Image Analysis
Digital slides serve as the springboard for a complete digital pathology environment in which the pathologist can view, retrieve, annotate, archive images while applying sophisticated imaging analysis protocols as aids in diagnosis.
Studies show that immunohistochemistry (IHC) or immunofluorescence scoring by human visual inspection is fraught with problems including inter-observer variability, imprecision as well as the limitation of slide scoring in an 8 hour day.
The use of automated image analysis software to quantitate stains based on color and morphology can help advance the standardization of IHC and other techniques used within pathology today.
The reproducibility that a computer assisted analysis provides allows pathologists and researchers to obtain more consistent interpretations of results. Image analysis algorithms also automate many tedious analysis tasks such as cell counting. Repetitive tasks like the analysis of assay quantification and pattern recognition is simplified by algorithms, thereby saving time for the pathologist.
Image analysis can also decrease effects of observer bias and potentially reduce intra and inter-observer variability in scoring for difficult markers such as Her2. It provides consistent output of results. Depending on study results, therapies are personalized for patients resulting in more precise, targeted treatment and disease-state monitoring.
IHC, immunofluorescence, and fluorescence in situ hybridization (FISH), are being used by pathologists to arrive at a specific diagnosis and to guide clinicians in therapeutic decisions for their patients.