基于配准与ASM方法的医学图像分割

目的随着医学图像数据的急剧增长,建立从医学图像中自动分割特定解剖结构的算法。方法首先,获取的脑图像体数据集通过与参考体数据集的配准,使对应层图像包含与参考数据相似的解剖结构;然后利用训练得到的统计形状模型自动定位、分割指定的解剖结构。结果实验表明这种算法能取得良好的分割结果。结论本文提出的基于互信息的图像配准和统计形状模型的分割算法,能够实现从体数据中自动定位解剖结构所在的图像位置并分割出目标结构。     

蚁群算法在磁共振图像分割中的应用

研究一种智能的图像分割方法并且把这种分割方法应用到磁共振的图像分割中,对目前应用的图像分割方法进行比较后提出了一种基于蚁群的磁共振图像分割方法。最后将算法应用到颅脑磁共振的图像分割当中,实验结果表明新算法具有很强的噪声和模糊边界的检测能力。该算法的提出对磁共振研究和临床应用都有很大的理论和实践意义。     

基于多重分形的医学图像分割方法

背景:由于人体解剖结构的复杂性、组织器官形状的不规则性及不同个体间的差异性,所以比较适合用多重分形来分析.目的:采用多重分形理论对医学图像进行图像分割.方法:采用基于容量测度的多重分形谱计算及基于概率测度的多重分形谱计算方法对图像进行分割.对于待处理图片分别进行传统的区域生长分割,max容量测度图像分割,sum容量测度图像分割,概率测度图像分割等4种分割,并加入噪声后再进行同样的分割处理作为比较.结果与结论:采用的两种基于多重分形谱的计算法中,基于容量测量的多重分形谱计算方法的关键是定义合适的测度μα;基于概率测度的多重分形谱计算方法的关键是定义合适的归一化概率Pi,不同的测度(概率)和不同的阈值对结果的影像比较大.基于概率测度的方法对噪声比较敏感,但是在滤过噪声时对图像象素大小变化比较大、比较复杂的图像有较好的分割效果.实验表明基于多重分形谱的医学图像分割方法在选择合适的测度(概率)和阈值时是可行的,特别是在较为复杂的图像处理中对于纹理和边缘的区别上有较大的优势,在准确地分割的同时能保留更多的细节,具有重要的实际意义.同时,多重分形也可以作为一种图像的特征,为特征提取多提供一种有力的数据.     

Gibbs场先验参数估计在医学图像分割中的应用

背景:基于马尔科夫随机场的图像分割算法已经成为医学图像分割的重要方法,其中,Gibbs场先验参数的取值对分割精度有很大的影响.目的:根据脑部MR图像的成像特点,探讨Gibbs场先验参数的估计方法,从而提高图像分割的精度.方法:通过对脑部MR图像的统计分析,得到图像高斯噪声的方差与Gibbs场先验参数的对应关系.然后在基于马尔可夫随机场图像分割算法的迭代过程中,根据高斯分布的方差估计值,用插值方法估计Gibbs场先验参数.结果与结论:通过对模拟脑部MR图像和临床脑部MR图像分割实验,表明该方法比传统的设定Gibbs场先验参数为某一常数的方法有更精确的图像分割能力,并且实现了图像的自适应分割,具有方法简单、运算速度快、稳健性好的特点.     

海马结构的磁共振图像分割方法

1 海马结构MR图像分割的目的和意义 图像分割(image segmentation)是指根据区域的相似性以及区域间的不同,将一幅图像分割成若干互不交迭区域的过程.海马结构的图像分割就是在图像上把海马结构的边界找出来,使其成为一个连通、闭合区域的过程.海马结构体积测量(volume measurement)在颞叶癫痫、老年性痴呆、遗忘综合征、精神分裂症等神经系统疾病的临床诊断、治疗、疗效评价及计算机辅助诊断(CAD)等方面有重要的应用价值[1].海马结构(hippocampal formation)的图像分割是海马结构体积测量、三维重建的关键和基础.因此,海马结构的图像分割在临床上具有重要意义.临床上使用的分割方法主要还是以人工分割为主,因此研究适用于海马结构MR图像分割的方法有很广的临床应用价值[2,3].     

基于阈值分割和Snake模型的弱边缘医学超声图像自动分割

医学超声图像分割是图像处理中的一项关键技术.文章以胆结石超声图像为例,介绍一种新的弱边缘超声图像自动分割算法.首先采用基于直方图凹度分析的闽值分割方法确定Snake模型的初始蛇,再基于Snake模型结合贪婪算法对图像进行目标分割.实验结果表明该算法对弱边缘现象较为严重的医学超声图像进行目标分割时,定位准确,分割效果良好,足一种全自动的超声医学图像分割方法.     

超声图像分割研究现状

目前,医学图像作为临床检测以及放疗引导的重要参考依据,在医学的发展中起着关键作用。医学图像主要包括计算机断层扫描（CT）、核磁共振（MRI）、X射线、超声（US）等,超声相对前三者价格较低,对软组织成像效果较好且对人体基本无伤害,在现阶段应用已越来越广泛。超声图像分割对后期图像分析有很大的作用,可以给临床诊断及放疗摆位等提供一定的参考,本文就超声图像的分割的传统方法、基于形变模型的分割方法和结合深度学习方法的研究情况进行阐述。     

神经网络技术及其在医学图像处理中的应用

神经网络技术是模拟生物神经系统的原理而构成的一种新型智能信息处理技术,已成功应用于疾病预报、方剂配伍等医学领域。近年来,在医学图像处理与分析领域,神经网络技术也得到了广泛应用。本文就神经网络技术在医学图像分割、医学图像配准以及基于医学图像的计算机辅助诊断技术等方面的应用及其研究进展进行综述,阐述具有代表性的技术和算法。     

医学图像处理技术在组织工程研究中的应用

数字图像处理在组织工程及生物医学工程研究领域的应用十分普遍。近年来,伴随着医学影像技术的快速发展,医学成像设备在越来越多的医学领域里被应用,同时医学图像     

级联结构网络在肺癌患者颈部淋巴结超声图像分割中的应用价值

为了准确诊断肺癌转移,本文应用深度学习技术对肺癌患者颈部淋巴结超声图像病灶区域进行分割,提出了一种用于超声图像分割的级联注意力UNet网络,该级联结构是将注意力UNet与EfficientNet相结合的二阶段分割网络,第一阶段为粗分割,第二阶段为细分割,编码器采用EfficientNet-B5作为主干网,图像多尺度输入;提出了适用于小目标、小样本场景的新损失函数;试验结果表明,本文提出的级联结构网络在肺癌患者颈部淋巴结超声图像分割中网络性能优异,Dice系数达到0.95,较其他UNet方法具有更优的分割性能。     

Weakly supervised histopathology cancer image segmentation and classification

Labeling a histopathology image as having cancerous regions or not is a critical task in cancer diagnosis; it is also clinically important to segment the cancer tissues and cluster them into various classes. Existing supervised approaches for image classification and segmentation require detailed manual annotations for the cancer pixels, which are time-consuming to obtain. In this paper, we propose a new learning method, multiple clustered instance learning (MCIL) (along the line of weakly supervised learning) for histopathology image segmentation. The proposed MCIL method simultaneously performs image-level classification (cancer vs. non-cancer image), medical image segmentation (cancer vs. non-cancer tissue), and patch-level clustering (different classes). We embed the clustering concept into the multiple instance learning (MIL) setting and derive a principled solution to performing the above three tasks in an integrated framework. In addition, we introduce contextual constraints as a prior for MCIL, which further reduces the ambiguity in MIL. Experimental results on histopathology colon cancer images and cytology images demonstrate the great advantage of MCIL over the competing methods.     

Upper airway segmentation and dimensions estimation from cone-beam CT image datasets

Objective To segment and measure the upper airway using cone-beam computed tomography (CBCT). This information may be useful as an imaging biomarker in the diagnostic assessment of patients with obstructive sleep apnea and in the planning of any necessary therapy. Methods With Institutional Review Board Approval, anonymous CBCT datasets from subjects who had been imaged for a variety of conditions unrelated to the airway were evaluated. DICOM images were available. A segmentation algorithm was developed to separate the bounded upper airway and measurements were performed manually to determine the smallest cross-sectional area and the anteriorposterior distance of the retropalatal space (RP-SCA and RP-AP, respectively) and retroglossal space (RG-SCA and RG-AP, respectively). A segmentation algorithm was developed to separate the bounded upper airway and it was applied to determine RP-AP, RG-AP, the smallest transaxial-sectional area (TSCA) and largest sagittal view airway area (LCSA). A second algorithm was created to evaluate the airway volume within this bounded upper airway. Results Measurements of the airway segmented automatically by the developed algorithm agreed with those obtained using manual segmentation. The corresponding volumes showed only very small differences considered clinically insignificant. Conclusion Automatic segmentation of the airway imaged using CBCT is feasible and this method can be used to evaluate airway cross-section and volume comparable to measurements extracted using manual segmentation.     

An integrated approach to segmentation and nonrigid registration for application in image-guided pelvic radiotherapy

External beam radiotherapy (EBRT) has become the preferred options for nonsurgical treatment of prostate cancer and cervix cancer. In order to deliver higher doses to cancerous regions within these pelvic structures (i.e. prostate or cervix) while maintaining or lowering the doses to surrounding non-cancerous regions, it is critical to account for setup variation, organ motion, anatomical changes due to treatment and intra-fraction motion. In previous work, manual segmentation of the soft tissues is performed and then images are registered based on the manual segmentation. In this paper, we present an integrated automatic approach to multiple organ segmentation and nonrigid constrained registration, which can achieve these two aims simultaneously. The segmentation and registration steps are both formulated using a Bayesian framework, and they constrain each other using an iterative conditional model strategy. We also propose a new strategy to assess cumulative actual dose for this novel integrated algorithm, in order to both determine whether the intended treatment is being delivered and, potentially, whether or not a plan should be adjusted for future treatment fractions. Quantitative results show that the automatic segmentation produced results that have an accuracy comparable to manual segmentation, while the registration part significantly outperforms both rigid and nonrigid registration. Clinical application and evaluation of dose delivery show the superiority of proposed method to the procedure currently used in clinical practice, i.e. manual segmentation followed by rigid registration.     

比较TOF-PET和PSF-PET图像重建技术在临床中的应用价值

目的：TOF-PET（Time of flight-positron emission tomography,TOF-PET）和PSF-PET（Point spread function-PET,PSF-PET）是用于提高PET性能的最新技术。本研究目的是比较TOF-PET和PSF-PET对脑、肝脏正常组织以及胸腹部肿瘤病灶组织对18F-FDG标准摄取率最大值（Standard uptake value maximum,SUVmax）的影响。以指导临床更好使用TOF-PET和PSF-PET技术。材料和方法：35例患者（男19例,女16例,年龄（58.69±12.88）岁）,体质量指数（BMI）25.18±4.32。35例患者肺部病灶29个,腹部12个。CT测量病灶直径范围5~40 mm。使用GE Discovery PET/CT Elite和AW工作站。选择SharpIR+VUE Point HD,TOF+VUE Point HD 和SharpIR+VUE Point HD+TOF重建PET图像。SharpIR和VUE Point HD是分别PSF和迭代重建的商品名。结果：TOF与非TOF比较,胸部、腹部病灶SUVmax分别提高7.1%（n=29,P<0.001）、5.54%（n=12,P<0.006）。BMI>25组TOF与非TOF相比较,胸部、腹部病灶SUVmax分别提高15.6%（n=12,P<0.001）和20.6%（n=4,P<0.035）。同一组病灶（n=6）采用VUE HD+SharpIR、VUE HD+TOF、VUE HD+SharpIR+TOF处理后与VUE HD相比较,SUVmax分别提高了17.69%、2.3%和22.54%。对于所有病灶、脑正常组织,TOF-PET和PSF-PET均能提高SUVmax,但是对于BMI>25的病灶,TOF-PET提高SUVmax更明显。PSF-PET与TOF-PET相比较,PSF-PET对SUVmax提高的幅度明显大于TOF-PET。肝脏正常组织受TOF-PET技术影响并不明显。讨论：TOF-PET和PSF-PET均能提高病灶和脑正常组织的SUVmax,但是PSF-PET优于TOF-PET。对于BMI>25的患者的胸部、腹部病灶,TOF-PET技术对病灶SUVmax的提高更为显著。     

3D image segmentation using combined shape-intensity prior models

Objective Statistical models for medical images have been developed to increase robustness in the segmentation process. In this project, a fully automatic approach to build a statistical shape-intensity model and combine this model with level set segmentation was designed, implemented and tested by applying the algorithm to clinical image data. Methods By using a hierarchical registration approach based on mutual information and demons registration, 3D statistical shape-intensity models were created by applying Principal Component Analysis. Using these models in combination with level set segmentation results in a fully automatic modeling and segmentation pipeline. Results Examples for shape-intensity models were synthesized and these models were used to automatically segment 3D MRI and CT images. Quantitative evaluation of the framework was performed by comparing automatic segmentation results to segmentation results of medical experts. Conclusion Evaluation tests in which this method was used for the automatic segmentation of femora and cardiac MRI endocardial surfaces are very promising. The implementation of an additional cost function term and the addition of information about the surroundings of an organ in the model are currently under development.     

二维超声图像定量在诊断原发性肾小球肾炎中的应用

目的 应用自行开发的图像处理系统,探讨量化诊断原发性肾小球肾炎的价值和途径。方法 采集受检者每只肾脏的冠状切面二维超声灰阶图,并将所采集的声像图经图像接口输入计算机,应用自行开发的图像处理系统进行量化分析。结果 肾实质回声的灰阶值随病变加重而变大,肾集合系统灰阶值随病变加重而逐渐变小（P＜0．01）。早期肾炎的肾实质灰阶均值为80.2dB/cm^2,肾集合系统灰阶均值为103.4dB/cm^2,两者的灰阶比值在0．80以内,中期肾炎肾实质灰阶均值为84.6dB/cm^2,肾集合系统灰阶均值为96.3dB/cm^2,两者的灰阶比值在0．80－0．92,晚期肾实质灰阶均值为87.3dB/cm^2,肾集合系统灰阶均值为91.7dB/cm^2,两者的灰阶比值在0．92以上。结论 应用肾脏实质与肾集合系统的灰阶值能够反映肾炎病变程度,设计的量化分析软件具有临床应用价值。     

X-ray image segmentation for vertebral mobility analysis

Objective The goal of this work is to extract the parameters determining vertebral motion and its variation during flexion–extension movements using a computer vision tool for estimating and analyzing vertebral mobility. Materials and Methods To compute vertebral body motion parameters we propose a comparative study between two segmentation methods proposed and applied to lateral X-ray images of the cervical spine. The two vertebra contour detection methods include (1) a discrete dynamic contour model (DDCM) and (2) a template matching process associated with a polar signature system. These two methods not only enable vertebra segmentation but also extract parameters that can be used to evaluate vertebral mobility. Lateral cervical spine views including 100 views in flexion, extension and neutral orientations were available for evaluation. Vertebral body motion was evaluated by human observers and using automatic methods. Results The results provided by the automated approaches were consistent with manual measures obtained by 15 human observers. Conclusion The automated techniques provide acceptable results for the assessment of vertebral body mobility in flexion and extension on lateral views of the cervical spine. Electronic supplementary material The online version of this article (doi:) contains supplementary material, which is available to authorized users.     

Interactive segmentation based on the live wire for 3D CT chest image analysis

Object The definition of regions of interest (ROIs) such as suspect cancer nodules or lymph nodes in 3D MDCT chest images is often difficult because of the complexity of the phenomena that give rise to them. Manual slice tracing has been used commonly for such problems, but it is extremely time consuming, subject to operator biases, and does not enable reproducible results. Proposed automated 3D image-segmentation methods are generally application dependent, and even the most robust methods have difficulty in defining complex ROIs. Materials and methods The semi-automatic interactive paradigm known as live wire has been proposed by researchers, whereby the human operator interactively defines an ROI’s boundary, guided by an active automated method. We propose 2D and 3D live-wire methods that improve upon previously proposed techniques. The 2D method gives improved robustness and incorporates a search region to improve computational efficiency. The 3D method requires the operator to only consider a few 2D slices, with an automated procedure performing the bulk of the analysis. Results For tests run with five human operators on both 2D and 3D ROIs in 3D MDCT chest images, the reproducibility was >97% and the ground-truth correspondence was at least 97%. The 2D live-wire approach was ≥14 times faster than manual slice tracing, while the 3D method was ≥28 times faster than manual slice tracing. Finally, we describe a computer-based tool and its application to 3D MDCT-based planning and follow-on live guidance of bronchoscopy. Conclusion The live-wire methods are efficient, reliable, easy to use, and applicable to a wide range of circumstances.     

Integrating segmentation methods from the Insight Toolkit into a visualization application

The Insight Toolkit (ITK) initiative from the National Library of Medicine has provided a suite of state-of-the-art segmentation and registration algorithms ideally suited to volume visualization and analysis. A volume visualization application that effectively utilizes these algorithms provides many benefits: it allows access to ITK functionality for non-programmers, it creates a vehicle for sharing and comparing segmentation techniques, and it serves as a visual debugger for algorithm developers. This paper describes the integration of image processing functionalities provided by the ITK into VolView, a visualization application for high performance volume rendering. A free version of this visualization application is publicly available and is available in the online version of this paper. The process for developing ITK plugins for VolView according to the publicly available API is described in detail, and an application of ITK VolView plugins to the segmentation of Abdominal Aortic Aneurysms (AAAs) is presented. The source code of the ITK plugins is also publicly available and it is included in the online version.