In image‐guided radiotherapy (IGRT) of prostate cancer, delineation of the clinical target volume (CTV) often relies on magnetic resonance (MR) because of its good soft‐tissue visualization. Registration of MR and computed tomography (CT) is required in order to add this accurate delineation to the dose planning CT. An automatic approach for local MR‐CT registration of the prostate has previously been developed using a voxel property‐based registration as an alternative to a manual landmark‐based registration. The aim of this study is to compare the two registration approaches and to investigate the clinical potential for replacing the manual registration with the automatic registration. Registrations and analysis were performed for 30 prostate cancer patients treated with IGRT using a Ni‐Ti prostate stent as a fiducial marker. The comparison included computing translational and rotational differences between the approaches, visual inspection, and computing the overlap of the CTV. The computed mean translational difference was 1.65, 1.60, and 1.80 mm and the computed mean rotational difference was 1.51°, 3.93°, and 2.09° in the superior/inferior, anterior/posterior, and medial/lateral direction, respectively. The sensitivity of overlap was 87%. The results demonstrate that the automatic registration approach performs registrations comparable to the manual registration.PACS number(s): 87.57.nj, 87.61.‐c, 87.57.Q‐, 87.56.J‐

This approaches the interobserver DSC of 0.90 and interobserver MSD 0f 1.15 mm and is comparable to other studies performing prostate segmentation in MR.

Evaluation of the fetoplacental macrovasculature is feasible with computed tomography angiography. In normal birthweight placentas, macrovascular volume and surface area increase as pregnancy advances by vessel branching rather than increased vessel diameter and elongation. The FGR placenta was smaller; however, the macrovascular volume was within normal range because of an increased macrovascular density.

This study demonstrated the feasibility for an automatic local MR-CT registration using the prostate stent.

Background. The prostate gland is delineated as the clinical target volume (CTV) in treatment planning of prostate cancer. Therefore, an accurate delineation is a prerequisite for efficient treatment. Accurate automated prostate segmentation methods facilitate the delineation of the CTV without inter-observer variation. The purpose of this study is to present an automated three-dimensional (3D) segmentation of the prostate using an active appearance model. Material and methods. Axial T2-weighted magnetic resonance (Mr) scans were used to build the active appearance model. The model was based on a principal component analysis of shape and texture features with a level-set representation of the prostate shape instead of the selection of landmarks in the traditional active appearance model. To achieve a better fit of the model to the target image, prior knowledge to predict how to correct the model and pose parameters was incorporated. The segmentation was performed as an iterative algorithm to minimize the squared difference between the target and the model image. Results. The model was trained using manual delineations from 30 patients and was validated using leave-one-out cross validation where the automated segmentations were compared with the manual reference delineations. The mean and median dice similarity coefficient was 0.84 and 0.86, respectively. Conclusion. This study demonstrated the feasibility for an automated prostate segmentation using an active appearance with results comparable to other studies.