Foued Derraz

Development of CBCT-based prostate setup correction strategies and impact of rectal distension

BackgroundCone-beam computed tomography (CBCT) image-guided radiotherapy (IGRT) systems are widely used tools to verify and correct the target position before each fraction, allowing to maximize treatment accuracy and precision. In this study, we evaluate automatic three-dimensional intensity-based rigid registration (RR) methods for prostate setup correction using CBCT scans and study the impact of rectal distension on registration quality.MethodsWe retrospectively analyzed 115 CBCT scans of 10 prostate patients. CT-to-CBCT registration was performed using (a) global RR, (b) bony RR, or (c) bony RR refined by a local prostate RR using the CT clinical target volume (CTV) expanded with 1-to-20-mm varying margins. After propagation of the manual CT contours, automatic CBCT contours were generated. For evaluation, a radiation oncologist manually delineated the CTV on the CBCT scans. The propagated and manual CBCT contours were compared using the Dice similarity and a measure based on the bidirectional local distance (BLD). We also conducted a blind visual assessment of the quality of the propagated segmentations. Moreover, we automatically quantified rectal distension between the CT and CBCT scans without using the manual CBCT contours and we investigated its correlation with the registration failures. To improve the registration quality, the air in the rectum was replaced with soft tissue using a filter. The results with and without filtering were compared.ResultsThe statistical analysis of the Dice coefficients and the BLD values resulted in highly significant differences (p<10−6) for the 5-mm and 8-mm local RRs vs the global, bony and 1-mm local RRs. The 8-mm local RR provided the best compromise between accuracy and robustness (Dice median of 0.814 and 97% of success with filtering the air in the rectum). We observed that all failures were due to high rectal distension. Moreover, the visual assessment confirmed the superiority of the 8-mm local RR over the bony RR.ConclusionThe most successful CT-to-CBCT RR method proved to be the 8-mm local RR. We have shown the correlation between its registration failures and rectal distension. Furthermore, we have provided a simple (easily applicable in routine) and automatic method to quantify rectal distension and to predict registration failure using only the manual CT contours.

Unsupervised texture segmentation using active contours driven by the Chernoff gradient flow

We present a new unsupervised segmentation of textural images based on integration of a texture descriptor in the formulation of active contour. The proposed texture descriptor intrinsically describes the geometry of textural regions using the shape operator defined in Beltrami framework. We use the Chernoff distance to define an active contours model which discriminates textures by maximizing the distance between the probability density functions which leads to distinguish textural objects of interest and background described by texture descriptor. We prove the existence of a solution to the new formulated active contours based segmentation model and we propose a fast and easy algorithm based on the dual formulation of the Total Variation norm. Finally, we show results on challenging images to illustrate accurate segmentations that are possible.

A Geometrical Active Contour Based on Statistical Shape Prior Model

A new geometric active contour based level-sets model combining gradient, region and shape knowledge information cues is proposed to robust object detection boundaries in presence of occlusions and cluttered background. The gradient, region and shape knowledge information are incorporated as energy terms. The a priori shape model is based on statistical learning of the training data distribution where the structure of data distribution is approximated by a probability density model. The obtained probability is treated as Kernel Principal Component Analysis (KPC) by allowing the shapes that are close to the training data as energy term and incorporated a prior knowledge about shapes in a more robust manner into evolving equation model to constrain the further segmentation evolution process. We applied successfully the proposed model to synthetic and real MR images. The results drawn by the newer model are compared to expert segmentation and evaluated in terms of F-mesure.

Texture segmentation using globally active contours model and Cauchy-Schwarz distance

We present a new unsupervised segmentation based active contours model and local region texture descriptor. The proposed local region texture descriptor intrinsically describes the geometry of textural regions using the shape operator defined in Beltrami framework. The local texture descriptor is incorporated in the active contours using the Cauchy-Schwarz distance. The texture is discriminated by maximizing distance between the probability density functions which leads to distinguish textural objects of interest and background. We propose a fast Bregman split implementation of our segmentation algorithm based on the dual formulation of the Total Variation norm. Finally, we show results on some challenging images to illustrate segmentations that are possible.

Automatic prostate segmentation in cone-beam computed tomography images using rigid registration

We propose to evaluate automatic three-dimensional gray-value rigid registration (RR) methods for prostate localization on cone-beam computed tomography (CBCT) scans. In total, 103 CBCT scans of 9 prostate patients have been analyzed. Each one was registered to the planning CT scan using different methods: (a) global RR, (b) pelvis bone structure RR, (c) bone RR refined by local soft-tissue RR using the CT clinical target volume (CTV) expanded with a 1, 3, 5, 8, 10, 12, 15 or 20-mm margin. To evaluate results, a radiation oncologist was asked to manually delineate the CTV on the CBCT scans. The Dice coefficients between each automatic CBCT segmentation - derived from the transformation of the manual CT segmentation - and the manual CBCT segmentation were calculated. Global or bone CT/CBCT RR has been shown to yield insufficient results in average. Local RR with an 8-mm margin around the CTV after bone RR was found to be the best candidate for systematically significantly improving prostate localization.

Fast Unsupervised Texture Segmentation Using Active Contours Model Driven by Bhattacharyya Gradient Flow

We present a new unsupervised segmentation based active contours model and texture descriptor. The proposed texture descriptor intrinsically describes the geometry of textural regions using the shape operator defined in Beltrami framework. We use Bhattacharyya distance to discriminate textures by maximizing distance between the probability density functions which leads to distinguish textural objects of interest and background. We propose a fast Bregman split implementation of our segmentation algorithm based on the dual formulation of the Total Variation norm. Finally, we show results on some challenging images to illustrate segmentations that are possible.

MR Images Segmentation Based on Coupled Geometrical Active Contour Model to Anisotropic Diffusion Filtering

In this paper, a new geometrical active contour model for image segmentation is presented. This model is based on the choice of optimal parameters, in particular in the iterative construction of edge-stopping map function obtained by homogenizing the gray-level of the initial image. To obtain this homogenisation, we propose to couple adaptively the partial derivative equation (PDE) of the anisotropic diffusion process with that of geometrical active contour model. The suggested coupling avoids the leakage problem and ensures that the evolving curve of geometrical active contour model reaches more rapidly the true edge boundary of the objects to be segmented. The performances, in term of robustness and precision of proposed model are evaluated on MR images.

Prostate contours delineation using interactive directional active contours model and parametric shape prior model

Prostate contours delineation on Magnetic Resonance (MR) images is a challenging and important task in medical imaging with applications of guiding biopsy, surgery and therapy. While a fully automated method is highly desired for this application, it can be a very difficult task due to the structure and surrounding tissues of the prostate gland. Traditional active contours-based delineation algorithms are typically quite successful for piecewise constant images. Nevertheless, when MR images have diffuse edges or multiple similar objects (e.g. bladder close to prostate) within close proximity, such approaches have proven to be unsuccessful. In order to mitigate these problems, we proposed a new framework for bi-stage contours delineation algorithm based on directional active contours (DAC) incorporating prior knowledge of the prostate shape. We first explicitly addressed the prostate contour delineation problem based on fast globally DAC that incorporates both statistical and parametric shape prior model. In doing so, we were able to exploit the global aspects of contour delineation problem by incorporating a user feedback in contours delineation process where it is shown that only a small amount of user input can sometimes resolve ambiguous scenarios raised by DAC. In addition, once the prostate contours have been delineated, a cost functional is designed to incorporate both user feedback interaction and the parametric shape prior model. Using data from publicly available prostate MR datasets, which includes several challenging clinical datasets, we highlighted the effectiveness and the capability of the proposed algorithm. Besides, the algorithm has been compared with several state-of-the-art methods.

Fast globally supervised segmentation by active contours with shape and texture descriptors

We present a new globally supervised segmentation method in the characteristic function framework based on an active contours (AC) model incorporating both shape prior and texture descriptors. The shape prior descriptor is formulated as the traditional Legendre moment and the texture descriptor as a linear combination of local inside/outside texture descriptor. Using these two descriptors, the AC energy incorporates both learned textures and training shapes. This formulation has two main advantages: 1) by discriminating independently the foreground/background textures. 2) by incorporating both the learned inside/outside texture and the training shape. The trade-off between inside and outside texture descriptor is ensured by balancing descriptor. We illustrate the performance of our segmentation algorithm using some challenging textured images.

Improved edge map of geometrical active contour model based on coupling to anisotropic diffusion filtering

A new geometric active contour model based on iterative refinement of edge map stopping function obtained by iterative grey level homogenization is presented. To homogenize grey level of initial image, we proposed to couple adaptively the partial differential equation (PDE) of the anisotropic diffusion filter to that of geometric active contour model. The proposed model avoids the leakage problems and ensures that the evolving level set curves of geometric active contour model to reach more rapidly the true edges boundaries of the objects to be segmented. The robustness and precision performance of proposed model are evaluated on MR images and compared to the classical geometric active contour model (CGAC).