Shmuel Aharon

Random walks for interactive organ segmentation in two and three dimensions: implementation and validation

A new approach to interactive segmentation based on random walks was recently introduced that shows promise for allowing physicians more flexibility to segment arbitrary objects in an image. This report has two goals: To introduce a novel computational method for applying the random walker algorithm in 2D/3D using the Graphics Processing Unit (GPU) and to provide quantitative validation studies of this algorithm relative to different targets, imaging modalities and interaction strategies.

GPU histogram computation

Due to the immense computational power of today’s graphics processors (GPU), general purpose computation on GPUs has become a vivid research area. The performance of algorithms running on GPUs highly depends on how well they can be arranged to fit and exploit the processors single instruction multiple data (SIMD) architecture. Many tasks that are considered simple on a CPU such as grouping and counting of values of a domain for statistical purposes appear rather challenging to be implemented on a GPU. In this poster, we present a method to compute histograms in shader programs. On the example of image segmentation, we show that our method enables iterative and histogram guided algorithms to run efficiently on graphics hardware without costly CPU intervention. Using an image segmentation example, we demonstrate how the algorithm can be optimized for smaller regions of interest inside larger domains.

Multi-modality model-based registration in the cardiac domain

Recent advances in CT technology have made possible the acquisition of high resolution images of the heart in which important anatomical features such as the coronary vessels are visible. The state of the art in MR Imaging techniques, on the other hand, typically provides lower resolution but can facilitate the differentiation of viable (healthy) cardiac tissue from infarcted (injured) tissue. The registration of CT and MR images of the same heart permit the association of anatomy with function. As an example, given an infarcted region, the coronary vessels responsible for irrigating that region may be isolated and targeted for treatment (e.g., coronary bypass). We demonstrate a software package, PROTEUS, which is capable of registering CT and MR images. Since the manifestation of cardiac tissue in the two modalities differs so greatly we rely on features to perform the matching. Specifically, we employ a model-based approach that relies on segmented contours delineating the inner and outer borders of the left ventricle (LV). Given these contours, our technique is completely automatic.

Efficient Framework for Deformable 2D-3D Registration

Using 2D-3D registration it is possible to extract the body transformation between the coordinate systems of X-ray and volumetric CT images. Our initial motivation is the improvement of accuracy of external beam radiation therapy, an effective method for treating cancer, where CT data play a central role in radiation treatment planning. Rigid body transformation is used to compute the correct patient setup. The drawback of such approaches is that the rigidity assumption on the imaged object is not valid for most of the patient cases, mainly due to respiratory motion. In the present work, we address this limitation by proposing a flexible framework for deformable 2D-3D registration consisting of a learning phase incorporating 4D CT data sets and hardware accelerated free form DRR generation, 2D motion computation, and 2D-3D back projection.