H&N & OC are two of the most commonly diagnosed cancers across the world today, with RT treatment being commonly used in the curative and palliative setting. Improvements in the technology behind RT have led to in- vestigations in the con-formality of the delivered RT dose and multi-modality treatment pathways. Delineation of the GTV, however, has been identified as the largest source of error in accurate RT delivery. Therefore, PET imag- ing has been investigated for providing complementary information to aid in accurate GTV delineation. However, the low spatial resolution of PET imaging combined with complex biological uptake of the radiotracer means delineation on PET imaging is subject to inter and intra-observer variability and is a time consuming process. These combined challenges drive the inter- est in the need for semi-automated and automated delineation of the tumour on PET imaging. The following chapter introduces PET-AS algorithms and compares the methods that have been proposed in the literature.
Figure 1.2: CT scan of patient. a) Air has a density of -1000 Hounsfields, b) Liver, has a density of ≈ 54-60 Hounsfields and c) Bone has Hounsfield values of +700.
Figure 1.3: The GTV corresponds to all of the detectable disease. Whereas, the CTV is an extension of the GTV, which incorporates all of the GTV whilst accounting for microscopic disease extensions. An additional expan- sion of the CTV is required to account for errors which occur during radio- therapy planning and delivery.
Figure 1.4: A planar scinitgraphy obtained from a patient injected with a radiotracer designed for the imaging and detection of bone tumours [39]. Areas of increased metabolic activity / radioactivity are darker and more visible in comparison to less metabolically active tissue.
(a) PET scan (b) CT scan
Figure 1.5: (a) PET showing heterogeneous uptake of the radiotracer in the liver. The more visible areas have increased metabolic uptake and are therefore potentially cancerous and b) CT imaging showing homogeneous tissue density values obtained from the same patient [40, 41]
Figure 1.6: SPECT images obtained from a patient injected with a radio- tracer designed for the monitoring of diffusion of blood in the brain [42]. Areas which are more visible (orange) indicate increased blood flow.
Annihilation Event 511 KeV Gamma ray Detector Ring Detector + -
Figure 1.7: The collision of an positron emitted from 18F-FDG and an elec-
tron resulting in an annihilation event and generation of gamma rays detected by the PET scanner.
X A
B C D
(a) Four lines of response detected by a PET scanner.
Angle
90°
-90° 0°
Displacement from gantry centre A B C D
(b) Sinogram of four lines of response de- tected by a PET scanner.
(a) Original PET scan
(b) Denoised PET scan
Figure 1.9: (a) PET showing heterogeneous uptake of the radiotracer. Two areas of noise in the PET image are shown in red and blue. The noise is represented by darker regions in areas of no to little uptake and are typically a singular voxel. (b) A median filter is applied to the PET image to reduce noise present in the obtained image.
Preamble Prefix … Data Element Data Element Data Element Data Element Data Element
Chapter 2
Segmentation of PET
PET-AS methods potentially offer a more reliable MTV delineation process, which reduces intra-observer and inter-observer variability [37]. Thereby, this allows for the standardisation of MTV delineation across multiple cen- tres [63], which is critical in multi-centre clinical trials. Multiple segmenta- tion algorithms and methodologies have been published and recommended for use in clinical practice [35, 64–72]. There has been no recommendation or consensus, however, on a single segmentation method for use in the clinical environment [1] as the proposed PET-AS methods have been shown to per- form differently when applied to PET images with different conditions [33]. The PET-AS methods investigated throughout this body of work are sum- marised in Table 2.1. The following section defines classifications for the proposed PET-AS methods based upon their implementation, approach and level of automation.
Table 2.1: Name and description of PET-AS methods used in this study, with references of published work using similar segmentation approaches
Algorithm Description Key References
AT 3D Adaptive iterative thresholding, using back- ground subtraction
Jentzen et al [67], Drever et al [66]
RG 3D Region-growing with au- tomatic seed finder and stopping criterion
Day et al [68]
KM 3D K-mean iterative clus- tering with custom stopping criterion
Zaidi and El Naqa [35]
FCM 3D Fuzzy C-mean itera- tive clustering with custom stopping criterion
Belhassen and Zaidi [71]
GCM 3D Gaussian Mixture Mod- els based clustering with custom stopping criterion
Hatt et al [72]
WT Watershed Transform- based algorithm, using sobel filter
Geets et al [69], Tylski et al [70]
2.1
Classification of PET-AS methods
PET-AS methods vary in implementation design, from intuitive threshold based segmentation methods [32], which include in the resulting tumour vol- ume all voxels with an intensity higher than a single threshold value, to advanced machine-learned approaches [65]. Further, PET-AS methods differ in levels of automation, from being fully automated to semi-automated re- quiring user input. Within these, PET-AS method implementations can vary using differing pre and post-processing steps. Therefore, PET-AS methods can be classified in a variety of ways [1]:
• The segmentation algorithm employed and its assumptions and com- plexity.
• Level of pre and post-processing steps. • Automation level.
The classification of PET-AS methods based upon image segmentation ap- proach is a commonly used practice. This classification process relies upon comparing the statistical approach, clustering methodology, simplicity or complexity of the PET-AS algorithms.
A second classification approach compares PET-AS methods based upon the pre and/or post-processing steps used in the specific implementation of the PET-AS algorithm. PET-AS algorithms, however, are typically applied to raw PET data that have not been pre-processed. Optionally, de-noising filters may or may not be used in a PET-AS algorithms implementation. Within this classification approach, further classifiers are the use of phantom acquired data to optimise the PET-AS algorithm, as well as the requirement of image databases to develop statistical models for MTV delineation. Classifiying PET-AS algorithms based upon the level of automation requires the division of the MTV delineation into two different steps [73]. These pro- cesses are the identification of the tumour location and then the delineation of the MTV. Dependency of MTV delineation on operators, through the identification of the tumour location, requires operators to have specific ex- pert knowledge of18F-FDG PET, MTV delineation and the diagnosed cancer
of the tumour location by a user defining a volume of interest (VOI). The PET-AS method is then applied within this defined VOI. This is classed as the standard delineation process and therefore the majority of PET-AS algorithms are regarded as semi-automated, due to the need for human inter- action. Other proposed algorithms rely upon the identification of the tumour location after application of the PET-AS method to the PET image [71] or the manual definition of the area defined as the background uptake in a PET image. Additionally, some PET-AS methods and algorithms require the definition of seed points, within the tumour location, from which the segmentation algorithm is initialised [74].