Applications of Image Analysis to Anatomic Pathology: Realities and Promises

Abstract

Image Analysis in Pathology is viewed as an ancillary method meant to provide objective support in the resolution of difficult problems. Its Achilles heel is the process of nuclear segmetation (delimitation of the nuclear membrane) which is extremely difficult in pathology materials. Although interactive segmentation procedures are available no reliable fully automatic method has been described. The only application of image analysis that has truly succeeded in Pathology is DNA ploidy measurement. A very desirable application is the quantitation of immunohistochemical markers, which is technically challenging, has been resolved only in certain cases and is unlikely to have a general solution. Nuclear quantitation has repeatedly proven to be helpful in reaching differential diagnoses, in particular when based on size distributions of nuclear profiles rather than its average, but is hampered by the segmentation problem discussed above. Texture analysis of chromatin is an exciting, mathematically complex application likely to succeed, for which many approaches have been described. Finally a diagnosis (classification) can be obtained based on algorithms applied to multiple descriptors of tumor cells (for instance nuclear sizes, chromatin texture, shape, etc). The best classificatory approaches are neural networks (a form of artificial intelligence), multivariate analysis, and logistic regression (statistical).

• Image analysis
• Pathology
• Morphometry
• Ploidy
• Immunohistochemistry
• Texture analysis
• Fractal analysis
• Nuclear quantitation
• Diagnostic classification algorithms