INTRODUCTION: Despite the advances in radiotherapy treatment delivery, target volume
delineation remains one of the greatest sources of error in the radiotherapy delivery process,
which can lead to poor tumour control probability and impact clinical outcome. Contouring
assessments are performed to ensure high quality of target volume definition in clinical trials
but this can be subjective and labour-intensive.
This project addresses the hypothesis that computational segmentation techniques, with a given
prior, can be used to develop an image-based tumour delineation process for contour
assessments. This thesis focuses on the exploration of the segmentation techniques to develop
an automated method for generating reference delineations in the setting of advanced lung
cancer. The novelty of this project is in the use of the initial clinician outline as a prior for
image segmentation.
METHODS: Automated segmentation processes were developed for stage II and III non-small
cell lung cancer using the IDEAL-CRT clinical trial dataset. Marker-controlled watershed
segmentation, two active contour approaches (edge- and region-based) and graph-cut applied
on superpixels were explored. k-nearest neighbour (k-NN) classification of tumour from
normal tissues based on texture features was also investigated.
RESULTS: 63 cases were used for development and training. Segmentation and classification
performance were evaluated on an independent test set of 16 cases. Edge-based active contour
segmentation achieved highest Dice similarity coefficient of 0.80 ± 0.06, followed by graphcut
at 0.76 ± 0.06, watershed at 0.72 ± 0.08 and region-based active contour at 0.71 ± 0.07,
with mean computational times of 192 ± 102 sec, 834 ± 438 sec, 21 ± 5 sec and 45 ± 18 sec
per case respectively. Errors in accuracy of irregularly shaped lesions and segmentation
leakages at the mediastinum were observed.
In the distinction of tumour and non-tumour regions, misclassification errors of 14.5% and
15.5% were achieved using 16- and 8-pixel regions of interest (ROIs) respectively. Higher
misclassification errors of 24.7% and 26.9% for 16- and 8-pixel ROIs were obtained in the
analysis of the tumour boundary.
CONCLUSIONS: Conventional image-based segmentation techniques with the application of
priors are useful in automatic segmentation of tumours, although further developments are
required to improve their performance. Texture classification can be useful in distinguishing
tumour from non-tumour tissue, but the segmentation task at the tumour boundary is more
difficult. Future work with deep-learning segmentation approaches need to be explored.Funded by National Radiotherapy Trials Quality Assurance (RTTQA) grou