Purpose: To demonstrate a novel automatic slice-positioning technique based on three new anatomical landmarks and to standardize prospective scans by lowering rotational and translational variances.
Materials and Methods:After defining the interpeduncular fossa corner and the eyeball centers as landmarks, they are manually labeled on 25 different T1 MRI scans. New scans are produced according to the Eyeball centersMesencephalon (EM) plane. The comparison of angular deviations at EM and original scans is based on the comparison of rotational angles according to manually labeled Talairach points on both scans. The same variability comparison is also done with automatically captured landmarks to see the effects of segmentation errors.Results: Analysis of variances proved significant lowering of intersubject variability for pitch and yaw angles (P pitch < 0.005, P yaw < 0.001), which are the two basic causes of misalignments. Automatic segmentation accuracy is proved by paired t-test and significance tests.
Conclusion:A new field of view and slice orientation proposed by the EM technique will have fixed the follow-up scans by significantly lowering the rotational and translational variances. The EM technique will precisely match the intrasubject scans and produce better standardized intersubject scans. The distinguishing features of landmarks are sufficient for robust automatic capture.
Stereopsis of X-ray images can produce 3D tree of coronary arteries up to a certain accuracy level with a lower dose of radiation when compared to computer tomography (CT). In this study, a novel and complete automatic system is designed that covers preprocessing, segmentation, matching and reconstruction steps for that purpose. First, an automatic and novel pattern recognition technique is applied for extraction of the bifurcation points with their diameters recorded in a map. Then, a novel optimization algorithm is run for matching the branches efficiently which is based on that map and the epipolar geometry of stereopsis. Finally, cut branches are fixed one by one at the bifurcations for completing the 3D reconstruction. This method prevails the similar ones in the literature with this novelty since it automatically and inherently prevents the wrong overlapping of branches. Other essential problems like correct detection of the bifurcations and accurate calibration parameters and fast overlapping of matched branches are addressed at acceptable levels. The accuracy of bifurcation extraction is high at 90 % with 96 % sensitivity. Accuracy of vessel centerlines has rootmean-square (rms) error smaller than 0.57 mm for 20 different patients. For phantom model, rms error is 0.75 ± 0.8 mm in 3D localization.
Gray matter (GM) and white matter (WM) are adjacent tissues in the brain separated by an interface.Extraction of this boundary is very important for quantitative analysis and monitoring of atrophy. A perfect capture for this purpose is a missing vital item for which research continues. In order to get close to such precision, two novel systems are presented for segmentation of cortical GM and WM in brain MRIs. The system imitates human perceptual sensitiveness to contrast, which is the basic principle of edge detection algorithms, and completes its shortcoming feature of segmentation. As well as being completely automatic, the system is also unsupervised. The correct GM-WM boundary rate gets close to 77% and the segmentation accuracy is over 95%, which are promising results. In comparison tests with SPM, the proposed technique showed 6% higher accuracy with both noisy and normal data and better recovery of small cavities in sulci, being confirmed by experts' drawings.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.