Coronary Artery Segmentation by Deep Learning Neural Networks on Computed Tomographic Coronary Angiographic Images.

Coronary artery lumen delineation, to localize and grade stenosis, is an important but tedious and challenging task for coronary heart disease evaluation. Deep learning has recently been successful applied to many applications, including medical imaging. However for small imaged objects such as coronary arteries and their segmentation, it remains a challenge.

Advanced analyses of computed tomography coronary angiography can help discriminate ischemic lesions.

Background: Computed tomography coronary angiography (CTCA) image analysis enables plaque characterization and non-invasive fractional flow reserve (FFR) calculation. We analyzed various parameters derived from CTCA images and evaluated their associations with ischemia.

Methods: 49 (61 lesions) patients underwent CTCA and invasive FFR.

Fast segmentation and modeling of hepatic components for the planning of robot-assisted liver tumor ablation.

Segmentation and modeling of hepatic components from pre-operative images is very important for treatment planning and guidance in robot-assisted liver tumor ablation. An in-house developed system for hepatic component segmentation and modeling using CT data is presented in this paper. This system includes gross liver segmentation by a 3D mesh deformation model, liver vasculature segmentation by a vessel context-based voting and grouping method, liver tumor segmentation by a support vector machine framework, and other segmentation/modeling tool.

Quantification of coronary artery Stenosis by Area Stenosis from cardiac CT angiography.

Non-invasive cardiac computed tomography angiography (CTA) is widely used to assess coronary artery stenosis and give clinical decision-making support to clinicians. The severity of stenosis lesion is commonly graded by a range of percent Diameter Stenosis (DS), which can introduce false positive diagnoses or over-estimation, triggering unnecessary further procedures. In this paper, a system and the associate methods to quantify stenosis by the percent Area Stenosis (AS) from cardiac CTA is presented.

Three-dimensional spatiotemporal features for fast content-based retrieval of focal liver lesions.

Content-based image retrieval systems for 3-D medical datasets still largely rely on 2-D image-based features extracted from a few representative slices of the image stack. Most 2 -D features that are currently used in the literature not only model a 3-D tumor incompletely but are also highly expensive in terms of computation time, especially for high-resolution datasets. Radiologist-specified semantic labels are sometimes used along with image-based 2-D features to improve the retrieval performance.

Content-based image retrieval of multiphase CT images for focal liver lesion characterization.

Purpose: Characterization of focal liver lesions with various imaging modalities can be very challenging in the clinical practice and is experience-dependent. The authors' aim is to develop an automatic method to facilitate the characterization of focal liver lesions (FLLs) using multiphase computed tomography (CT) images by radiologists.

Methods: A multiphase-image retrieval system is proposed to retrieve a preconstructed database of FLLs with confirmed diagnoses, which can assist radiologists' decision-making in FLL characterization.

Computer-aided focal liver lesion detection.

Purpose: Our aim is to develop an automatic method which can detect diverse focal liver lesions (FLLs) in 3D CT volumes.

Method: A hybrid generative-discriminative framework is proposed. It first uses a generative model to describe non-lesion components and then identifies all candidate FLLs within a 3D liver volume by eliminating non-lesion components.

Modeling n-furcated liver vessels from a 3-D segmented volume using hole-making and subdivision methods.

It is difficult to build an accurate and smooth liver vessel model due to the tiny size, noise, and n-furcations of vessels. To overcome these problems, we propose an n-furcation vessel tree modeling method. In this method, given a segmented volume and a point indicating the root of the vessels, centerlines and cross-sectional contours of the vessels are extracted and organized as a tree first.

Segmentation of liver vasculature from contrast enhanced CT images using context-based voting.

A novel vessel context-based voting is proposed for automatic liver vasculature segmentation in CT images. It is able to conduct full vessel segmentation and recognition of multiple vasculatures effectively. The vessel context describes context information of a voxel related to vessel properties, such as intensity, saliency, direction and connectivity.

Part-based object retrieval in cluttered environment.

A novel local structural approach, which is a sequel to our previous work, is proposed in this paper for object retrieval in a cluttered and occluded environment without identifying the outlines of an object. It works by first extracting consistent and structurally unique local neighborhood from inputs or models and then voting on the optimal matches employing dynamic programming and a novel hypercube-based indexing structure. The proposed concepts have been tested on a database with thousands of images and compared with the six nearest-neighbors shape description with superior results.

[X-ray diffraction quantitative analysis with polynomial-fit method for polycrystalline materials].

The polynomial-fit method for XRD quantitative analysis of polycrystalline materials is presented in this paper, which combines a mathematic function model with computer technology. Based on the construction of diffraction peak mathematic function model, the XRD atlases from experiments were analyzed by means of polynomial whole pattern fitting to the spectral lines using computer software, then the integral intensities of every peak and weight percentages of each phase could be obtained accurately. This paper mainly includes three parts: 1.