Cancer cell detection and invasion depth estimation in brightfield images

The study of cancer cell invasion under the effect of differentconditions is fundamental for the understanding ofthe cancer invasion mechanism and to test possible therapiesfor its regulation. To simulate invasion across tissuebasement membrane, biologists established in vitro assayswith cancer cells invading extracellular matrix components.However, analysis of such assays is manual, being timeconsumingand error-prone, which motivates an objectiveand automated analysis tool.Towards automating such analysis we present a methodologyto detect cells in 3D matrix cell assays and correctlyestimate their invasion, measured by the depth of the penetrationin the gel. Detection is based on the sliding bandfilter, by evaluating the gradient convergence and not intensity.As such it can detect low contrast cells which otherwisewould be lost. For cell depth estimation we present a focusestimator based on the convergence gradients magnitude.The final cell detections precision and recall are of 0.896and 0.910 respectively, and the average error in the cellsposition estimate is of 0.41µm, 0.37µm and 3.7µm in thex, y and z directions, respectively

3D Gray Level Co-Occurrence Matrix Based Classification of Favor Benign and Borderline Types in Follicular Neoplasm Images

Abstract Since the efficiency of treatment of thyroid disorder depends on the risk of malignancy, indeterminate follicular neoplasm (FN) images should be classified. The diagnosis process has been done by visual interpretation of experienced pathologists. However, it is difficult to separate the favor benign from borderline types. Thus, this paper presents a classification approach based on 3D nuclei model to classify favor benign and borderline types of follicular thyroid adenoma (FTA) in cytological specimens. The proposed method utilized 3D gray level co-occurrence matrix (GLCM) and random forest classifier. It was applied to 22 data sets of FN images. Furthermore, the use of 3D GLCM was compared with 2D GLCM to evaluate the classification results. From experimental results, the proposed system achieved 95.45% of the classification. The use of 3D GLCM was better than 2D GLCM according to the accuracy of classification. Consequently, the proposed method probably helps a pathologist as a prescreening tool