Magnetic resonance imaging is the method of choice for non-invasive detection and evaluation of tumors of the central nervous system. However, discrimination of tumor boundaries from normal tissue, and the evaluation of heterogeneous lesions have proven to be limitations in traditional magnetic resonance imaging. The use of post-image acquisition processing techniques, such as multispectral tissue segmentation analysis, may provide more accurate clinical information. In this report, we have employed an experimental animal model for brain tumors induced by glial cells transformed by the human neurotropic JC virus to examine the utility of multispectral tissue segmentation for tumor cell identification. Six individual tissue types were discriminated by segmentation analysis, including heterogeneous tumor tissue, a clear demarcation of the boundary between tumor and non-tumor tissue, deep and cortical gray matter, and cerebrospinal fluid. Furthermore, the segmentation analysis was confirmed by histopathological evaluation. The use of multispectral tissue segmentation analysis may optimize the non-invasive determination and volumetric analysis of CNS neoplasms, thus providing improved clinical evaluation of tumor growth and evaluation of the effectiveness of therapeutic treatments.
- Tissue segmentation
ASJC Scopus subject areas
- Biomedical Engineering
- Radiology Nuclear Medicine and imaging