Mathematical morphologic segmentation dedicated to quantitative immunohistochemistry

OBJECTIVE: To develop automatic segmentation sequences for fully automated quantitative immunohistochemistry of cancer cell nuclei by image analysis. STUDY DESIGN: The study focused on the automated delineation of cancer cell lobules and nuclei, taking breast carcinoma as an example. A hierarchic se gmentation was developed, employing mainly the chaining of mathematical mor phology operators. The proposed sequence was tested on 22 images of various situations, collected from 18 different cases of breast carcinoma. A quali ty control procedure was applied, comparing the automated method with manua l outlining of cancer cell foci and with manual pricking of cancer cell nuc lei. RESULTS: Good concordance was found between automated and manual segme ntation procedures (90% for cancer cell clumps, 97% for cancer cell nuclei on average), but the rate of false positive nuclei (small regions labeled a s nuclei by the segmentation procedure) could be relatively high (11% on av erage, with a maximum of 35%) and can result in underestimation of the immu nostaining ratio. CONCLUSION. This study examined a preliminary approach to automated immunoquantification, limited to automated segmentation without any color characterization. The automated hierarchic segmentation presented here leads to good discrimination of cancer cell nuclei at the chosen magn ification.