Automated cell nuclear segmentation in color images of hematoxylin and eosin-stained breast biopsy

OBJECTIVE: To develop an automated, reproducible epithelial cell nuclear segmentation method to quantify cytologic features quickly and accurately from breast biopsy. STUDY DESIGN: The method, based on fuzzy c-mean clustering of the hue-band of color images and the watershed transform, was applied to 39 images from 3 histologic types (typical hyperplasia, atypical hyperplasia, and ductal carcinoma in situ [cribriform and solid]). RESULTS: The performance of the segmentation algorithm was evaluated by visually determining the percentage of badly segmented nuclei (approximately 25% for all types), the percentage of nuclei that remained in clumps (4.5-16.7%) and the percentage of missed nuclei (0.4-1.5%) for each image. CONCLUSION: The segmentation algorithm was sensitive in that a small percentage of nuclei were missed. However, the percentage of badly segmented nuclei was on the order of 25%, and the percentage of nuclei that remained in clumps was on the order of 10% of the total number of nuclei in the duct. Even so, > 600 nuclei per duct, on average, were segmented correctly; that was a sufficient number by which to calculate accurate quantitative, cytologic, morphometric measurements of epithelial cell nuclei in stained tissue sections of breast biopsy.     

Neural network-based segmentation and classification system for automated grading of histologic sections of bladder carcinoma

OBJECTIVE: To develop an image analysis system for automated nuclear segmentation and classification of histologic bladder sections employing quantitative nuclear features. STUDY DESIGN: Ninety-two cases were classified into three classes by experienced pathologists according to the WHO grading system: 18 cases as grade 1, 45 as grade 2, and 29 as grade 3. Nuclear segmentation was performed by means of an artificial neural network (ANN)-based pixel classification algorithm, and each case was represented by 36 nuclei features. Automated grading of bladder tumor histologic sections was performed by an ANN classifier implemented in a two-stage hierarchic tree. RESULTS: On average, 95% of the nuclei were correctly detected. At the first stage of the hierarchic tree, classifier performance in discriminating between cases of grade 1 and 2 and cases of grade 3 was 89%. At the second stage, 79% of grade 1 cases were correctly distinguished from grade 2 cases. CONCLUSION: The proposed image analysis system provides the means to reduce subjectivity in grading bladder tumors and may contribute to more accurate diagnosis and prognosis since it relies on nuclear features, the value of which has been confirmed.     

Automatic segmentation of cell nuclei in Feulgen-stained histological sections of prostate cancer and quantitative evaluation of segmentation results

Digital image analysis of cell nuclei is useful to obtain quantitative information for the diagnosis and prognosis of cancer. However, the lack of a reliable automatic nuclear segmentation is a limiting factor for high-throughput nuclear image analysis. We have developed a method for automatic segmentation of nuclei in Feulgen-stained histological sections of prostate cancer. A local adaptive thresholding with an object perimeter gradient verification step detected the nuclei and was combined with an active contour model that featured an optimized initialization and worked within a restricted region to improve convergence of the segmentation of each nucleus. The method was tested on 30 randomly selected image frames from three cases, comparing the results from the automatic algorithm to a manual delineation of 924 nuclei. The automatic method segmented a few more nuclei compared to the manual method, and about 73% of the manually segmented nuclei were also segmented by the automatic method. For each nucleus segmented both manually and automatically, the accuracy (i.e., agreement with manual delineation) was estimated. The mean segmentation sensitivity/specificity were 95%/96%. The results from the automatic method were not significantly different from the ground truth provided by manual segmentation. This opens the possibility for large-scale nuclear analysis based on automatic segmentation of nuclei in Feulgen-stained histological sections.     

Automatic Nuclei Segmentation in H&E Stained Breast Cancer Histopathology Images

The introduction of fast digital slide scanners that provide whole slide images has led to a revival of interest in image analysis applications in pathology. Segmentation of cells and nuclei is an important first step towards automatic analysis of digitized microscopy images. We therefore developed an automated nuclei segmentation method that works with hematoxylin and eosin (H&E) stained breast cancer histopathology images, which represent regions of whole digital slides. The procedure can be divided into four main steps: 1) pre-processing with color unmixing and morphological operators, 2) marker-controlled watershed segmentation at multiple scales and with different markers, 3) post-processing for rejection of false regions and 4) merging of the results from multiple scales. The procedure was developed on a set of 21 breast cancer cases (subset A) and tested on a separate validation set of 18 cases (subset B). The evaluation was done in terms of both detection accuracy (sensitivity and positive predictive value) and segmentation accuracy (Dice coefficient). The mean estimated sensitivity for subset A was 0.875 (±0.092) and for subset B 0.853 (±0.077). The mean estimated positive predictive value was 0.904 (±0.075) and 0.886 (±0.069) for subsets A and B, respectively. For both subsets, the distribution of the Dice coefficients had a high peak around 0.9, with the vast majority of segmentations having values larger than 0.8.     

Automatic segmentation and supervised learning-based selection of nuclei in cancer tissue images

Analysis of preferential localization of certain genes within the cell nuclei is emerging as a new technique for the diagnosis of breast cancer. Quantitation requires accurate segmentation of 100-200 cell nuclei in each tissue section to draw a statistically significant result. Thus, for large-scale analysis, manual processing is too time consuming and subjective. Fortuitously, acquired images generally contain many more nuclei than are needed for analysis. Therefore, we developed an integrated workflow that selects, following automatic segmentation, a subpopulation of accurately delineated nuclei for positioning of fluorescence in situ hybridization-labeled genes of interest. Segmentation was performed by a multistage watershed-based algorithm and screening by an artificial neural network-based pattern recognition engine. The performance of the workflow was quantified in terms of the fraction of automatically selected nuclei that were visually confirmed as well segmented and by the boundary accuracy of the well-segmented nuclei relative to a 2D dynamic programming-based reference segmentation method. Application of the method was demonstrated for discriminating normal and cancerous breast tissue sections based on the differential positioning of the HES5 gene. Automatic results agreed with manual analysis in 11 out of 14 cancers, all four normal cases, and all five noncancerous breast disease cases, thus showing the accuracy and robustness of the proposed approach. ? Published 2012 Wiley Periodicals, Inc.     

Segmentation of color images from serous cytology for automated cell classification

OBJECTIVE: To design an automated system for the classification of cells based on analysis of serous cytology, with the aim of segmenting both cytoplasm and nucleus using color information from the images as the main characteristic of the cells. STUDY DESIGN: The segmentation strategy uses color information coupled with mathematical morphology tools, such as watersheds. Cytoplasm and nuclei of all diagnostic cells are retained; erythrocytes and debris are eliminated. Special techniques are used for the separation of clustered cells. RESULTS: A large set of cells was assessed by experts to score the segmentation success rate. All cells were segmented whatever their spatial configurations. The average success rate was 92.5% for nuclei and 91.1% for cytoplasm. CONCLUSION: This color information-based segmentation of images of serous cells is accurate and provides a useful tool. This segmentation strategy will improve the automated classification of cells.     

Automatic image segmentation of nuclear stained breast tissue sections using color active contour model and an improved watershed method

Automatic image segmentation of immunohistologically stained breast tissue sections helps pathologists to discover the cancer disease earlier. The detection of the real number of cancer nuclei in the image is a very tedious and time consuming task. Segmentation of cancer nuclei, especially touching nuclei, presents many difficulties to separate them by traditional segmentation algorithms. This paper presents a new automatic scheme to perform both classification of breast stained nuclei and segmentation of touching nuclei in order to get the total number of cancer nuclei in each class. Firstly, a modified geometric active contour model is used for multiple contour detection of positive and negative nuclear staining in the microscopic image. Secondly, a touching nuclei method based on watershed algorithm and concave vertex graph is proposed to perform accurate quantification of the different stains. Finally, benign nuclei are identified by their morphological features and they are removed automatically from the segmented image for positive cancer nuclei assessment. The proposed classification and segmentation schemes are tested on two datasets of breast cancer cell images containing different level of malignancy. The experimental results show the superiority of the proposed methods when compared with other existing classification and segmentation methods. On the complete image database, the segmentation accuracy in term of cancer nuclei number is over than 97%, reaching an improvement of 3–4% over earlier methods.     

Fully Automated Fluorescent in situ Hybridization (FISH) Staining and Digital Analysis of HER2 in Breast Cancer: A Validation Study

HER2 assessment is routinely used to select patients with invasive breast cancer that might benefit from HER2-targeted therapy. The aim of this study was to validate a fully automated in situ hybridization (ISH) procedure that combines the automated Leica HER2 fluorescent ISH system for Bond with supervised automated analysis with the Visia imaging D-Sight digital imaging platform. HER2 assessment was performed on 328 formalin-fixed/paraffin-embedded invasive breast cancer tumors on tissue microarrays (TMA) and 100 (50 selected IHC 2+ and 50 random IHC scores) full-sized slides of resections/biopsies obtained for diagnostic purposes previously. For digital analysis slides were pre-screened at 20x and 100x magnification for all fluorescent signals and supervised-automated scoring was performed on at least two pictures (in total at least 20 nuclei were counted) with the D-Sight HER2 FISH analysis module by two observers independently. Results were compared to data obtained previously with the manual Abbott FISH test. The overall agreement with Abbott FISH data among TMA samples and 50 selected IHC 2+ cases was 98.8% (κ = 0.94) and 93.8% (κ = 0.88), respectively. The results of 50 additionally tested unselected IHC cases were concordant with previously obtained IHC and/or FISH data. The combination of the Leica FISH system with the D-Sight digital imaging platform is a feasible method for HER2 assessment in routine clinical practice for patients with invasive breast cancer.     

Quantitative image analysis of connective tissue progenitors

OBJECTIVE: To develop an image analysis system to automatically identify colony-forming units (CFUs) in in vitro cell cultures of connective tissue progenitors. This system was designed to quantitatively assess colony morphology and number of colonies in 4-cm(2) culture wells. STUDY DESIGN: Large field-of-view high-resolution fluorescence images of 4',6-diamidino-2-phenylindole (DAPI)- and alkaline phosphatase (AP)-stained bone marrow cell cultures were obtained using an epi-fluorescence microscope and automated scanning stage. Cell nuclei were identified in the DAPI-stained images after removal of fluorescent debris from the image. An Euclidean distance map (EDM) of the segmented cell nuclei was used to cluster cell nuclei into colonies. The automated system was evaluated using 40 tissue culture wells of bone marrow aspirate samples. The results of the automated analysis were compared to the manual tracings of colonies by 3 reviewers. RESULTS: The automated method agreed with all 3 reviewers on average 87.5% of the time. Additionally, reviewers identified other colonies not outlined by the reviewers on average 2.7 times more than the automated method. CONCLUSION: The automated method is a less biased method for identifying CFUs than individual reviewers, it provides more quantitative information about colony morphology than can be obtained manually and it is less time consuming.     

Use of automated microscopy for the detection of disseminated tumor cells in bone marrow samples

The use of automated microscopy has reached the maturity necessary for its routine use in the clinical pathology laboratory. In the following study we compared the performance of an automated microscope system (MDS) with manual method for the detection and analysis of disseminated tumor cells present in bone marrow preparations from breast carcinoma patients. The MDS System detected rare disseminated tumor cells among bone marrow mononuclear cells with higher sensitivity than standard manual microscopy. Automated microscopy also proved to be a method of high reproducibility and precision, the advantage of which was clearly illustrated by problems of variability in manual screening. Accumulated results from two pathologists who had screened 120 clinical slides from breast cancer patients both by manual microscopy and by use of the MDS System revealed only two (3.8%) missed by the automatic procedure, whereas as many as 20 out of 52 positive samples (38%) were missed by manual screening.     

Automated segmentation of routinely hematoxylin-eosin-stained microscopic images by combining support vector machine clustering and active contour models

OBJECTIVE: To develop a method for the automated segmentation of images of routinely hematoxylin-eosin (H-E)-stained microscopic sections to guarantee correct results in computer-assisted microscopy. STUDY DESIGN: Clinical material was composed 50 H-E-stained biopsies of astrocytomas and 50 H-E-stained biopsies of urinary bladder cancer. The basic idea was to use a support vector machine clustering (SVMC) algorithm to provide gross segmentation of regions holding nuclei and subsequently to refine nuclear boundary detection with active contours. The initialization coordinates of the active contour model were defined using a SVMC pixel-based classification algorithm that discriminated nuclear regions from the surrounding tissue. Starting from the boundaries of these regions, the snake fired and propagated until converging to nuclear boundaries. RESULTS: The method was validated for 2 different types of H-E-stained images. Results were evaluated by 2 histopathologists. On average, 94% of nuclei were correctly delineated. CONCLUSION: The proposed algorithm could be of value in computer-based systems for automated interpretation of microscopic images.     

3D cell nuclei segmentation based on gradient flow tracking

Background  

Reliable segmentation of cell nuclei from three dimensional (3D) microscopic images is an important task in many biological studies. We present a novel, fully automated method for the segmentation of cell nuclei from 3D microscopic images. It was designed specifically to segment nuclei in images where the nuclei are closely juxtaposed or touching each other. The segmentation approach has three stages: 1) a gradient diffusion procedure, 2) gradient flow tracking and grouping, and 3) local adaptive thresholding.     

Automated identification of diploid reference cells in cervical smears using image analysis

BACKGROUND: Acquisition of DNA ploidy histograms by image analysis may yield important information regarding the behavior of premalignant cervical lesions. Accurate selection of nuclei for DNA measurement is an important prerequisite for obtaining reliable data. Traditionally, manual selection of nuclei of diagnostic and reference cells is performed by an experienced cytotechnologist. In the present study, a method for the fully automated identification of nuclei of diploid epithelial reference cells in Feulgen- restained Papanicolaou (PAP) smears is described. METHODS: The automated procedure consists of a decision tree implemented on the measurement device, containing nodes with feature threshold values and multivariate discriminant functions. Nodes were constructed to recognize debris and inflammatory cells, as well as diploid and nondiploid epithelial cells of the uterine cervix. Evaluation of the classifier was performed by comparing resulting diploid integrated optical densities with those from manually selected reference cells. RESULTS AND CONCLUSION: On average, automatically acquired values deviated 2.4% from manually acquired values, indicating that the method described in this paper may be useful in cytometric practice.     

Automated fluorescent in situ hybridization (FISH) analysis of t(9;22)(q34;q11) in interphase nuclei.

BACKGROUND: For chronic myeloid leukemia, the FISH detection of t(9;22)(q34;q11) in interphase nuclei of peripheral leukocytes is an alternative method to bone marrow karyotyping for monitoring treatment. With automation, several drawbacks of manual analysis may be circumvented. In this article, the capabilities of a commercially available automated image acquisition and analysis system were determined by detecting t(9;22)(q34;q11) in interphase nuclei of peripheral leukocytes. METHODS: Three peripheral blood samples of normal adults, 21 samples of CML patients, and one sample of a t(9;22)(q34;q11) positive cell-line were used. RESULTS: Single nuclei with correctly detected signals amounted to 99.6% of nuclei analyzed after exclusion of overlapping nuclei and nuclei with incorrect signal detection. A cut-off value of 0.84 mum was defined to discriminate between translocation positive and negative nuclei based on the shortest distance between signals. Using this value, the false positive rate of the automated analysis for negative samples was 7.0%, whereas that of the manual analysis was 5.8%. Automated and manual results showed strong correlation (R(2) = 0.985), the mean difference of results was only 3.7%. CONCLUSIONS: A reliable and objective automated analysis of large numbers of cells is possible, avoiding interobserver variability and producing statistically more accurate results than manual evaluation.     

Confocal DNA cytometry: a contour-based segmentation algorithm for automated three-dimensional image segmentation

BACKGROUND: Confocal laser scanning microscopy (CLSM) presents the opportunity to perform three-dimensional (3D) DNA content measurements on intact cells in thick histological sections. So far, these measurements have been performed manually, which is quite time-consuming. METHODS: In this study, an intuitive contour-based segmentation algorithm for automatic 3D CLSM image cytometry of nuclei in thick histological sections is presented. To evaluate the segmentation algorithm, we measured the DNA content and volume of human liver and breast cancer nuclei in 3D CLSM images. RESULTS: A high percentage of nuclei could be segmented fully automatically (e.g., human liver, 92%). Comparison with (time-consuming) interactive measurements on the same CLSM images showed that the results were well correlated (liver, r = 1.00; breast, r = 0.92). CONCLUSIONS: Automatic 3D CLSM image cytometry enables measurement of volume and DNA content of large numbers of nuclei in thick histological sections within an acceptable time. This makes large-scale studies feasible, whereby the advantages of CLSM can be exploited fully. The intuitive modular segmentation algorithm presented in this study detects and separates overlapping objects, also in two-dimensional (2D) space. Therefore, this algorithm may also be suitable for other applications.     

PCG-Cut: Graph Driven Segmentation of the Prostate Central Gland

Prostate cancer is the most abundant cancer in men, with over 200,000 expected new cases and around 28,000 deaths in 2012 in the US alone. In this study, the segmentation results for the prostate central gland (PCG) in MR scans are presented. The aim of this research study is to apply a graph-based algorithm to automated segmentation (i.e. delineation) of organ limits for the prostate central gland. The ultimate goal is to apply automated segmentation approach to facilitate efficient MR-guided biopsy and radiation treatment planning. The automated segmentation algorithm used is graph-driven based on a spherical template. Therefore, rays are sent through the surface points of a polyhedron to sample the graph’s nodes. After graph construction – which only requires the center of the polyhedron defined by the user and located inside the prostate center gland – the minimal cost closed set on the graph is computed via a polynomial time s-t-cut, which results in the segmentation of the prostate center gland’s boundaries and volume. The algorithm has been realized as a C++ module within the medical research platform MeVisLab and the ground truth of the central gland boundaries were manually extracted by clinical experts (interventional radiologists) with several years of experience in prostate treatment. For evaluation the automated segmentations of the proposed scheme have been compared with the manual segmentations, yielding an average Dice Similarity Coefficient (DSC) of 78.94±10.85%.     

Automated topographical cell proliferation analysis

BACKGROUND: Cell proliferation is often studied using the incorporation of bromodeoxyuridine (BrdU). Immunohistochemical staining is then used to detect BrdU in the nucleus. To circumvent the observer bias and labor-intensive nature of manually counting BrdU-labeled nuclei, an automated topographical cell proliferation analysis method is developed. METHODS: Sections stained with fluorescein-labeled anti-BrdU and counterstained with To-Pro-3 are scanned using confocal laser scanning microscopy (CLSM). For every point in the image, the nucleus density of BrdU-labeled nuclei and the total nucleus density of the neighborhood of that point are calculated from the BrdU and the To-Pro-3 signal, respectively. The ratio of these densities gives an indication of the amount of cell proliferation at that point. The automated measure is validated by comparing it with the ratio of BrdU-stained nuclei to the total number of nuclei obtained from a manual count. RESULTS: A positive correlation is found between the automated measure and the ratios calculated from the manual counting (r = 0.86, P < 0.001). Calculating the topographical cell proliferation using the automated method is faster and does not suffer from interobserver variability. CONCLUSIONS: Automated topographical cell proliferation analysis is a fast method to objectively find differences in cell proliferation within a tissue. This can be visualized by a topographical map that corresponds to the tissue under study.     

An edge relocation segmentation algorithm

An automated procedure that refines the nuclear contour of a previously segmented nucleus is described. The algorithm makes use of intensity information, edge magnitude information and both object and edge connectivity information. This automated procedure generates a closed contour precisely along the edge of the nucleus. The procedure was tested on a database of 3,680 red-green-blue images of thionin-SO2 and orange II-stained cervical cells obtained from normal and dysplastic samples. When used in conjunction with a simple threshold selection algorithm and an artifact removal routine, this edge relocation algorithm resulted in the correct segmentation of over 98% of the nuclei. Only 63 (1.7%) of all nuclei were incorrectly segmented.