Robust and fully automated segmentation of mandible from CT scans

Mandible bone segmentation from computed tomography (CT) scans is challenging due to mandible's structural irregularities, complex shape patterns, and lack of contrast in joints. Furthermore, connections of teeth to mandible and mandible to remaining parts of the skull make it extremely difficult to identify mandible boundary automatically. This study addresses these challenges by proposing a novel framework where we define the segmentation as two complementary tasks: recognition and delineation. For recognition, we use random forest regression to localize mandible in 3D. For delineation, we propose to use 3D gradient-based fuzzy connectedness (FC) image segmentation algorithm, operating on the recognized mandible sub-volume. Despite heavy CT artifacts and dental fillings, consisting half of the CT image data in our experiments, we have achieved highly accurate detection and delineation results. Specifically, detection accuracy more than 96% (measured by union of intersection (UoI)), the delineation accuracy of 91% (measured by dice similarity coefficient), and less than 1 mm in shape mismatch (Hausdorff Distance) were found.Comment: 4 pages, 5 figures, IEEE International Symposium on Biomedical Imaging (ISBI) 201

Relational Reasoning Network (RRN) for Anatomical Landmarking

Accurately identifying anatomical landmarks is a crucial step in deformation analysis and surgical planning for craniomaxillofacial (CMF) bones. Available methods require segmentation of the object of interest for precise landmarking. Unlike those, our purpose in this study is to perform anatomical landmarking using the inherent relation of CMF bones without explicitly segmenting them. We propose a new deep network architecture, called relational reasoning network (RRN), to accurately learn the local and the global relations of the landmarks. Specifically, we are interested in learning landmarks in CMF region: mandible, maxilla, and nasal bones. The proposed RRN works in an end-to-end manner, utilizing learned relations of the landmarks based on dense-block units and without the need for segmentation. For a given a few landmarks as input, the proposed system accurately and efficiently localizes the remaining landmarks on the aforementioned bones. For a comprehensive evaluation of RRN, we used cone-beam computed tomography (CBCT) scans of 250 patients. The proposed system identifies the landmark locations very accurately even when there are severe pathologies or deformations in the bones. The proposed RRN has also revealed unique relationships among the landmarks that help us infer several reasoning about informativeness of the landmark points. RRN is invariant to order of landmarks and it allowed us to discover the optimal configurations (number and location) for landmarks to be localized within the object of interest (mandible) or nearby objects (maxilla and nasal). To the best of our knowledge, this is the first of its kind algorithm finding anatomical relations of the objects using deep learning.Comment: 10 pages, 6 Figures, 3 Table

Moving object detection using adaptive subband decomposition and fractional lower-order statistics in video sequences

In this paper, a moving object detection method in video sequences is described. In the first step, the camera motion is eliminated using motion compensation. An adaptive subband decomposition structure is then used to analyze the motion compensated image. In the "low-high" and "high-low" subimages moving objects appear as outliers and they are detected using a statistical detection test based on fractional lower-order statistics. It turns out that the distribution of the subimage pixels is almost Gaussian in general. On the other hand, at the object boundaries the distribution of the pixels in the subimages deviates from Gaussianity due to the existence of outliers. By detecting the regions containing outliers the boundaries of the moving objects are estimated. Simulation examples are presented. © 2002 Elsevier Science B.V. All rights reserved

Anesthesia Management in Robinow Syndrome (A Case Report)

Robinow Syndrome (RS) is a rare disease characterized by anomalies in the face, head, external reproductive organs, and spine segmentation. The three main symptoms of the syndrome are fetal face appearance, genital hypoplasia, and gingival hyperplasia. Fifteen percent of the cases have congenital heart defects. Short neck, large tongue, and airway problems due to a structural disorder of the face may be observed. In this paper, we present our anesthesia practice in a case that had been diagnosed with RS

Efficient structures and design of filter banks with applications to image analysis.

Efficient structures and design of filter banks with applications to image analysis

Automated Prescription of an Optimal Imaging Plane for Measurement of Cerebral Blood Flow by Phase Contrast Magnetic Resonance Imaging

This study describes and evaluates a semiautomated method for prescribing an optimal imaging plane that is located as close as possible to the skull base, and is simultaneously nearly perpendicular to the four arteries leading blood to the brain [internal carotid arteries (ICAs) and vertebral arteries (VAs)]. Such a method will streamline and improve reliability of the measurement of total cerebral blood flow and intracranial pressure by velocity encoding phase-contrast magnetic resonance imaging. The method first extracts the vessels' centerline from a 2-D time-of-flight magnetic resonance angiogram of the neck by performing distance transformations. An anatomical marker, the V2 segment of the VAs, is then identified to guide the imaging plane to be as close and below the skull base. An imaging plane that is nearly perpendicular to the ICAs and V2 segment of VAs is then identified by minimizing a misalignment value, estimated by a weighted mean of the angles between the plane's normal and the vessel axes at the vessel-plane intersections. The performance of the semiautomated method was evaluated by comparing manually selected planes to those found semiautomatically in nine magnetic resonance angiogram datasets. The semiautomated method consistently outperformed manual prescription with a significantly smaller misalignment value, 8.6° versus 20.7° (P <; 0.001), respectively, and significantly improved reproducibility

Computer-aided method for automated selection of optimal imaging plane for measurement of total cerebral blood flow by MRI

A computer-aided method for finding an optimal imaging plane for simultaneous measurement of the arterial blood inflow through the 4 vessels leading blood to the brain by phase contrast magnetic resonance imaging is presented. The method performance is compared with manual selection by two observers. The skeletons of the 4 vessels for which centerlines are generated are first extracted. Then, a global direction of the relatively less curved internal carotid arteries is calculated to determine the main flow direction. This is then used as a reference direction to identify segments of the vertebral arteries that strongly deviates from the main flow direction. These segments are then used to identify anatomical landmarks for improved consistency of the imaging plane selection. An optimal imaging plane is then identified by finding a plane with the smallest error value, which is defined as the sum of the angles between the plane's normal and the vessel centerline's direction at the location of the intersections. Error values obtained using the automated and the manual methods were then compared using 9 magnetic resonance angiography (MRA) data sets. The automated method considerably outperformed the manual selection. The mean error value with the automated method was significantly lower than the manual method, 0.09±0.07 vs. 0.53±0.45, respectively (p<.0001, Student's t-test). Reproducibility of repeated measurements was analyzed using Bland and Altman's test, the mean 95% limits of agreements for the automated and manual method were 0.01~0.02 and 0.43~0.55 respectively