Surgical microscopes are becoming more advanced all the time, and while there is great promise in their use, the problem still remains that the diffuse view seen by the surgeon does not allow analysis of the microscopic morphology of the tissue reliably. Many studies have looked at possible inclusion of color filtering,1,2
or spectroscopic channel analysis5
in order to try and enhance the contrast at the margin between confirmed tumor and surrounding normal tissues. The creation of a tool that provides better delineation of the tissue at the microscopic level, with real-time viewing of the signal, in situ
, would be a great asset, and therefore, it could gain clinical adoption readily. In this study, one approach to imaging tissue scatter spectra is used6
in conjunction with an automated classification approach to imaging.
Scatter analysis of cells and tissues accomplished by angle-resolved or coherence-based methods has proved successful in the quantification the subcellular origin of certain features of tissue.7-12
The measurement can be robust, and changes in scatter spectra are related to pathologic structures that occur in the tissue; and thus, measurement of this could provide a unique tool for guiding surgical resection if a way was developed to help the surgeon in data reduction and display in real time.
A raster-scanning confocal reflectance imaging system to directly quantify tissue scatter in situ
was previously designed and tested in tumor tissues.6
In addition, an attempt to establish a correlation between scatter changes and tissue morphologies was performed. The main conclusions of this study were that changes are subtle and the data are multiparametric. Therefore, an automated methodology to classify the encountered scatter changes according to their tissue subtypes is required before proceeding to clinical studies. An automated interpretation into what the signal means relative to the pathology has been designed here.
The analysis was done in stages, with the first aim being the development of a methodology able to perform accurate tumor versus normal tissue discrimination, allowing reliable margin detection. However, the quantification of the scattering coefficient heterogeneity within tumor is also critical to treatment planning, because tumors can be extremely heterogeneous in terms of their fibrocystic and necrotic changes. Thus, the study here was done in these two stages and the automated identification process is described in detail for each.