In brachytherapy, radiation therapy is performed by placing the radiation source into or very close to the tumour. When calculating the absorbed dose, water is often used as the radiation transport and dose scoring medium for soft tissues and this leads to inaccuracies. The iterative reconstruction algorithm DIRA is under development at the Center for Medical Imaging Science and Visualization, Linköping University. DIRA uses dual-energy CT to decompose tissues into different doublets and triplets of base components for a better absorbed dose estimation. To accurately determine mass fractions of these base components for different tissues, the tissues needs to be identified in the image. The aims of this master thesis are: (i) Find an automated segmentation algorithm in CT that best segments the male pelvis. (ii) Implement a segmentation algorithm that can be used in DIRA. (iii) Implement a fully automatic segmentation algorithm. Seven segmentation methods were tested in Matlab using images obtained from Linköping University Hospital. The methods were: active contours, atlas based registration, graph cuts, level set, region growing, thresholding and watershed. Four segmentation algorithms were selected for further analysis: phase based atlas registration, region growing, thresholding and active contours without edges. The four algorithms were combined and supplemented with other image analysis methods to form a fully automated segmentation algorithm that was implemented in DIRA. The newly developed algorithm (named MK2014) was sufficiently stable for pelvic image segmentation with a mean computational time of 45.3 s and a mean Dice similarity coefficient of 0.925 per 512×512 image. The performance of MK2014 tested on a simplified anthropomorphic phantom in DIRA gave promising result. Additional tests with more realistic phantoms are needed to confirm the general applicability of MK2014 in DIRA.

Automatic segmentation of human organs allows more accurate calculation of organ doses in radiationtreatment planning, as it adds prior information about the material composition of imaged tissues. For instance, the separation of tissues into bone, adipose tissue and remaining soft tissues allows to use tabulated material compositions of those tissues. This approximation is not perfect because of variability of tissue composition among patients, but is still better than no approximation at all. Another use for automated tissue segmentationis in model based iterative reconstruction algorithms. An example of such an algorithm is DIRA, which is developed at the Medical Radiation Physics and the Center for Medical Imaging Science and Visualization(CMIV) at Linköpings University. DIRA uses dual-energy computed tomography (DECT) data to decompose patient tissues into two or three base components. So far DIRA has used the MK2014 algorithm which segments human pelvis into bones, adipose tissue, gluteus maximus muscles and the prostate. One problem was that MK2014 was limited to 2D and it was not very robust. Aim: The aim of this thesis work was to extend the MK2014 to 3D as well as to improve it. The task was structured to the following activities: selection of suitable segmentation algorithms, evaluation of their results and combining of those to an automated segmentation algorithm. Of special interest was image registration usingthe Morphon. Methods: Several different algorithms were tested.  For instance: Otsu's method followed by threshold segmentation; histogram matching followed by threshold segmentation, region growing and hole-filling; affine phase-based registration and the Morphon. The best-performing algorithms were combined into the newly developed JJ2016. Results: For the segmentation of adipose tissue and the bones in the eight investigated data sets, the JJ2016 algorithm gave better results than the MK2014. The better results of the JJ2016 were achieved by: (i) a new segmentation algorithm for adipose tissue which was not affected by the amount of air surrounding the patient and segmented smaller regions of adipose tissue and (ii) a new filling algorithm for connecting segments of compact bone. The JJ2016 algorithm also estimates a likely position for the prostate and the rectum by combining linear and non-linear phase-based registration for atlas based segmentation. The estimated position (center point) was in most cases close to the true position of the organs. Several deficiencies of the MK2014 algorithm were removed but the improved version (MK2014v2) did not perform as well as the JJ2016. Conclusions: JJ2016 performed well for all data sets. The JJ2016 algorithm is usable for the intended application, but is (without further improvements) too slow for interactive usage. Additionally, a validation of the algorithm for clinical use should be performed on a larger number of data sets, covering the variability of patients in shape and size.

Dans cette thèse, nous présentons une méthode de segmentation d’images radiographiques des membres inférieurs en régions d’intérêt (ROIs), une méthode de recalage rigide tridimensionnel (3D) / bidimensionnel (2D) des prothèses du genou sur les deux images biplanaires radiographiques calibrées et une méthode de reconstruction 3D des membres inférieurs à partir de deux images biplanaires radiographiques calibrées. Le premier article présente une méthode de segmentation de rotule, astragale et bassin des images radiographiques en régions d’intérêt basée sur la fusion de multi-atlas et superpixels. Cette méthode utilise l’apprentissage d’une base de données d’images radiographiques de ces os segmentées manuellement et recalées entre elles pour estimer un ensemble de superpixels permettant de tenir compte de toute la variabilité locale et non linéaire existante dans la base, puis la propagation d’étiquettes basée sur le concept d’entropie pour raffiner la carte de segmentations en régions internes afin d’obtenir le résultat final. Le deuxième article présente une méthode de recalage rigide 3D / 2D des composants tibiaux et fémoraux de prothèse du genou sur deux images biplanaires radiographiques calibrées. Cette méthode utilise une mesure de similarité hybride basée sur les notions de contours et régions puis un algorithme d’optimisation stochastique pour estimer la position des composants. La similarité basée sur les régions est stable et robuste contre les bruits. Cependant, cette mesure n’est pas précise car le nombre de pixels aux contours est inférieur au celui à l’intérieur de la région. Au contraire, la similarité basée sur les contours est précise mais plus sensible au bruit ou à d’autres artefacts existant dans les images. C’est pourquoi la combinaison de ces deux similarités fournit une méthode de recalage robuste et précise. Le troisième article représente une méthode statistique biplanaire de reconstruction 3D de rotule, astragale et bassin. Cette méthode utilise un algorithme de réduction de dimensionnalité pour définir un modèle déformable paramétrique qui contient toutes les déformations statistiques admissibles apprises à partir d’une base de données des structures osseuses. Puis un algorithme d’optimisation stochastique est utilisé pour minimiser la différence entre la projection des contours / régions des modèles surfaciques osseux avec ceux segmentés sur les deux images radiographiques. / In this thesis, we present a segmentation method of lower limbs of X-ray images into regions of interest (ROIs), a three-dimensional (3D) / two-dimensional (2D) rigid registration method of knee implant components to biplanar X-ray images, and a 3D reconstruction method of the lower limbs using biplanar X-ray images. The first paper presents a superpixel and multi-atlas-based segmentation method of the patella, talus, and pelvis into regions of interest. This method uses a training dataset of pre-segmented and co-registered X-ray images of these bones to estimate a collection of superpixels allowing to take into account all the nonlinear and local variability existing in the dataset, then a propagation of label based on the entropy concept for refining the segmentation map into internal regions to the final result. The second paper presents a 3D / 2D rigid registration method of tibial and femoral components of knee implants to calibrated biplanar X-ray images. This method uses a hybrid edge- and region-based similarity measure then a stochastic optimization algorithm to estimate the component position. The region-based similarity is stable and robust to noise. However, this measure is not precise because the number of pixels in the border is fewer than the number of pixels inside the region. On the contrary, the edge-based similarity is accurate but more sensitive to noise or other artifacts existing in the images. That’s why the combination of these two similarity types provides a robust and accurate registration method. The third paper presents a statistical biplanar 3D reconstruction method of the patella, talus, and pelvis. This method uses a dimensionality reduction algorithm to define a deformable parametric model which contains all admissible statistical deformations learned from the bone structure dataset. Then a stochastic optimization algorithm is used to minimize the difference between the contour / region projection of bone models and the contours / regions in two segmented X-ray images.