Search

A novel centerline extraction framework is reported which combines an end-to-end trainable multi-task fully convolutional network (FCN) with a minimal path extractor. The FCN simultaneously computes centerline distance maps and detects branch endpoints. The method generates single-pixel-wide centerlines with no spurious branches. It handles arbitrary tree-structured object with no prior assumption regarding depth of the tree or its bifurcation pattern. It is also robust to substantial scale changes across different parts of the target object and minor imperfections of the object's segmentation mask. To the best of our knowledge, this is the first deep-learning based centerline extraction method that guarantees single-pixel-wide centerline for a complex tree-structured object. The proposed method is validated in coronary artery centerline extraction on a dataset of 620 patients (400 of which used as test set). This application is challenging due to the large number of coronary branches, branch tortuosity, and large variations in length, thickness, shape, etc. The proposed method generates well-positioned centerlines, exhibiting lower number of missing branches and is more robust in the presence of minor imperfections of the object segmentation mask. Compared to a state-of-the-art traditional minimal path approach, our method improves patient-level success rate of centerline extraction from 54.3% to 88.8% according to independent human expert review., Comment: Accepted by the international conference on Information Processing in Medical Imaging (IPMI) 2019

PURPOSE: Establishing and obtaining consistent quantitative indices of retinal thickness from a variety of clinically used Spectral-Domain Optical Coherence Tomography scanners. DESIGN: Retinal images from five Spectral-Domain Optical Coherence Tomography scanners were used to determine total retinal thickness with scanner-specific correction factors establishing consistency of thickness measurement across devices. PARTICIPANTS: 55 Fovea-centered Spectral-Domain Optical Coherence Tomography volumes from eleven subjects were analyzed, obtained from Cirrus HD-OCT, RS-3000, Heidelberg Spectralis, RTVue and Topcon2000, seven subjects with retinal diseases and four normal controls. METHOD: The Iowa Reference Algorithm measured total retinal thickness. Nonlinear model of total retinal thickness measurement comparisons was derived and used for device-specific comparisons. Bland-Altman plots and pairwise predictive equations yielded pairwise scanner-specific differences. Mendel test determined whether measurement biases were constant for each scanner pair. RESULTS: Mendel test revealed that all pairwise scanner differences of total retinal thickness were constant across the cohort (p=0.992). Individual measurements can thus be bias-corrected and the Iowa Reference Algorithm serve as a scanner-agnostic independent standard of total retinal thickness across the five tested SD-OCT scanners. CONCLUSIONS: Combination of the Iowa Reference Algorithm with scanner-specific bias correction yields cross-scanner consistency of total retinal thickness measurements, facilitating scanning-device independent quantitative assessment of total retinal thickness, longitudinal follow-up quantification without requiring patients to be imaged on the same scanner model, and allowing for multi-center studies with heterogeneous device utilization when using the Iowa Reference Algorithm., Comment: 14 pages, 5 tables, 5 figures

Optical coherence tomography (OCT) is a powerful and noninvasive method for retinal imaging. In this paper, we introduce a fast segmentation method based on a new variant of spectral graph theory named diffusion maps. The research is performed on spectral domain (SD) OCT images depicting macular and optic nerve head appearance. The presented approach does not require edge-based image information and relies on regional image texture. Consequently, the proposed method demonstrates robustness in situations of low image contrast or poor layer-to-layer image gradients. Diffusion mapping is applied to 2D and 3D OCT datasets composed of two steps, one for partitioning the data into important and less important sections, and another one for localization of internal layers.In the first step, the pixels/voxels are grouped in rectangular/cubic sets to form a graph node.The weights of a graph are calculated based on geometric distances between pixels/voxels and differences of their mean intensity.The first diffusion map clusters the data into three parts, the second of which is the area of interest. The other two sections are eliminated from the remaining calculations. In the second step, the remaining area is subjected to another diffusion map assessment and the internal layers are localized based on their textural similarities.The proposed method was tested on 23 datasets from two patient groups (glaucoma and normals). The mean unsigned border positioning errors(mean - SD) was 8.52 - 3.13 and 7.56 - 2.95 micrometer for the 2D and 3D methods, respectively., Comment: 30 pages,32 figures

This study sought to determine whether cardiovascular risk factors are associated with aortic intima-media thickness (aIMT) and carotid intima-media thickness (cIMT) in adolescents and young adults.Atherosclerotic lesions begin developing in youth, first in the distal abdominal aorta and later in the carotid arteries. Knowledge of how risk factors relate to aIMT and cIMT may help in the design of early interventions to prevent cardiovascular disease.Participants were 635 members of the Muscatine Offspring cohort. The mean aIMT and cIMT were measured using an automated reading program.The mean (SD) values of aIMT and cIMT were 0.63 (0.14) and 0.49 (0.04) mm, respectively. In adolescents (age 11 to 17 years), aIMT was associated with triglycerides, systolic blood pressure (SBP), diastolic blood pressure (DBP), body mass index (BMI), and waist/hip ratio, after adjusting for age, sex, and height. In young adults (age 18 to 34 years), aIMT was associated with those same 5 risk factors, plus high-density lipoprotein cholesterol and pulse pressure. In adolescents, cIMT was associated with SBP, pulse pressure, heart rate, BMI, and waist/hip ratio. In young adults, cIMT was associated with total cholesterol, low-density lipoprotein cholesterol, triglycerides, SBP, DBP, BMI, waist/hip ratio, and glycosylated hemoglobin. In both age groups, aIMT and cIMT were significantly correlated with the Pathobiological Determinants of Atherosclerosis in Youth coronary artery risk score.Both aIMT and cIMT are associated with cardiovascular risk factors. Using aIMT in adolescents gives information beyond that obtained from cIMT alone. Measurement of aIMT and cIMT may help identify those at risk for premature cardiovascular disease.

Automated or semiautomated segmentation of medical images decreases interstudy variation, observer bias, and postprocessing time as well as providing clincally-relevant quantitative data. In this paper we present a new dynamic deformable modeling approach to 3D segmentation. It utilizes recently developed dynamic remeshing techniques and curvature estimation methods to produce high-quality meshes. The approach has been implemented in an interactive environment that allows a user to specify an initial model and identify key features in the data. These features act as hard constraints that the model must not pass through as it deforms. We have employed the method to perform semi-automatic segmentation of heart structures from cine MRI data.

The aim of this work is to explore the performance of Active Appearance Models (AAMs) in reconstruction and interpretation of bones in hand radiographs. AAM is a generative approach that unifies image segmentation and image understanding. Initial locations for the AAM search are generated by an exhaustive filtering method. A series of AAMs for smaller groups of bones are used. It is found that AAM successful reconstructs 99% of metacarpals, proximal and medial phalanges and the distal 3 cm of radius and ulna. The rms accuracy is better than 240 microns (point-to-curve). The generative property is used (1) to define a measure of fit that allows the models to self-evaluate and chose between the multiple found solutions, (2) to overcome obstacles in the image in the form of rings by predicting the missing part, and (3) to detect anomalies, e.g. rheumatoid arthritis. The shape scores are used as a biometrics to check the identity of patients in a longitudinal study. The conclusion is that AAM provides a highly efficient and unified framework for various tasks in diagnosis and assessment of bone related disorders.