Computer-aided cytological cancer diagnosis: cell type classification as a step towards fully automatic cancer diagnostics on cytopathological specimens of serous effusions

Timna E. Schneider; André A. Bell; Dietrich Meyer-Ebrecht; Alfred Böcking; Til Aach

doi:10.1117/12.710355

29 March 2007 Computer-aided cytological cancer diagnosis: cell type classification as a step towards fully automatic cancer diagnostics on cytopathological specimens of serous effusions

Timna E. Schneider, André A. Bell, Dietrich Meyer-Ebrecht, Alfred Böcking, Til Aach

Author Affiliations +

Proceedings Volume 6514, Medical Imaging 2007: Computer-Aided Diagnosis; 65140G (2007) https://doi.org/10.1117/12.710355
Event: Medical Imaging, 2007, San Diego, CA, United States

Abstract

Compared to histopathological methods cancer can be detected earlier, specimens can be obtained easier and with less discomfort for the patient by cytopathological methods. Their downside is the time needed by an expert to find and select the cells to be analyzed on a specimen. To increase the use of cytopathological diagnostics, the cytopathologist has to be supported in this task. DNA image cytometry (DNA-ICM) is one important cytopathological method that measures the DNA content of cells based on the absorption of light within Feulgen stained cells. The decision whether or not the patient has cancer is based on the histogram of the DNA values. To support the cytopathologist it is desirable to replace manual screening of the specimens by an automatic selection of relevant cells for DNA-ICM. This includes automated acquisition and segmentation of focused cells, a recognition of cell types, and a selection of cells to be measured. As a step towards automated cell type detection we show the discrimination of cell types in serous effusions on a selection of about 3, 100 manually classified cells. We present a set of 112 features and the results of feature selection with ranking and a floating-search method combined with different objective functions. The validation of the best feature sets with a k-nearest neighbor and a fuzzy k-nearest neighbor classifier on a disjoint set of cells resulted in classification rates of 96% for lymphocytes and 96.8% for the diagnostically relevant cells (mesothelial+ cells), which includes benign and malign mesothelial cells and metastatic cancer cells.

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Timna E. Schneider, André A. Bell, Dietrich Meyer-Ebrecht, Alfred Böcking, and Til Aach "Computer-aided cytological cancer diagnosis: cell type classification as a step towards fully automatic cancer diagnostics on cytopathological specimens of serous effusions", Proc. SPIE 6514, Medical Imaging 2007: Computer-Aided Diagnosis, 65140G (29 March 2007); https://doi.org/10.1117/12.710355

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