3D reconstruction of coronary arteries from angiographic sequences for interventional assistance

Introduction -- Review of literature -- Research hypothesis and objectives -- Methodology -- Results and discussion -- Conclusion and future perspectives

Rib detection in 3D MRI using dynamic programming based on vesselness and ridgeness

In this paper, a fully automatic method is proposed to detect the ribs in 3D MRI. The purpose of the detection is MR-guided HIFU treatment of liver lesions, in which the ribs should be avoided. Rib segmentations are required for treatment planning and they may also be used for motion tracking during treatment. The rib detection results can serve as an initialization to automatic rib cage segmentation. The algorithm is based on surface detection and dynamic programming. First, the outer surface of the rib cage is detected. Vesselness and ridgeness are computed to highlight elongated structures. The ribs are tracked simultaneously on a 2D projection of the vesselness in the surface, using dynamic programming. Finally, the extracted lines are backprojected into the original 3D volume. Preliminary results of this algorithm are presented on data of five subjects. The results were evaluated by visual inspection of the backprojected lines in 3D. It was checked whether a line belonged to the correct rib and whether it stayed inside this rib. Overall, our algorithm was capable of detecting the ribs that were visible in the images. Testing on five volunteers yielded one failure. The remaining four results were satisfactory. Our method seems suitable to serve as initialization to a full rib cage segmentation in MRI. © 2013 Springer-Verlag

Rib detection in 3D MRI using dynamic programming based on vesselness and ridgeness

In this paper, a fully automatic method is proposed to detect the ribs in 3D MRI. The purpose of the detection is MR-guided HIFU treatment of liver lesions, in which the ribs should be avoided. Rib segmentations are required for treatment planning and they may also be used for motion tracking during treatment. The rib detection results can serve as an initialization to automatic rib cage segmentation. The algorithm is based on surface detection and dynamic programming. First, the outer surface of the rib cage is detected. Vesselness and ridgeness are computed to highlight elongated structures. The ribs are tracked simultaneously on a 2D projection of the vesselness in the surface, using dynamic programming. Finally, the extracted lines are backprojected into the original 3D volume. Preliminary results of this algorithm are presented on data of five subjects. The results were evaluated by visual inspection of the backprojected lines in 3D. It was checked whether a line belonged to the correct rib and whether it stayed inside this rib. Overall, our algorithm was capable of detecting the ribs that were visible in the images. Testing on five volunteers yielded one failure. The remaining four results were satisfactory. Our method seems suitable to serve as initialization to a full rib cage segmentation in MRI. © 2013 Springer-Verlag