUME DELINEATION IN PET

25

4. Complex algorithms: Gradient based segmentation - Validation

METHODS FOR TARGET VOLUME DELINEATION IN PET

Author Reference District Patient N°

Shridar et al. AJR 2014; H&N, Lung,

Colorectal 52

Comparison between Gradient based and Fixed Thresholds methods

FDG PET metabolic tumor volume estimated using gradient segmentation had superior correlation and reliability with the estimated ellipsoid pathologic volume of the tumors compared with fixed threshold method segmentation.

26

4. Complex algorithms: Fuzzy C-means clustering

METHODS FOR TARGET VOLUME DELINEATION IN PET

Dunn JC. A fuzzy relative of the isodata process and its use in detecting compact well-separated clusters. J Cybernet. 974;31:32–57. Hatt M, Cheze le Rest C, Turzo A, Roux C, Visvikis D. A fuzzy Bayesian locally adaptive segmentation approach for volume determination in PET. IEEE Trans Med Imaging. 2009;28 (6):881–93. Hatt M, et al. Accurate automatic delineation of heterogeneous functional volumes in positron emission tomography for oncology applications. Int J Radiat Oncol Biol Phys. 2010;77(1):301–8. Zhu W, Jiang T. Automation segmentation of PET image for brain tumors. IEEE Nucl Sci Symp Conf Rec. 2003;4:2627–9. Belhassen S, Zaidi H. A novel fuzzy C-means algorithm for unsupervised heterogeneous tumor quantification in PET. Med Phys. 2010;37(3):1309–24.

This algorithm iteratively estimates cluster “centroids” (centres of mass) in the image, computing a voxels membership between 0 and 1 to a given cluster depending on the distance between the voxels value and the cluster centroids.

The FCM algorithm lacks explicit noise and spatial correlation modeling. Thus it is sensitive to both noise and intensity heterogeneity.

The FCM algorithm has been independently validated

27

4. Complex algorithms: Fuzzy locally adaptive Bayesian (3-FLAB)

METHODS FOR TARGET VOLUME DELINEATION IN PET

Hatt M, Cheze le Rest C, Turzo A, Roux C, Visvikis D. A fuzzy Bayesian locally adaptive segmentation approach for volume determination in PET. IEEE Trans Med Imaging. 2009;28 (6):881–93. Hatt M, et al. Accurate automatic delineation of heterogeneous functional volumes in positron emission tomography for oncology applications. Int J Radiat Oncol Biol Phys. 2010;77(1):301–8. Hatt M. et al PET functional volume delineation: a robustness and repeatability study. Eur J Nucl Med Mol Imaging (2011) 38:663–672

“It computes, for each voxel, a probability of belonging to a given “class” (for instance, tumor, background or a given uptake level within a tumor). This probability takes into account the voxel intensity, spatial correlation with surrounding voxels (the assumption being that voxels of similar intensities and close to each other have higher probability of belonging to the same class) as well as the overall statistical distributions in the regions of the image by estimating the mean and variance for each class. The FLAB algorithm automatically estimates the parameters of interest (number of classes, class mean and variance, spatial correlation of each voxel) within a stochastic expectation maximization (SEM) framework”

The FLAB algorithm is therefore able to accurately differentiate if necessary both the overall tumor spatial extent from its surrounding background (binary classification) as well as tumor sub volumes with different uptakes (3-FLAB).

28

4. Complex algorithms: Fuzzy locally adaptive Bayesian (FLAB)

METHODS FOR TARGET VOLUME DELINEATION IN PET

Hatt M, Cheze le Rest C, Turzo A, Roux C, Visvikis D. A fuzzy Bayesian locally adaptive segmentation approach for volume determination in PET. IEEE Trans Med Imaging. 2009;28 (6):881–93. Hatt M, et al. Accurate automatic delineation of heterogeneous functional volumes in positron emission tomography for oncology applications. Int J Radiat Oncol Biol Phys. 2010;77(1):301–8. Hatt M. et al PET functional volume delineation: a robustness and repeatability study. Eur J Nucl Med Mol Imaging (2011) 38:663–672

“The new 3-FLAB algorithm is able to extract the overall tumor from the background tissues and delineate variable uptake regions within the tumors, with higher accuracy and robustness compared with adaptive threshold (T(bckg)) and fuzzy C-means (FCM).”

29

Validation Studies - FLAB

METHODS FOR TARGET VOLUME DELINEATION IN PET

Author Reference District Pat.N° Hatt M et al. J Nucl Med 2011; 52:1690-7 NSCLC 25

Threshold-based methods, should not be used for the delineation of cases of large heterogeneous NSCLC, as these methods tend to largely underestimate the spatial extent of the functional tumor

Author Reference District Pat.N° Hatt M et al. Eur J Nucl Med 2012; 52:1858-67 Liver 20

Coefficient of repeatability of SUV (max) and SUV (mean) were 39 and 31 %, respectively, independent of the delineation method used and image reconstruction parameters. The FLAB delineation method improved the repeatability of the volume and TLG measurements compared to PET(SBR)

30

5. Robustness Studies - ATA

METHODS FOR TARGET VOLUME DELINEATION IN PET

Ollers M, Bosmans G, van Baardwijk A, et al 2008 The integration of PET-CT scans from different hospitals into radiotherapy treatment planning. Radiother. Oncol. 87 142–146.

The regression lines did not differ significantly between scanners of the same type equipped with similar electronics The calibration curves for scanners of different type clearly differ. The number of iteration is not a significant predictor of TS, provided that this number is kept above a certain level which is both recommended by the manufactures and necessary to have good image quality

Sphere ID < 3 cm TBR 2- 12 3 scanners

2 Accel 2D 1 ACCEL 3D

Identical Acq

and Recon IT= 2-64

FWHM = 5 mm

31

Robustness Studies ATA – FCM - FLAB METHODS FOR TARGET VOLUME DELINEATION IN PET

Hatt M, Cheze Le Rest C, Albarghach N, Pradier O, Visvikis D 2011 PET functional volume delineation: a robustness and repeatability study Eur. J. Nucl. Med. Mol. Imaging. 38 663-72.

Hatt evaluated the robustness and repeatability of a TBR algorithm in comparison to fuzzy C-means clustering and fuzzy locally adaptive Bayesian algorithm.

The authors performed phantom measurements on four different PET/CT scanners (Philips Gemini and Gemini TF, Siemens Biograph and GE Discovery LS)

32

Robustness Studies - ATA

METHODS FOR TARGET VOLUME DELINEATION IN PET

Schaefer A, Nestle U, Kremp S, et al. 2012 Multi-centre calibration of an adaptive thresholding method for PET-based delineation of tumour volumes in radiotherapy planning of lung cancer. Nuklearmedizin. 51 101-110.

Schaefer (Schaefer et al 2012) evaluated the calibration of an adaptive SUV thresholding algorithm in eleven centers equipped with 5 Siemens Biograph, 5 Philips Gemini and one Siemens ECAT ART scanners. They reported only minor differences in calibration parameters for scanners of the same type provided that identical imaging protocols were used, whereas significant differences were found comparing scanners of different type. Moreover, they reported no statistically significant differences among SUV thresholds calculated for each site by use of the “site-specific” calibration neither among scanners of the same type at different sites nor among scanners of different type at different sites.

33

SC

AN

NE

RS

2 GE Discovery 600

2 GE Discovery ST

1 GE Discovery STE

1 GE Discovery 690

1 Siemens HI-REZ

1 Siemens TRUEV

1 Philips Gemini GXL

2 Philips Gemini TF

Multicentre Italian Study on AT Algorithms (ATA)

Brambilla M, et al. An Adaptive Thresholding Method for BTV Estimation Incorporating PET Reconstruction Parameters: A Multicenter Study of the Robustness and the Reliability Comput Math Methods Med. 2015:571473.

34

MATERIALS & METHODS

Thresholds were determined as a % of the maximum intensity in the cross section area of the spheres. EQUATORIAL CROSS SECTION OF SPHERES

Software EyeLiteRT v.1.1 (G-Squared, Vicenza, Italy)

Imag

e A

naly

sis

To avoid the influence of the operator in ROIs dimensioning and to minimize the influence of the operator in the ROIs positioning.

DAT

A A

NA

LYS

IS

Stepwise multiple linear regression methods to fit the model:

TS = B0 +B1 x sphere A + B2 x (1-1/TB)+ B3 x FWHM + E

35

Scanner Model Specific Calibration Curves

RESULTS

TS = B0 +B1 x sphere A + B2 x (1-1/TB)+ B3 x FWHM + E

36

RESULTS

1.The calibration curves for the proposed adaptive thresholding method were not significantly different between scanners of the same type at different sites.

2.The incorporation in the scanner-model specific algorithms of the post-reconstruction Gaussian smoothing avoids the need of system-dependent optimization procedures.

3.Significant differences were found only between different scanners of different type mainly due to advanced reconstruction algorithms (TOF, PSF)

37

Validation ATA - Spherical Homogeneous lesions

(Phantom) Biograph

HIREZ TB V [cm3]

nominal V [cm3]

ATA Diff.

[cm3] Diff. [%]

DICE Index

S1 16 99 100.2 1.2 1 0.97 S2 16 26.5 26.2 -0.3 -1 0.96 S3 16 11.5 11.1 -0.4 -3 0.96 S4 16 5.6 4.8 -0.8 -14 0.92 S5 16 2.6 2.9 0.3 12 0.80 S1 8 99 99.1 0.1 0 0.97 S2 8 26.5 25.0 -0.5 -6 0.96 S3 8 11.5 10.5 -1.0 -9 0.91 S4 8 5.6 5.1 -0.5 -9 0.93 S5 8 2.6 2.8 0.3 8 0.80 S1 4 99 96.0 -3.0 -3 0.95 S2 4 26.5 27.0 0.5 2 0.91 S3 4 11.5 10.3 -1.2 -10 0.91 S4 4 5.6 6.2 0.6 11 0.83 S5 4 2.6 3.5 1.0 35 0.65

Mean ± SD. -0.3 ± 1.1

1 ± 12 0.90 ± 0.09

38

Validation ATA - Irregular shaped lesions (Phantom)

3 zeolites (Molecular sieves) to simulate 6 lesions

Acquisitions performed with Biograph HiREZ, Siemens scanner using a clinical protocol

N. Volume, ml TB Location 1a, 1b 4.1 5 - 7 Liver 2a, 2b 4.4 10 - 12 Abdomen 3a, 3b 3.9 7 - 12 Thorax

Anatomical Region

Background FDG kBq/ml

Lung 1 Thorax 3 Liver 9

R. Matheoud Secco C, Ridone S, Inglese E, Brambilla M. The use of molecular sieves to simulate hot lesions in (18)F-fluorodeoxyglucose--positron emission tomography imaging. Phys Med Biol 2008;53:N137-48

39

1cm

RESULTS AB

DO

MEN

LIVE

R

THO

RAX

1a

1b

2a

2b

3a

3b

Similar to the SPHERE having A = 227 mm2

40

Liver 1a

Liver 1b

Abdomen 2a

Abdomen 2b

Thorax 3a

Thorax 3b

TB 5 7 10 12 12 13 Vtrue, mL 4.1 4.4 3.9 VATA, mL 3.8 3.8 3.3 3.6 3.6 3.9

Deviation, mL -0.3 -0.3 -1.1 -0.3 -0.3 -1.1 Deviation % -7 -7 -25 -7 -7 -24

RESULTS

~ 12 %

41

Validation ATA - Irregular shaped lesions (Phantom)

6 foam to simulate 6 irregular shaped lesions TB VTC [cm3]

VPET [cm3]

Diff %

FOAM1 6 18.0 17.2 -4

FOAM2 9 34.0 38.8 14

FOAM3 6 14.7 17.1 22

FOAM4 12 30.7 25.8 -16

FOAM5 10 35.3 35.1 0

FOAM6 3 33.7 29.3 -13

foam1 foam3

foam6 foam5

foam4

42

GTV(manual)=23.1cm3

GTV(ATA)=22.1cm3

GTV(manual)=14.7cm3

GTV(ATA)=5.4cm3

43

ATA – and Resolution recovery algorithms

The lack of evidences ensuring that RM algorithms can provide accurate and reproducible data necessary to successfully complete trials involving quantitative PET imaging has led many researchers to not implement PSF assisted reconstructions because of concerns regarding the variability of reconstructed quantitation using these techniques at this time, both in the domain of validation of scanners at sites that wish to participate in oncology clinical trials and in the domain of calibration of adaptive thresholding methods for PET-based delineation of tumor volumes in radiotherapy planning

Resolution Modeling reconstruction algorithms in quantitative PET imaging need to be accurately validated before their introduction in clinical routine.

Matheoud R et al. Performance comparison of two resolution modeling PET reconstruction algorithms in terms of physical figures of merit used in quantitative imaging. Phys Med. 2015;

44

Methods for segmentation of non uniform tracer concentration not based on thresholding have been recently proposed. Statistical method appears to be robust and accurate and

particularly suited for non uniform tumor segmentation. Their use is currently restricted to the developers.

While referring to these promising methods it should be pointed out that, adaptive thresholding segmentation methods are and will be used in most clinics and therefore need to be accurately

characterized. They are robust and accurate at least for segmenting lesions with uniform tracer uptake and can include

both reconstruction parameter and lesion movement.

Semi-automatic methods

CONCLUSIONS

45

CONCLUSIONS –EORTC 2010 Visual Contouring

46

Approach for FDG based target volume definition of the primary tumors based on Adaptive threshold

contouring or advanced gradient or statistical methods

should be considered as a robust and reliable tool to aid physicians as an initial guess in segmenting

biological volumes on FDG-PET images.

CONCLUSIONS – IPET 2015