Quantification of epithelial volume by image processing applied to ovarian tumors

Quantifying local heterogeneity via morphologic scale: Distinguishing tumoral from stromal regions

INTRODUCTION

The notion of local scale was introduced to characterize varying levels of image detail so that localized image processing tasks could be performed while simultaneously yielding a globally optimal result. In this paper, we have presented the methodological framework for a novel locally adaptive scale definition, morphologic scale (MS), which is different from extant local scale definitions in that it attempts to characterize local heterogeneity as opposed to local homogeneity.

METHODS

At every point of interest, the MS is determined as a series of radial paths extending outward in the direction of least resistance, navigating around obstructions. Each pixel can then be directly compared to other points of interest via a rotationally invariant quantitative feature descriptor, determined by the application of Fourier descriptors to the collection of these paths.

RESULTS

OUR GOAL IS TO DISTINGUISH TUMOR AND STROMAL TISSUE CLASSES IN THE CONTEXT OF FOUR DIFFERENT DIGITIZED PATHOLOGY DATASETS: prostate tissue microarrays (TMAs) stained with hematoxylin and eosin (HE) (44 images) and TMAs stained with only hematoxylin (H) (44 images), slide mounts of ovarian H (60 images), and HE breast cancer (51 images) histology images. Classification performance over 50 cross-validation runs using a Bayesian classifier produced mean areas under the curve of 0.88 ± 0.01 (prostate HE), 0.87 ± 0.02 (prostate H), 0.88 ± 0.01 (ovarian H), and 0.80 ± 0.01 (breast HE).

CONCLUSION

For each dataset listed in Table 3, we randomly selected 100 points per image, and using the procedure described in Experiment 1, we attempted to separate them as belonging to stroma or epithelium.

Segmentation of epithelium in H&E stained odontogenic cysts

An algorithm for the automated segmentation of epithelial tissue in digital images of histologic tissue sections of odontogenic cysts (cysts originating from residual odontogenic epithelium) is presented. The algorithm features an image standardization process that greatly reduces variation in luminance and chrominance between images due to variations in sample preparation. Segmentation of the epithelial regions of images uses an algorithm based on binary graph cuts where graph weights depend on probabilities obtained from colour histogram models of epithelium and stroma image regions. Algorithm training used a data set of 38 images of four types of odontogenic cyst and was tested using a separate data set of 35 images of the same four cyst types. The best parameters for the segmentation algorithm were determined using a response-surface optimizer. The best parameter set resulted in an overall mean (± std. dev.) sensitivity of 91.5 ± 17% and overall mean specificity of 85.1 ± 18.6% on the training set. Particularly good results were obtained for dentigerous and odontogenic keratocysts for which the mean sensitivities/specificities were 91.9 ± 6.15%/97.4 ± 2.15% and 96.1 ± 1.98%/98.7 ± 3.16%, respectively. Our method is potentially applicable to many pathological conditions in similar tissues, such as skin and mucous membranes where there is a clear microscopic distinction between epithelium and connective tissues.

An evaluation of prognostic factors in advanced ovarian cancer

A summary is presented of currently available prognostic factors in advanced ovarian cancer of the common epithelial types. The emphasis is on the most promising clinical, classical pathological, biochemical, immunohistochemical, molecular biological and quantitative pathological factors.

Method for counting mitoses by image processing in Feulgen stained breast cancer sections

This study describes an image processing method for the assessment of the mitotic count in Feulgen-stained breast cancer sections. The segmentation procedure was optimized to eliminate 95-98% of the nonmitoses, whereas 11% of the mitoses did not survive the segmentation procedure. Contour features and optical density measurements of the remaining objects were computed to allow for classification. Twelve specimens were analyzed, nine used to serve as a training set, and three put aside for later use as independent test set. The fully automatic image processing method correctly classified 81% of the mitoses at the specimen level while inserting 30% false positives. The automatic procedure strongly correlated with the interactive counting procedure (r = 0.98). Although the fully automatic method provided satisfactory results, it is not yet suited for clinical practice. The automated method with an interactive evaluation step gave an accurate reflection of the mitotic count showing an almost perfect correlation with the results of the interactive morphometry (r = 0.998). Therefore this semiautomated method may be useful as prescreening device.

Automated estimation of epithelial volume in breast cancer sections. A comparison with the image processing steps applied to gynecologic tumors

The paper describes an image analysis technique for automated estimation of the epithelial percentage in standard paraffin tissue sections of invasive ductal breast cancers. Two staining procedures are evaluated: Feulgen (pararosanilin) and CAM 5.2-demonstrating the presence of cytokeratin 8 and 18-, both counterstained with naphthol yellow. In the technique, one image is recorded with a filter to visualize where the epithelium lies. This filter is chosen corresponding to the type of staining: it is yellow for Feulgen and blue for anti-cytokeratin CAM 5.2. To visualize where the stroma lies, the same image can be used for anti-cytokeratin CAM 5.2, whereas for Feulgen, a second image has to be recorded from the same microscope field with a blue filter. The image processing steps to determine the total tissue area comprise correction for shading, segmentation of the tissue area, and restoration of the segmented image by removal of small artefacts and closure of small tears in the tissue. The method for determination of the epithelial area consists of the following steps: correction for shading, gaussian blurring, segmentation of nuclei or epithelial cells, and editing of the segmented image by removal of small objects and closure of small spaces between the epithelial nuclei or cells. These image processing steps are compared to those for quantification of the epithelial percentage in gynecologic tumors of epithelial origin. For the Feulgen stain, the method is evaluated on 30 breast cancers of the ductal type (4 grade I, 12 grade II, and 14 grade III).(ABSTRACT TRUNCATED AT 250 WORDS)

Quantification of epithelial area by image processing applied to endometrial carcinomas: a comparison with ovarian tumors

In endometrial carcinomas, the epithelial area measured by interactive morphometry is an important feature in the classification of tumors of varying histologic grades. This report describes an image analysis technique for the fully automated estimation of the area percentages of epithelium and stroma in tissue sections of the endometrium obtained from hysterectomy specimens. The method is evaluated using endometrial carcinomas with varying degrees of malignancy. From standard paraffin sections stained with pararosanilin Feulgen and naphthol yellow, a blue-yellow image pair was recorded. The blue image was used to determine the total tissue area and the yellow image was used to determine the epithelial area. Image processing of the blue image was comprised of correction for shading, segmentation of the tissue area, and restoration of the segmented image by removing small artefacts and closing small tears in the tissue. Image processing of the yellow image was based on the fact that epithelial nuclei are generally more tightly packed than stromal nuclei and consists of the following steps: correction for shading, gaussian blurring, segmentation of nuclei, and editing the segmented image by removing small objects and closing small spaces between the epithelial nuclei. These image processing steps are compared with those used for quantification of the epithelial area in ovarian tumors. The performance of the method was evaluated using 120 image pairs from 30 endometrial carcinomas of varying histologic grades. The epithelial area percentages, as assessed by digital image processing, strongly correlate to control percentages that were established by interactive morphometry (r = .987).

Flow cytometric analysis of DNA ploidy pattern from deparaffinized formalin-fixed gastric cancer tissue

Histologically processed tissue from gastric cancers has been analyzed by flow cytometry in an attempt to correlate DNA ploidy pattern and behavior of the tumor. Of the mucosal and submucosal cancers (so-called early, all stage I in the present series), 62.7% show a diploid DNA pattern and 37.3% show a single aneuploid pattern. Of the deeply infiltrating (beyond the submucosa) cancers (stage II and III), 52.1% are single aneuploid and 47.9% are multiploid. While stage-I patients are all alive at the end of the follow-up period (6 years), in stage II and III cases Cox's regression model shows that the hazard function depends on DNA pattern: survival is negatively influenced by multiploidy. On this basis, it may be assumed that the DNA pattern is a useful prognostic indicator of gastric cancer. As expected, in Cox's regression model an even more important negative correlation exists between survival and stage: single aneuploid cases in stage II have a better prognosis than those in stage III. Instead, no correlation is found between histological cancer subtype (Laurén and WHO classifications), grade and DNA pattern.

The prognostic value of morphometrical features and cellular DNA content in cis-platin treated late ovarian cancer patients

In 73 CAP-1 treated stage III and IV ovarian cancers, the prognostic significance of morphometric features and cellular DNA content has been evaluated in comparison with histologic type, grade of differentiation and a number of clinical characteristics. Borderline tumours were excluded from the study. Median follow-up was 44 months, median survival time 36 months. Single features associated with prognosis were (in order of decreasing significance according to single variate analysis): FIGO stage (P = 0.0002), bulky disease (P = 0.004), standard deviation and mean of nuclear area (P = 0.0006 and P = 0.01), cellular DNA content (P = 0.01), mitotic activity index (P = 0.08) and volume percentage epithelium (P = 0.13, not quite significant). Tumours with a mean nuclear area greater than 70 micron2 (which occurred in 35% of the cases) were nearly all aneuploid. Multivariate analysis showed that the statistically most significant prognostic combination of features consisted of mean nuclear area, presence or absence of bulky disease and FIGO stage (in order of decreasing importance) (Mantel-Cox = 23.07, P less than 0.00001). A low value for the multivariate function of this combination of features was associated with a poor prognosis within 24 months, a high value with a favourable outcome. Another favourable combination of features appeared to be diploid cellular DNA content and a low mitotic activity index (11 patients, one died). However, even with the prognostically most favourable combination of these features, several patients died. Of all combinations of features investigated, only two were associated with an excellent prognosis (low mitotic activity index and low volume percentage epithelium). Cancers of 7 patients (10%) displayed such features, and none of them died during the follow-up period (minimally 20 and maximally 54 months). It is concluded that morphometric and flow cytometric analysis can provide significant and objective information to predict the prognosis of cis-platin-treated advanced ovarian cancer patients.