An automatic segmentation algorithm for 3D cell cluster splitting using volumetric confocal images

With the rapid advance of three-dimensional (3D) confocal imaging technology, more and more 3D cellular images will be available. Segmentation of intact cells is a critical task in automated image analysis and quantification of cellular microscopic images. One of the major complications in the automatic segmentation of cellular images arises due to the fact that cells are often closely clustered. Several algorithms are proposed for segmenting cell clusters but most of them are 2D based. In other words, these algorithms are designed to segment 2D cell clusters from a single image. Given 2D segmentation methods developed, they can certainly be applied to each image slice with the 3D cellular volume to obtain the segmented cell clusters. Apparently, in such case, the 3D depth information with the volumetric images is not really used. Often, 3D reconstruction is conducted after the individualized segmentation to build the 3D cellular models from segmented 2D cellular contours. Such 2D native process is not appropriate as stacking of individually segmented 2D cells or nuclei do not necessarily form the correct and complete 3D cells or nuclei in 3D. This paper proposes a novel and efficient 3D cluster splitting algorithm based on concavity analysis and interslice spatial coherence. We have taken the advantage of using the 3D boundary points detected using higher order statistics as an input contour for performing the 3D cluster splitting algorithm. The idea is to separate the touching or overlapping cells or nuclei in a 3D native way. Experimental results show the efficiency of our algorithm for 3D microscopic cellular images.     

An automatic algorithm for the segmentation and morphological analysis of microvessels in immunostained histological tumour sections

A fully automatic segmentation and morphological analysis algorithm for the analysis of microvessels from CD31 immunostained histological tumour sections is presented. Development of the algorithm exploited the distinctive hues of stained vascular endothelial cells, cell nuclei and background, to provide the seeds for a 'region-growing' method for object segmentation in the 3D hue, saturation, value (HSV) colour model. The segmented objects, identified as microvessels by CD31 immunostaining, were post-processed with three morphological tasks: joining separate objects that were likely to belong to a single vessel, closing objects that had a narrow gap around their periphery, and splitting objects with multiple lumina into individual vessels. The automatic segmentation was validated against a hand-segmented set of 44 images from three different SW1222 human colorectal carcinomas xenografted into mice. 96.3 ± 0.9% of pixels were found to be correctly classified. Automated segmentation was carried out on a further 53 images from three histologically distinct mouse fibrosarcomas (MFs) for morphological comparison with the SW1222 tumours. Four morphometric measurements were calculated for each segmented vessel: vascular area (VA), ratio of lumen area to vascular area (lu/VA), eccentricity (e), and roundness (ro). In addition, the total vascular area relative to tumour tissue area (rVA) was calculated. lu/VA, e and ro were found to be significantly smaller in MF tumours than in SW1222 tumours (p < 0.05; unpaired t-test). The algorithm is available through the website http://www.caiman.org.uk where images can be uploaded, processed and sent back to users. The output from CAIMAN consists of the original image with boundaries of segmented vessels overlaid, the calculated parameters and a Matlab file, which contains the segmentation that the user can use to derive further results.     

Combining intensity, edge and shape information for 2D and 3D segmentation of cell nuclei in tissue sections

We present a region‐based segmentation method in which seeds representing both object and background pixels are created by combining morphological filtering of both the original image and the gradient magnitude of the image. The seeds are then used as starting points for watershed segmentation of the gradient magnitude image. The fully automatic seeding is done in a generous fashion, so that at least one seed will be set in each foreground object. If more than one seed is placed in a single object, the watershed segmentation will lead to an initial over‐segmentation, i.e. a boundary is created where there is no strong edge. Thus, the result of the initial segmentation is further refined by merging based on the gradient magnitude along the boundary separating neighbouring objects. This step also makes it easy to remove objects with poor contrast. As a final step, clusters of nuclei are separated, based on the shape of the cluster. The number of input parameters to the full segmentation procedure is only five. These parameters can be set manually using a test image and thereafter be used on a large number of images created under similar imaging conditions. This automated system was verified by comparison with manual counts from the same image fields. About 90% correct segmentation was achieved for two‐ as well as three‐dimensional images.     

基于数学形态学的新方法在脑组织分割中的应用

针对人体脑部切片图像特点,提出了一种基于数学形态学的脑组织自动分割算法.该算法首先通过形态重构获得粗糙的脑组织区域,然后运用腐蚀和膨胀运算进行边界定位分割出了脑组织,最后对连续断层图像的分割结果进行了三维重建.结果表明该算法分割准确且自动化程度高,适合于大量序列切片图像的快速自动分割.     

Four‐dimensional telomere analysis in recordings of living human cells acquired with Controlled Light Exposure Microscopy

Telomeres are the complex end structures that confer functional integrity and positional stability to human chromosomes. Telomere research has long been dominated by length measurements and biochemical analyses. Recently, interest has shifted towards the role of their three‐dimensional organization and dynamics within the nuclear volume. In the mammalian interphase nucleus, there is increasing evidence for a telomeric configuration that is non‐random and is cell cycle and cell type dependent. This has functional implications for genome stability. Objective and reproducible representation of the spatiotemporal organization of telomeres, under different experimental conditions, requires quantification by reliable automated image processing techniques. In this paper, we describe methods for quantitative telomere analysis in cell nuclei of living human cells expressing telomere‐binding fusion proteins. We present a toolbox for determining telomere positions within the nucleus with subresolution accuracy and tracking telomeres in 4D controlled light exposure microscopy (CLEM) recordings. The use of CLEM allowed for durable imaging and thereby improved segmentation performance considerably. With minor modifications, the underlying algorithms can be expanded to the analysis of other intranuclear features, such as nuclear bodies or DNA double stranded break foci.     

Segmentation of intestinal gland images with iterative region growing

A region growing algorithm for segmentation of human intestinal gland images is presented. The initial seeding regions are identified based on the large vacant regions (lumen) inside the intestinal glands by fitting with a very large moving window. The seeding regions are then expanded by repetitive application of a morphological dilate operation with a much smaller round window structure set. False gland regions (nongland regions initially misclassified as gland regions) are removed based on either their excessive ages of active growth or inadequate thickness of dams formed by the strings of goblet cell nuclei sitting immediately outside the grown regions. The goblet cell nuclei are then identified and retained in the image. The gland contours are detected by applying a large moving round window fitting to the enormous empty exterior of the goblet cell nucleus chains in the image. The assumptions based on real intestinal gland images include the closed chain structured goblet cell nuclei that sit side-by-side with only small gaps between the neighbouring nuclei and that the lumens enclosed by the goblet cell nucleus chains are most vacant with only occasional run-away nuclei. The method performs well for most normal and abnormal intestinal gland images although it is less applicable to cancer cases. The experimental results show that the segmentations of the real microscopic intestinal gland images are satisfactorily accurate based on the visual evaluations.     

A method for the evaluation of thousands of automated 3D stem cell segmentations

There is no segmentation method that performs perfectly with any dataset in comparison to human segmentation. Evaluation procedures for segmentation algorithms become critical for their selection. The problems associated with segmentation performance evaluations and visual verification of segmentation results are exaggerated when dealing with thousands of three‐dimensional (3D) image volumes because of the amount of computation and manual inputs needed. We address the problem of evaluating 3D segmentation performance when segmentation is applied to thousands of confocal microscopy images (z‐stacks). Our approach is to incorporate experimental imaging and geometrical criteria, and map them into computationally efficient segmentation algorithms that can be applied to a very large number of z‐stacks. This is an alternative approach to considering existing segmentation methods and evaluating most state‐of‐the‐art algorithms. We designed a methodology for 3D segmentation performance characterization that consists of design, evaluation and verification steps. The characterization integrates manual inputs from projected surrogate ‘ground truth’ of statistically representative samples and from visual inspection into the evaluation. The novelty of the methodology lies in (1) designing candidate segmentation algorithms by mapping imaging and geometrical criteria into algorithmic steps, and constructing plausible segmentation algorithms with respect to the order of algorithmic steps and their parameters, (2) evaluating segmentation accuracy using samples drawn from probability distribution estimates of candidate segmentations and (3) minimizing human labour needed to create surrogate ‘truth’ by approximating z‐stack segmentations with 2D contours from three orthogonal z‐stack projections and by developing visual verification tools. We demonstrate the methodology by applying it to a dataset of 1253 mesenchymal stem cells. The cells reside on 10 different types of biomaterial scaffolds, and are stained for actin and nucleus yielding 128 460 image frames (on average, 125 cells/scaffold × 10 scaffold types × 2 stains × 51 frames/cell). After constructing and evaluating six candidates of 3D segmentation algorithms, the most accurate 3D segmentation algorithm achieved an average precision of 0.82 and an accuracy of 0.84 as measured by the Dice similarity index where values greater than 0.7 indicate a good spatial overlap. A probability of segmentation success was 0.85 based on visual verification, and a computation time was 42.3 h to process all z‐stacks. While the most accurate segmentation technique was 4.2 times slower than the second most accurate algorithm, it consumed on average 9.65 times less memory per z‐stack segmentation.     

Automated recognition of atypical nuclei in breast cancer cytology specimens by iterative image transformations

In order to develop an objective grading system for nuclear atypia in breast cancer, an image analysis technique has been applied for the automated recognition of enlarged and hyperchromatic nuclei in cytology specimens. The image segmentation algorithm, based on the ‘top hat’ image transformation developed in mathematical morphology, is implemented on the LEYTAS automated microscope system. The performance of the segmentation algorithm has been evaluated for fifty malignant and eighty-five benign breast lesions by visual inspection of the displayed ‘flagged’ objects. The mean number of flagged objects per 1600 image fields for breast cancers was 887 (range 0–7920) of which 87% consisted of single, atypical nuclei. For benign lesions the mean number was 30 (range 0–307) of which 20% were single nuclei. By adaptation of the ‘top hat’ parameter values, a more extreme subpopulation of atypical nuclei could be discriminated. The large interspecimen variation in the breast cancer results was related to differences in DNA content distribution and mean nuclear area, determined independently with scanning cytophotometry, and to some extent with the histological type.     

A guide to analysis and reconstruction of serial block face scanning electron microscopy data

Serial block face scanning electron microscopy (SBF‐SEM) is a relatively new technique that allows the acquisition of serially sectioned, imaged and digitally aligned ultrastructural data. There is a wealth of information that can be obtained from the resulting image stacks but this presents a new challenge for researchers – how to computationally analyse and make best use of the large datasets produced. One approach is to reconstruct structures and features of interest in 3D. However, the software programmes can appear overwhelming, time‐consuming and not intuitive for those new to image analysis. There are a limited number of published articles that provide sufficient detail on how to do this type of reconstruction. Therefore, the aim of this paper is to provide a detailed step‐by‐step protocol, accompanied by tutorial videos, for several types of analysis programmes that can be used on raw SBF‐SEM data, although there are more options available than can be covered here. To showcase the programmes, datasets of skeletal muscle from foetal and adult guinea pigs are initially used with procedures subsequently applied to guinea pig cardiac tissue and locust brain. The tissue is processed using the heavy metal protocol developed specifically for SBF‐SEM. Trimmed resin blocks are placed into a Zeiss Sigma SEM incorporating the Gatan 3View and the resulting image stacks are analysed in three different programmes, Fiji, Amira and MIB, using a range of tools available for segmentation. The results from the image analysis comparison show that the analysis tools are often more suited to a particular type of structure. For example, larger structures, such as nuclei and cells, can be segmented using interpolation, which speeds up analysis; single contrast structures, such as the nucleolus, can be segmented using the contrast‐based thresholding tools. Knowing the nature of the tissue and its specific structures (complexity, contrast, if there are distinct membranes, size) will help to determine the best method for reconstruction and thus maximize informative output from valuable tissue.     

多尺度区域生长与去粘连模型的乳腺细胞分割

乳腺癌已经成为女性最常见的恶性肿瘤,组织切片显微图像的病理分析是诊断的主要手段,细胞的准确分割是病理分析的重要环节。该文提出了一种新的乳腺细胞显微图像的自动分割算法:首先结合小波分解和多尺度区域生长算法分离细胞和背景,实现对细胞的精确定位;然后采用改进的数学形态学对粘连细胞进行一次细分割;接着再采用基于曲率尺度空间(CSS)的角点检测分割算法对粘连细胞进行二次细分割;两次细分割方法构成了一个双策略去粘连模型,保证了去粘连的准确性和鲁棒性。将算法应用到22幅乳腺细胞显微图像上,可以对不同类型的乳腺细胞图像进行全自动分割,有较高的分割灵敏度(0.944±0.024)和特异度(0.937±0.038),且具有较好的普适性。     

Bayesian model for detection and classification of meningioma nuclei in microscopic images

Image segmentation aims to determine structures of interest inside a digital picture in biomedical sciences. State‐of‐the art automatic methods however still fail to provide the segmentation quality achievable by humans who employ expert knowledge and use software to mark target structures on an image. Manual segmentation is time‐consuming, tedious and suffers from interoperator variability, thus not serving the requirements of daily use well. Therefore, the approach presented here abandons the goal of full‐fledged segmentation and settles for the localization of circular objects in photographs (10 training images and 20 testing images with several hundreds of nuclei each). A fully trainable softcore interaction point process model was hence fit to the most likely locations of nuclei of meningioma cells. The Broad Bioimage Benchmark Collection/SIMCEP data set of virtual cells served as controls. A ‘colour deconvolution’ algorithm was integrated to determine (based on anti‐Ki67 immunohistochemistry) which real cells might have the potential to proliferate. In addition, a density parameter of the underlying Bayesian model was estimated. Immunohistochemistry results were ‘simulated'for the virtual cells. The system yielded true positive (TP) rates in the detection and classification of real nuclei and their virtual counterparts. These hits outnumbered those obtained from the public domain image processing software ImageJ by 10%. The method introduced here can be trained to function not only in medicine and morphology‐based systems biology but in other application domains as well. The algorithm lends itself to an automated approach that constitutes a valuable tool which is easy to use and generates acceptable results quickly.     

Segmentation of touching cell nuclei using gradient flow tracking

Reliable cell nuclei segmentation is an important yet unresolved problem in biological imaging studies. This paper presents a novel computerized method for robust cell nuclei segmentation based on gradient flow tracking. This method is composed of three key steps: (1) generate a diffused gradient vector flow field; (2) perform a gradient flow tracking procedure to attract points to the basin of a sink; and (3) separate the image into small regions, each containing one nucleus and nearby peripheral background, and perform local adaptive thresholding in each small region to extract the cell nucleus from the background. To show the generality of the proposed method, we report the validation and experimental results using microscopic image data sets from three research labs, with both over-segmentation and under-segmentation rates below 3%. In particular, this method is able to segment closely juxtaposed or clustered cell nuclei, with high sensitivity and specificity in different situations.     

An approach based on two-dimensional graph theory for structural cluster detection and its histopathological application

An approach based on graph theory is described for detecting clusters of cells in tissue specimens (two-dimensional space). With a set of discrete basic elements (cell nuclei) having several measurable features (area, surface, main and minor axis of best-fitting ellipses) a graph is defined as having attributes associated with edges. Different minimum spanning trees (MSTs) can be constructed using different weight functions on the attributes (attributed MST). Analysis of the MST and of an attributed MST by use of a decomposition function allows detection of image areas with similar local properties. These clusters, which are then clusters of the tree, describe, for example, partial growth in different directions in a case of a human fibrosarcoma assuming that tumour cell nuclei are homogeneous with respect to their configuration and size. The model allows the separation of clusters of tumour cells growing in different directions and the approximation of the different growth angles. This decomposition also allows us to create new (higher) orders of structure (cluster tree).     

一种改进的基于小波去噪和区域生长的彩色图像分割算法

彩色图像分割是彩色图像处理中的重要问题。传统的彩色图像分割都是基于灰度分割算法,而忽略了彩色的空间域视觉效果及噪声污染问题。文章提出一种新的基于小波去噪和种子区域生长的一种改进方法：首先,应用小波去噪技术,强化图像边缘特征,抑制噪声,提高原始图像的信噪比;其次,将RGB彩色图像转化到HIS空间进行边缘检测,对图像进行抖动处理以减少彩色图像中的颜色数目,然后对不同分量进行序列阀值分割;最后对分割结果再进行一种新的基于区域生长的颜色相似性的聚合。仿真结果表明该算法更加符合人眼的视觉特性。     

Performance evaluation of image segmentation algorithms on microscopic image data

In our paper, we present a performance evaluation of image segmentation algorithms on microscopic image data. In spite of the existence of many algorithms for image data partitioning, there is no universal and ‘the best’ method yet. Moreover, images of microscopic samples can be of various character and quality which can negatively influence the performance of image segmentation algorithms. Thus, the issue of selecting suitable method for a given set of image data is of big interest. We carried out a large number of experiments with a variety of segmentation methods to evaluate the behaviour of individual approaches on the testing set of microscopic images (cross‐section images taken in three different modalities from the field of art restoration). The segmentation results were assessed by several indices used for measuring the output quality of image segmentation algorithms. In the end, the benefit of segmentation combination approach is studied and applicability of achieved results on another representatives of microscopic data category – biological samples – is shown.     

基于快速递推模糊2-划分熵图割的红外图像分割

考虑现有图割算法没有充分考虑红外图像的模糊特性,分割精度和运行效率低的缺点,提出了基于快速递推模糊2-划分熵图割的红外图像分割算法以实现复杂背景下红外图像的自动高效分割。该方法利用图像感兴趣区域的最大模糊熵信息设计图割能量函数的似然能,基于局部最大模糊2-划分熵值迭代检测出包含图像最大信息的感兴趣区域来确保提取目标信息的完整性。为了提高最大模糊熵寻优的效率,引入时间复杂度为O(n2)的递推算法,将模糊熵计算转化为递推过程,并保存所有递推的熵函数值用于后续的穷举寻优。针对确定的感兴趣区域,利用该区域最大模糊2-划分时隶属度函数分布设置图割能量函数的似然能,从而充分考虑图像的模糊特性。对分割结果与几种常用的算法进行了视觉比较及运行时间,错分率,F指标的量化分析。结果表明:该算法分割精度F值高达95%,运行时间较其他常用算法至少缩短了72%,基本满足自动红外图像分割对精度、效率和鲁棒性的要求。     

Method for the study of the three-dimensional orientation of the nuclei of myocardial cells in fetal human heart by means of confocal scanning laser microscopy

A series of three-dimensional image analysis tools are used to measure the three-dimensional orientation of nuclei of myocardial cells. Confocal scanning laser microscopy makes it possible to acquire series of sections up to 100 μm inside thick tissue sections. A mean orientation vector of unit length is calculated for each segmented nucleus. The global orientation statistics are obtained by calculating the vectorial sum of the nuclear unit vectors. The final orientation is expressed by a mean azimuth angle, an elevation angle and a measure of the angular homogeneity. The method is illustrated for two different regions of the myocardium (interventricular septum and papillary muscle) of a normal human fetal heart. This quantitative method will be used to assess and calibrate the information provided by polarized light microscopy.     

Automated techniques for blood vessels segmentation through fundus retinal images: A review

Retina is the interior part of human's eye, has a vital role in vision. The digital image captured by fundus camera is very useful to analyze the abnormalities in retina especially in retinal blood vessels. To get information of blood vessels through fundus retinal image, a precise and accurate vessels segmentation image is required. This segmented blood vessel image is most beneficial to detect retinal diseases. Many automated techniques are widely used for retinal vessels segmentation which is a primary element of computerized diagnostic systems for retinal diseases. The automatic vessels segmentation may lead to more challenging task in the presence of lesions and abnormalities. This paper briefly describes the various publicly available retinal image databases and various machine learning techniques. State of the art exhibited that researchers have proposed several vessel segmentation methods based on supervised and supervised techniques and evaluated their results mostly on publicly datasets such as digital retinal images for vessel extraction and structured analysis of the retina. A comprehensive review of existing supervised and unsupervised vessel segmentation techniques or algorithms is presented which describes the philosophy of each algorithm. This review will be useful for readers in their future research.     

结合粒子群优化和综合评价的脉冲耦合神经网络图像自动分割

为了解决脉冲耦合神经网络(Pulse Coupled Neural Network,PCNN)在图像分割中多参数设定以及评价准则单一的问题,提出了一种结合粒子群优化算法(Particle Swarm Optimization,PSO)和综合评价准则的PCNN图像自动分割方法。采用单调递增阈值搜索策略的PCNN改进模型,将PSO优化原理与由交叉熵参数,边缘匹配度和噪点控制度共同构成的综合评价相结合,以综合评价作为粒子的适应度函数,自动寻优获取PCNN图像分割模型的目标时间常数,连接系数以及迭代次数n,从而实现全参数自适应的PCNN图像分割。实验结果表明算法在保证PCNN运行效率下对不同类型图像都能进行正确完整的分割并兼顾纹理细节的保留。从实验数据可以看到,本文算法在综合评价和通用综合指标上均优于其他对比算法,综合评价平均优于其他算法10.5%。客观评价结果与视觉主观评价相一致,分割较理想,算法具有较高的鲁棒性。     

A framework for automatic segmentation in three dimensions of microstructural tomography data

Routine use of quantitative three dimensional analysis of material microstructure by in particular, focused ion beam (FIB) serial sectioning is generally restricted by the time consuming task of manually delineating structures within each image slice or the quality of manual and automatic segmentation schemes. We present here a framework for performing automatic segmentation of complex microstructures using a level set method. The technique is based on numerical approximations to partial differential equations to evolve a 3D surface to capture the phase boundaries. Vector fields derived from the experimentally acquired data are used as the driving forces. The framework performs the segmentation in 3D rather than on a slice by slice basis. It naturally supplies sub-voxel precision of segmented surfaces and allows constraints on the surface curvature to enforce a smooth surface in the segmentation. Two applications of the framework are illustrated using solid oxide cell materials as examples.