Quantitative validation of anti‐ PTBP1 antibody for diagnostic neuropathology use: Image analysis approach

Traditional diagnostic neuropathology relies on subjective interpretation of visual data obtained from a brightfield microscopy. This approach causes high variability, unsatisfactory reproducibility, and inability for multiplexing even among experts. These problems may affect patient outcomes and confound clinical decision‐making. Also, standard histological processing of pathological specimens leads to auto‐fluorescence and other artifacts, a reason why fluorescent microscopy is not routinely implemented in diagnostic pathology. To overcome these problems, objective and quantitative methods are required to help neuropathologists in their clinical decision‐making. Therefore, we propose a computerized image analysis method to validate anti‐PTBP1 antibody for its potential use in diagnostic neuropathology. Images were obtained from standard neuropathological specimens stained with anti‐PTBP1 antibody. First, the noise characteristics of the images were modeled and images are de‐noised according to the noise model. Next, images are filtered with sigma‐adaptive Gaussian filtering for normalization, and cell nuclei are detected and segmented with a k‐means–based deterministic approach. Experiments on 29 data sets from 3 cases of brain tumor and reactive gliosis show statistically significant differences between the number of positively stained nuclei in images stained with and without anti‐PTBP1 antibody. The experimental analysis of specimens from 3 different brain tumor groups and 1 reactive gliosis group indicates the feasibility of using anti‐PTBP1 antibody in diagnostic neuropathology, and computerized image analysis provides a systematic and quantitative approach to explore feasibility.