Even with recent hardware improvements, superpixel segmentation of large 3D medical images at interactive speed (<500 ms) remains a challenge. We will describe methods to achieve such performances using a GPU based hybrid framework implementing wavefront propagation and cellular automata resolution.
Tasks will be scheduled in blocks (work units) using a wavefront propagation strategy, therefore allowing sparse scheduling. Because work units has been designed as spatially cohesive, the fast Thread Group Shared Memory can be used and reused through a Gauss-Seidel like acceleration. The work unit partitioning scheme will however vary on odd- and even-numbered iterations to reduce convergence barriers. Synchronization will be ensured by an 8-step 3D variant of the traditional Red Black Ordering scheme. An attack model and early termination will also be described and implemented as additional acceleration techniques.
Using our hybrid framework and typical operating parameters, we were able to compute the superpixels of a high-resolution 512x512x512 aortic angioCT scan in 283 ms using a AMD R9 290X GPU. We achieved a 22.3X speed-up factor compared to the published reference GPU implementation.
KEYWORDS: Image segmentation, 3D modeling, Angiography, Image processing algorithms and systems, 3D acquisition, Computed tomography, Process modeling, 3D image processing, Visualization, Medical imaging
We developed a hybrid CPU-GPU framework enabling semi-automated segmentation of abdominal aortic aneurysm
(AAA) on Computed Tomography Angiography (CTA) examinations. AAA maximal diameter (D-max) and volume
measurements and their progression between 2 examinations can be generated by this software improving patient followup.
In order to improve the workflow efficiency some segmentation tasks were implemented and executed on the
graphics processing unit (GPU). A GPU based algorithm is used to automatically segment the lumen of the aneurysm
within short computing time. In a second step, the user interacted with the software to validate the boundaries of the
intra-luminal thrombus (ILT) on GPU-based curved image reformation. Automatic computation of D-max and volume
were performed on the 3D AAA model. Clinical validation was conducted on 34 patients having 2 consecutive MDCT
examinations within a minimum interval of 6 months. The AAA segmentation was performed twice by a experienced
radiologist (reference standard) and once by 3 unsupervised technologists on all 68 MDCT. The ICC for intra-observer
reproducibility was 0.992 (≥0.987) for D-max and 0.998 (≥0.994) for volume measurement. The ICC for inter-observer
reproducibility was 0.985 (0.977-0.90) for D-max and 0.998 (0.996- 0.999) for volume measurement. Semi-automated
AAA segmentation for volume follow-up was more than twice as sensitive than D-max follow-up, while providing an
equivalent reproducibility.
This paper proposes a prior shape segmentation method to create a constant-width ribbon-like zone that runs
along the boundary to be extracted. The image data corresponding to that zone is transformed into a rectangular
image subspace where the boundary is roughly straightened. Every step of the segmentation process
is then applied to that straightened subspace image where the final extracted boundary is transformed back
into the original image space. This approach has the advantage of producing very efficient filtering and edge
detection using conventional techniques. The final boundary is continuous even over image regions where partial
information is missing. The technique was applied to the femoral head segmentation where we show that the
final segmented boundary is very similar to the one obtained manually by a trained orthopedist and has low
sensitivity to the initial positioning of the prior shape.
KEYWORDS: Image segmentation, Radiography, Signal to noise ratio, Linear filtering, Bone, Image processing, Spine, Convolution, Medical imaging, Computer programming
This paper describes a segmentation technique based on a new simplified active contour scheme. The method is a compromise between dynamic programming and the original `snake' solution. It follows a two-step procedure: the first step moves the contour points toward image features using a new potential vector field; the second step smoothes out the contour using a numeric filter to impose continuity and rigidity constraints on the contour model. This technique allows a natural definition of the active contour parameters in term of local curvature and rigidity of the contour. Since it performed particularly well in very noise images, the procedure is demonstrated for segmenting bone structures of the human spine from sagittal radiographs.
A computer segmentation method based on the active contours model (gsnake) and using a priori knowledge is adapted and used to detect automatically the contour lines of the vertebral body in digital radiographs of the scoliotic spine. These contour lines are used to identify correspondent anatomical landmarks for the 3D reconstruction of the scoliotic spine using a bi-planar technique. Automated digitization of the landmarks should drastically reduce the time and the variability of the actual manual digitization method and improve precision in 3D reconstruction. Our procedure is applied to ten different radiographs of scoliotic spine and the results are exposed and discussed. A variability study is also performed and variations in vertebral landmark locations are evaluated. In comparison with the manual landmark identification method, we show that the automated procedure can be used in a supervised environment for the precise extraction of anatomical landmarks.
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