An Improved Fuzzy Connected Image Segmentation Method Base on CUDA

已有的模糊连接并行算法CUDA-k FOE未考虑线程块边缘点同时更新所引发的竞争问题,导致计算结果出现少量误差.由于医学图像处理对精度的要求很高,为了解决边缘点计算误差的问题,基于CUDA-k FOE提出一种修正迭代算法.首先分析了CUDA-k FOE算法在线程块边缘产生竞争的原因;然后讨论了边缘点亲和力的所有可能的传递路径,以及由此造成的出错情况;最后提出二次迭代修正算法,将第一次迭代得到的所有边缘点转入第二次的修正迭代步骤,从而修正第一次迭代中错误的亲和力值.采用3组不同规格的CT序列对肝脏血管进行分割实验,并选用3个不同的种子点进行算法验证,结果表明,文中算法的计算结果与串行版本一致,解决了CUDA-k FOE算法的计算误差问题.A paralleled CUDA version of k FOE(CUDA-k FOE)was proposed to segment medical images. CUDA-k FOE achieves fast segmentation when processing large image datasets. However, it cannot precisely handle the competition of edge points when update operations happen by multiple threads simultaneously, thus an iterative correction method to improve CUDA-k FOE was proposed. By analyzing all the pathways of marginal voxels affinity and their consequently caused results, a two iteration correction scheme is employed to achieve the accurate calculation. In these two iterations, the resulted marginal voxels from the first iteration are used as the correction input of the second iteration, therefore, the values of affinity are corrected in the second iteration. Experiments are conducted on three CT image sequences of liver vessels with small, medium, and large size. By choosing three different seed points, final results are not only comparable to the sequential implementation of fuzzy connected image segmentation algorithm on CPU, but achieve more precise calculation compared with CUDA-k FOE.国家自然科学基金(61001144;61102137;61301010;61327001

Directional region growing algorithm and its applications in vessel segmentation

针对医学图像中微细管道结构灰度连续性差,采用常规区域生长法进行分割容易丢失末梢的问题,提出一种定向区域生长算法,可以在生长过程中跨越管道结构中的低灰度区域。算法向图像中已生长区域外灰度最高的方向进行生长,每次将一个体素加入已生长区域,将图像转变为一颗以种子点为根结点的树,再从叶子结点进行回溯以确定感兴趣区域。对实现算法的数据结构进行了讨论。算法可以应用于任意维的图像。对2维和3维图像的测试结果表明,相对于常规的区域生长法,算法可以分割出更多的血管分支。算法对3维图像的运行时间为秒钟量级,可以满足临床应用的要求。Accurate extraction of the vasculature in medical images is prerequisite to structural analysis and further applications such as surgical planning.Region growing algorithm is a simple and effective method to extract thick blood vessels which makes use of the spatial continuity of the vascular tree,while the extraction result of small vessels like hepatic artery is unacceptable.In order to solve the problem that the continuity of tenuous vasculature is poor in medical images and vessel segmentation based on traditional region growing may lose distal branches,a directional region growing(DRG) algorithm is proposed which can skip the low gray area in the vasculature during the growing process.The algorithm grows towards the direction of the maximum gray around the grown region,and adds one voxel to the grown region in each iteration.The image is transformed into a tree after the growing process in which the seed point is the root.A trace back procedure beginning from the leaf nodes of the tree can finally determine the region of interest(ROI).The algorithm relaxes the conditions to determine ROI,and small area with low gray in the ROI is permitted.There are two time-consuming steps in the algorithm due to the enormous amount of data in 3D medical images,one is to determine the growing direction in each iteration,the other is to construct the paths from the seed point to leaf nodes during the trace back procedure.Data structure to improve the speed of the algorithm is discussed.The algorithm can be applied to images with any dimension.The algorithm is tested with 2D and 3D images.In both conditions,the segmentation results obtained by DRG contain more distal branches in comparison with traditional region growing algorithm.To some vein phase CT images with poor quality,the proposed algorithm can also generate better results.Four parameters should be appointed in the algorithm and the empirical values are given.The computational time of the algorithm on 3D images is several seconds,which is acceptable in clinical applications.The surface of the extracted vasculature is rough due to the discrete nature of digital images,and further study is needed to smooth the surface before visualization.国家自然科学基金项目(60701022;30770561

肝脏管道供血分布及残肝体积的计算

在肝切除、肝移植等手术计划中,精确地计算残肝体积至关重要,它能够直接影响到手术的成败.文中结合临床上肝外科手术的术式,提出了能够实时、准确地计算出基于肝脏管道供血分布的残肝体积的算法.首先基于个体化肝脏CT数据,通过分割和细化2个步骤建立肝实质三维模型及肝内管道的抽象树状模型;在此基础上,通过人机交互灵活地选定肝内管道分支数目和分支起点,并基于多背景距离变换计算肝脏供血分布和各部分所占比例,从而得到准确的残肝体积.实验结果表明,该算法计算速度快,对肝切除结果的模拟和计算精度能满足实际临床需求,可为手术计划提供指导依据.国家自然科学基金(61001144,61102137,61271336

基于3D＿ResUnet肝脏CT图像分割的临床应用研究

目的:为解决传统肝实质分割方法在阈值分割方面存在的分割精度低的问题。方法:采用AI自动识别算法,通过Unet与Resnet相结合的3D_ResUnet网络对肝脏CT图像进行分割,并对分割结果通过最大联通分量的方法去除杂质,得到较为精确的肝脏区域,实现肝实质自动分割。结果:基于3D_ResUnet的肝脏CT图像分割,其分割的平均Dice为96.12%,高于3D_Unet的分割精度。结论:基于3D_ResUnet的肝脏CT图像分割提高了肝实质分割的精度,实现了无需人工交互的全自动分割,通过应用在肝癌手术计划系统中,为临床医生的肝癌手术规划提供了可视化依据。国家自然科学基金(编号：61327001)~

一种基于临床CT影像的肝内血管体积测量方法

肝脏血管体积不仅可以为临床疾病诊断提供重要参考,还对肝脏供血能力和肝脏储备功能的评估具有重要参考价值。本研究提出一种血管体积测量方法,利用置信连接和ITK-SNAP分割出肝内门静脉并进行空洞填补后,通过体素数目换算得到血管体积。该方法可快速、准确的计算出肝内门静脉的体积。实验采用10套不同规格的肝脏CT图像进行肝内门静脉血管体积测量,并选取3套数据与基于手工测量得到的血管体积进行对比。实验结果表明,通过本研究方法测量得到的体积与手工测量结果基本一致,利用统计学方法得到的肝内门静脉的体积为(11.316±1.080)m L。国家自然科学基金项目(61001144,61271336,61327001

深度神经网络压缩与加速综述

深度神经网络在人工智能的应用中,包括计算机视觉、语音识别、自然语言处理方面,取得了巨大成功.但这些深度神经网络需要巨大的计算开销和内存存储,阻碍了在资源有限环境下的使用,如移动或嵌入式设备端.为解决此问题,在近年来产生大量关于深度神经网络压缩与加速的研究工作.对现有代表性的深度神经网络压缩与加速方法进行回顾与总结,这些方法包括了参数剪枝、参数共享、低秩分解、紧性滤波设计及知识蒸馏.具体地,将概述一些经典深度神经网络模型,详细描述深度神经网络压缩与加速方法,并强调这些方法的特性及优缺点.此外,总结了深度神经网络压缩与加速的评测方法及广泛使用的数据集,同时讨论分析一些代表性方法的性能表现.最后,根据不同任务的需要,讨论了如何选择不同的压缩与加速方法,并对压缩与加速方法未来发展趋势进行展望.国家重点研发计划项目(2017YFC0113000,2016YFB10015032);;国家自然科学基金项目(U1705262,61772443,61402388,61572410);国家自然科学基金优秀青年科学基金项目(61422210);;福建省自然科学基金项目(2017J01125)~

2D/3D级联卷积在分割CT肺动脉上的应用研究

医学影像分割是计算机辅助诊断的重要组成部分。针对CT影像的三维特性,提出了一种基于2D/3D级联卷积的Unet网络结构用来分割肺动脉。该结构相比基于传统2D卷积的方法,关联了第三维度信息,提高了分割准确度和泛化能力,相比基于传统3D卷积的方法提高了准确度和执行效率。实验对多套肺动脉增强CT数据集做了验证,分割准确率达到85.7%,高于传统2D和3D Unet网络,同时执行效率较3D Unet提高近30%,在CT影像分割上做到了效率和准确度的兼顾。国家自然科学基金(编号:61001144,61102137,61327001,61671399)~

无铅镉化学镍沉积及其表征

优化无铅、无镉化学镍沉积工艺,应用扫描电子显微镜(SEM)、能量色散谱(EDS)、X射线衍射(XRD)和电化学方法表征化学镀镍层的形貌、组成、结构和电化学活性.结果表明,化学镍自催化沉积速率为22.4μm.h-1;沉积速率随溶液温度和pH值的提高而增大;比之硫酸镍,次磷酸钠对沉积速率的影响明显许多.化学镀镍层磷含量为7.8%(by mass),结构致密、晶粒细小,呈非晶态结构.在NaCl溶液中,镀层呈现良好的电化学耐蚀性

Reseach of the key technology for the voxel based 3D visualization of the chinese brain atlas

一个高精度、高速度、又易于操作的三维数字化人脑图谱，能使医生清楚地看到人脑内部复杂的空间关系，在手术计划、模型驱动分割及神经解剖教学等方面都有重要的应用。 本文对三维人脑图谱可视化的以下几个关键技术进行了深入研究： 1、 体数据的生成、压缩和存储技术 2、 体数据的切割技术 3、 脑图谱标签的制作、存储、显示和隐藏技术 4、 医学图象的简单配准技术 作者对以上几个方面均有一定的独到见解，并设计了相应的算法加以实现。 最后，使用以上的技术成果，作者基于PC机和Windows的平台开发了一个国人脑图谱的三维可视化演示系统，提供了一个图形用户界面，支持对三维场景中的二维及三维物体进行旋...A three-dimensional digitized atlas of human brain, which is high precise, high-speed, and easy to be operated, can help us to observe into the complex structure of human brain clearly. Therefore it can be widely used in operation plan, model driven segmentation and neuropathy anatomy teaching. This paper describes some key technology during the developing of a Chinese brain atlas of 3D visualiz...学位：工学硕士院系专业：计算机与信息工程学院计算机科学系_计算机应用技术学号：19992800

Research Progress on Simulation of Deformable Objects Using Finite Elements Model

可变形物体的建模与仿真是当今计算机图形学领域的难点和热点之一。对于手术模拟等需要精确计算的场合,最佳的建模工具仍然是有限元模型。惟一限制有限元模型广泛应用的瓶颈,在于有限元的大计算量。为了克服这个困难,目前出现了很多新颖的技术方法。对这些有限元新技术做一个比较全面的综述,对其进行分类和概括,并总结这些最新成果的解决思路以及具体的解决方法,对有意以有限元为工具开发仿真系统的研究者们具有很大的启发和参考价值。Nowadays the simulation of deformable objects is a hotspot in the field of computer graphics. In the simulation of operation, for the request of precision, Finite Elements Models (FEM) is the best model. The disadvantage of FEM is the mass quantity of the calculation. To solve this, many new techniques has come out recently. To summarize these new FEM techniques, including their thoughts and the solutions, it will be helpful to the researchers who want to use FEM to do some simulation work.国家自然基金(60371012);; 卫生部联合基金(WKJ2005-2-001);; 福建省科技重点项目(2005Y0018)资助;; 厦门市科技计划项目(3502Z20051015)