Connectivity-based local adaptive thresholding for carotid artery segmentation using MRA images

The computer algorithms for the delineation of anatomical structures and other regions of interest on the medical imagery are important component in assisting and automating specific radiological tasks. In addition, the segmentation of region is an important first step for variety image related application and visualization tasks. In this paper, we propose a fast and automated connectivity-based local adaptive thresholding (CLAT) algorithm to segment the carotid artery in sequence medical imagery. This algorithm provides the new feature that is the circumscribed quadrangle on the segmented carotid artery for region-of-interest (ROI) determination. By using the preserved connectivity between consecutive slice images, the size of the ROI is adjusted like a moving window according to the segmentation result of previous slice image. The histogram is prepared for each ROI and then smoothed by local averaging for the threshold selection. The threshold value for carotid artery segmentation is locally selected on each slice image and is adaptively determined through the sequence image. In terms of automated features and computing time, this algorithm is more effective than region growing and deformable model approaches. This algorithm is also applicable to segment the cylinder shape structures and tree-like blood vessels such as renal artery and coronary artery in the medical imagery. Experiments have been conducted on synthesized images, phantom and clinical data sets with various Gaussian noise.     

3D anatomical shape atlas construction using mesh quality preserved deformable models

3D anatomical shape atlas construction has been extensively studied in medical image analysis research, owing to its importance in model-based image segmentation, longitudinal studies and populational statistical analysis, etc. Among multiple steps of 3D shape atlas construction, establishing anatomical correspondences across subjects, i.e., surface registration, is probably the most critical but challenging one. Adaptive focus deformable model (AFDM) [1] was proposed to tackle this problem by exploiting cross-scale geometry characteristics of 3D anatomy surfaces. Although the effectiveness of AFDM has been proved in various studies, its performance is highly dependent on the quality of 3D surface meshes, which often degrades along with the iterations of deformable surface registration (the process of correspondence matching). In this paper, we propose a new framework for 3D anatomical shape atlas construction. Our method aims to robustly establish correspondences across different subjects and simultaneously generate high-quality surface meshes without removing shape details. Mathematically, a new energy term is embedded into the original energy function of AFDM to preserve surface mesh qualities during deformable surface matching. More specifically, we employ the Laplacian representation to encode shape details and smoothness constraints. An expectation–maximization style algorithm is designed to optimize multiple energy terms alternatively until convergence. We demonstrate the performance of our method via a set of diverse applications, including a population of sparse cardiac MRI slices with 2D labels, 3D high resolution CT cardiac images and rodent brain MRIs with multiple structures. The constructed shape atlases exhibit good mesh qualities and preserve fine shape details. The constructed shape atlases can further benefit other research topics such as segmentation and statistical analysis.     

基于HC-CFCN模型的肝脏CT图像分割

在计算机断层扫描(CT)图像中肝脏与相邻器官灰度值近似,且不同患者的肝脏轮廓存在差异性,导致肝脏CT图像的精确分割成为医学图像处理中的难题之一。为实现肝脏CT图像的自动分割,构建一种层间上下文级联式的全卷积神经网络模型HC-CFCN。利用第1级网络实现肝脏轮廓的粗略分割,并将其分割结果与原始CT图像、肝脏能量图共同作为第2级网络的输入,优化分割结果。在LiTS数据集上的实验结果表明,与U-Net、FCN+3DCRF和V-Net模型相比,HC-CFCN模型的分割精度较高。     

肺部影像解剖结构分割数据集及应用

目的 从影像中快速精准地分割出肺部解剖结构可以清晰直观地分辨各解剖结构间的关系,提供有效、客观的辅助诊断信息,大大提高医生的阅片效率并降低医生的工作量。随着影像分割算法的发展,越来越多的方法应用于分割肺部影像中感兴趣的解剖结构区域,但目前尚缺乏包含多种肺部精细解剖结构的影像数据集。本文创建了一个带标签的肺部CT/CTA （computer tomography/computer tomography angiography）影像数据集,以促进肺部解剖结构分割算法的发展。方法 该数据集共标记了67组肺部CT/CTA影像,包括CT影像24组、CTA影像43组,共计切片图像26 157幅。每组CT/CTA有4个不同的目标区域类别,标记对应支气管、肺实质、肺叶、肺动脉和肺静脉。结果 本文利用该数据集,用于肺部CT解剖结构分割医学影像挑战赛——2020年第四届国际图像计算与数字医学研讨会,该挑战赛提供了一个肺血管、支气管和肺实质的评估平台,通过Dice系数、过分割率、欠分割率、医学和算法行业专家对分割和3维重建效果进行了评估,目的是比较各种算法分割肺部解剖结构的性能。结论 本文详细描述了包括支气管、肺实质、肺叶、肺动脉和肺静脉等解剖结构标签的肺部影像数据集和应用结果,为相关研究人员利用本数据集进行更深入的研究提供参考。     

颌骨重建中的图像分割和轮廓对应及分支问题

根据人体颌骨的形状特征,提出一系列有针对性的方法,在CT断层数据的基础上重建三维表面模型．研究的内容包括医学图像的分割和从轮廓线重建表面两个方面．首先,采用一种称为“非种子区域分割”方法提取物体的轮廓线;然后,建立一条反应颌骨走向的弓形“参照基准线”,充分利用轮廓线相对于它的“归一化位置”信息解决表面重建中的对应和分支问题;同时考虑相邻层的影响,使生成的表面更加光滑、自然．最后,提出“多阶凸包点对应匹配”的思想,用分层分段迭代的方法拼接三角面片．     

Context-aware mesh smoothing for biomedical applications

Smoothing algorithms allow to reduce artifacts from mesh generation, but often degrade accuracy. Thus, we present a method that identifies staircase artifacts which result from image inhomogeneities and binary segmentation in medical image data for subsequent removal by adaptive mesh smoothing. This paper makes the following specific contributions: caps, which are flat regions, resulting from segmentation or clipping at the endings of anatomical structures are detected and modified by smoothing; the effects of the adaptive smoothing method involving context information are quantitatively analyzed with respect to accuracy and their influence on blood flow simulations; the image stack orientation, which is relevant for this context-aware smoothing approach, is estimated automatically from the surface models. Thus, context-aware smoothing enables to adaptively smooth artifact areas, while non-artifact features can be preserved. The approach has been applied to CT neck datasets, as well as phantom data and the results are evaluated regarding smoothness and model accuracy. The accuracy of model orientation estimation and cap detection has been evaluated for clinical and phantom data. Finally, context-aware smoothing has been applied to CT angiography data for the simulation of blood flow. The simulation results are presented and prove the general suitability of context-aware smoothing.     

一种隐式曲面多相图像分割的变分水平集模型

隐式曲面上的图像处理,与曲面的性状和特征息息相关,运用多个函数标记不同区域来进行图像分割计算量大。针对上述问题首先借助遥感图像提出了一种隐式曲面构建方式,利用图像中的高程数据来构建山体曲面模型,进而计算山体区域的曲面面积。其次将基于一个水平集函数的多相图像分割的模型推广到隐式曲面上,并设计了相应的交替方向乘子法,通过求解一个函数的极值实现对图像多个区域的分割,最后多个数值实验对该方法和模型的高效性和鲁棒性进行了验证。     

Robust and Efficient Implicit Surface Reconstruction for Point Clouds Based on Convexified Image Segmentation

We present an implicit surface reconstruction algorithm for point clouds. We view the implicit surface reconstruction as a three dimensional binary image segmentation problem that segments the entire space $\mathbb R ^3$ or the computational domain into an interior region and an exterior region while the boundary between these two regions fits the data points properly. The key points with using an image segmentation formulation are: (1) an edge indicator function that gives a sharp indicator of the surface location, and (2) an initial image function that provides a good initial guess of the interior and exterior regions. In this work we propose novel ways to build both functions directly from the point cloud data. We then adopt recent convexified image segmentation models and fast computational algorithms to achieve efficient and robust implicit surface reconstruction for point clouds. We test our methods on various data sets that are noisy, non-uniform, and with holes or with open boundaries. Moreover, comparisons are also made to current state of the art point cloud surface reconstruction techniques.     

考场监控环境下泄题试卷检测方法研究

根据考场监控环境下存在利用云台可变焦摄像机获取考试试卷进行泄题的可能性,提出了一个基于图像处理和模式分类技术的作弊试卷检测方法。首先通过阈值初选策略进行试卷像素初检,并采用自适应的高斯混合模型使检测结果进一步适应不同的光照和场地,然后利用区域分析和增长技术消除检测噪音并形成区域,最后提取多个形状描述特征对检测到的区域进行分类,得到泄题试卷。通过广泛的定量和定性的实验分析验证了该方法的性能和效率。     

基于残差双注意力U-Net模型的CT图像囊肿肾脏自动分割

人体肾脏存在形状的多样性和解剖学的复杂性,囊肿病变也会导致肾脏形状发生大幅变化。为应对CT图像囊肿肾脏自动分割存在的诸多挑战,提出一种新型深度分割网络模型。该模型设计有带残差连接的双注意力模块,在残差结构的基础上,联合空间注意力和通道注意力机制自适应学习更加有效的特征表达。依据U-Net架构,以残差双注意力模块为基础模块构建编码器和解码器,设置层级间的跳跃连接,使网络能够更加关注肾脏区域特征,有效应对肾脏的形状变化。为了验证所提模型的有效性,从医院共采集79位肾囊肿患者的CT图像进行训练和测试,实验结果表明该模型能够准确分割CT图像切片中的肾脏区域,且各项分割指标优于多个经典分割网络模型。     

Automated atlas-based segmentation of NISSL-stained mouse brain sections using supervised learning

The problem of segmentation of mouse brain images into anatomical structures is an important stage of practically every analytical procedure for these images. The present study suggests a new approach to automated segmentation of anatomical structures in the images of NISSL-stained histological sections of mouse brain. The segmentation algorithm is based on the method of supervised learning using the existing anatomical labeling of the corresponding sections from a specialized mouse brain atlas. A mouse brain section to be segmented into anatomical structures is preliminarily associated with a section from the mouse brain atlas displaying the maximum similarity. The image of this section is then preprocessed in order to enhance its quality and to make it as close to the corresponding atlas image as possible. An efficient algorithm of luminance equalization, an extension of the well-known Retinex algorithm is proposed. A random forest is trained on pixel feature vectors constructed based on the atlas section images and the corresponding class labels associated with anatomical structures extracted from the atlas anatomical labeling. The trained classifier is then applied to classify pixels of an experimental section into anatomical structures. A new combination of features based on superpixels and location priors is suggested. Accuracy of the obtained result is increased by using Markov random field. Procedures of luminance equalization and subsequent segmentation into anatomical structures have been tested on real experimental sections.     

Multiphase segmentation using an implicit dual shape prior: Application to detection of left ventricle in cardiac MRI

Cardiac magnetic resonance imaging (MRI) has been extensively used in the diagnosis of cardiovascular disease and its quantitative evaluation. Cardiac MRI techniques have been progressively improved, providing high-resolution anatomical and functional information. One of the key steps in the assessment of cardiovascular disease is the quantitative analysis of the left ventricle (LV) contractile function. Thus, the accurate delineation of LV boundary is of great interest to improve diagnostic performance. In this work, we present a novel segmentation algorithm of LV from cardiac MRI incorporating an implicit shape prior without any training phase using level sets in a variational framework. The segmentation of LV still remains a challenging problem due to its subtle boundary, occlusion, and inhomogeneity. In order to overcome such difficulties, a shape prior knowledge on the anatomical constraint of LV is integrated into a region-based segmentation framework. The shape prior is introduced based on the anatomical shape similarity between endocardium and epicardium. The shape of endocardium is assumed to be mutually similar under scaling to the shape of epicardium. An implicit shape representation using signed distance function is introduced and their discrepancy is measured in a probabilistic way. Our shape constraint is imposed by a mutual similarity of shapes without any training phase that requires a collection of shapes to learn their statistical properties. The performance of the proposed method has been demonstrated on fifteen clinical datasets, showing its potential as the basis in the clinical diagnosis of cardiovascular disease.     

利用三维自适应分裂-合并的MRI图像分割算法设计

为了在医学图像分割中,发现均匀几何三维区域的复杂形状,以提高分割准确率,提出一种基于3D几何特征分裂-合并(ASM)的脑部MRI图像分割算法;首先构建简单平行六面体的12种3D区域分割策略,体积分割技术将整个体积划分为许多大的均匀三维几何区;然后,在体积内定义更多小的均匀区域,以便在随后的合并步骤中有更大的生存概率;最后,进行多级区域合并,合并阶段只涉及复杂ASM树的叶子,考虑灰度相似性和共同边界区的大小,将小的区域合并为大邻近区;相比其他几种MRI图像分割算法,提出的方法在分割过程对噪声具有鲁棒性,提高了分割性能和准确率;另外提出的方法不需要训练数据集。     

基于主动轮廓模型的肺纹理自动提取新方法

Computed tomography (CT) is the primary imaging modality for investigation of lung function and lung diseases. High resolution CT slice images of chest contain lots of texture information, which provides powerful datasets to research computer aid-diagnosis (CAD) system. But the extraction of lung tissue textures is a challenge task. In this paper, we introduce a novel method based on level set to extract lung tissue texture tree, which is automatic and effectual. Firstly, we propose an improved implicit active contour model driven by local binary fitting energy, and the parameters are dynamic and modulated by image gradient information. Secondly, a new technique of painting background based on intensity nonlinear mapping is brought forward to remove the influence of background during the evolution of single level set function. At last, a number of contrast experiments are performed, and the results of 3D surface reconstruction show our method is efficient and powerful for the segmentation of fine lung tree texture structures.     

ANGY: A Rule-Based Expert System for Automatic Segmentation of Coronary Vessels From Digital Subtracted Angiograms

This paper details the design and implementation of ANGY, a rule-based expert system in the domain of medical image processing. Given a subtracted digital angiogram of the chest, ANGY identifies and isolates the coronary vessels, while ignoring any nonvessel structures which may have arisen from noise, variations in background contrast, imperfect subtraction, and irrelevent anatomical detail. The overall system is modularized into three stages: the preprocessing stage and the two stages embodied in the expert itself. In the preprocessing stage, low-level image processing routines written in C are used to create a segmented representation of the input image. These routines are applied sequentially. The expert system is rule-based and is written in OPS5 and LISP. It is separated into two stages: The low-level image processing stage embodies a domain-independent knowledge of segmentation, grouping, and shape analysis. Working with both edges and regions, it determines such relations as parallel and adjacent and attempts to refine the segmentation begun by the preprocessing. The high-level medical stage embodies a domain-dependent knowledge of cardiac anatomy and physiology. Applying this knowledge to the objects and relations determined in the preceding two stages, it identifies those objects which are vessels and eliminates all others.     

Structure-preserving smoothing of biomedical images

Smoothing of biomedical images should preserve gray-level transitions between adjacent tissues, while restoring contours consistent with anatomical structures. Anisotropic diffusion operators are based on image appearance discontinuities (either local or contextual) and might fail at weak inter-tissue transitions. Meanwhile, the output of block-wise and morphological operations is prone to present a block structure due to the shape and size of the considered pixel neighborhood.In this contribution, we use differential geometry concepts to define a diffusion operator that restricts to image consistent level-sets. In this manner, the final state is a non-uniform intensity image presenting homogeneous inter-tissue transitions along anatomical structures, while smoothing intra-structure texture. Experiments on different types of medical images (magnetic resonance, computerized tomography) illustrate its benefit on a further process (such as segmentation) of images.     

Search strategies for shape regularized active contour

Nonlinear shape models have been shown to improve the robustness and flexibility of contour-based object segmentation when there are appearance ambiguities between the object and the background. In this paper, we focus on a new search strategy for the shape regularized active contour (ShRAC) model, which adopts existing nonlinear shape models to segment objects that are similar to a set of training shapes. The search for optimal contour is performed by a coarse-to-fine algorithm that iterates between combinatorial search and gradient-based local optimization. First, multi-solution dynamic programming (MSDP) is used to generate initial candidates by minimizing only the image energy. In the second step, a combination of image energy and shape energy is minimized starting from these initial candidates using a local optimization method and the best one is selected. To generate diverse initial candidates while reducing invalid shapes, we apply two pruning methods to the search space of MSDP. Our search strategy combines the advantages of global combinatorial search and local optimization, and has shown excellent robustness to local minima caused by distracting suboptimal solutions. Experimental results on segmentation of different anatomical structures using ShRAC, as well as preliminary results on human silhouette segmentation are provided.     

基于水平集和形状描述符的腹部CT序列肝脏自动分割

肝脏分割是计算机辅助肝脏疾病诊断的重要前提和基础.本文提出了一种新的基于水平集和形状描述符的腹部CT序列图像肝脏自动分割方法.首先, 对原始腹部CT序列图像进行预处理, 去除与肝脏不相关的器官和组织.然后, 利用灰度偏移场, 结合周长项、距离正则项和相邻切片肝脏分割结果构建水平集能量函数, 实现CT序列肝脏自动分割.为避免分割误差累积, 提出一种基于形状描述符和瓶颈率的肝脏边缘优化方法, 在每张切片分割完毕后去除由于灰度重叠造成的过分割.通过对XHCSU14数据库和Sliver07数据库中腹部CT序列的肝脏分割实验, 以及与其他肝脏分割算法的比较, 表明了本文方法的有效性, 且分割精度高, 鲁棒性强.     

An interactive medical image segmentation system based on the optimal management of regions of interest using topological medical knowledge

This paper presents an original interactive system for efficient medical image segmentation in computer aided diagnosis. The main originality concerns the method used to manage, according to an a priori topological-based structural model, regions of interest (ROIs) within which computations can be constrained. The goal is then to avoid the processing of irrelevant image points, therefore improving and accelerating segmentations. In the case of a hierarchical modeling procedure, our ROI management method enables, for delineating a given medical structure, to optimally determine image points of interest by taking previously segmented structures into account. We propose a mathematical formulation of the method as well as a possible implementation within an interactive system. We also detail an experience report focussing on the segmentation of several abdominal structures from a CT image. It illustrates the behavior and the potential of our method.