AutoIHC-Analyzer: computer-assisted microscopy for automated membrane extraction/scoring in HER2 molecular markers.

Human epidermal growth factor receptor 2 (HER2) is one of the widely used Immunohistochemical (IHC) markers for prognostic evaluation amongst the patient of breast cancer. Accurate quantification of cell membrane is essential for HER2 scoring in therapeutic decision making. In modern laboratory practice, expert pathologist visually assesses the HER2-stained tissue sample under the bright field microscope for cell membrane assessment. This manual assessment is time consuming, tedious and quite often results in interobserver variability. Further, the burden of increasing number of patients is a challenge for the pathologists. To address these challenges, there is an urgent need with a rapid HER2 cell membrane extraction method. The proposed study aims at developing an automated IHC scoring system, termed as AutoIHC-Analyzer, for automated cell membrane extraction followed by HER2 molecular expression assessment from stained tissue images. A series of image processing approaches have been used to automatically extract the stained cells and membrane region, followed by automatic assessment of complete and broken membrane. Finally, a set of features are used to automatically classify the tissue under observation for the quantitative scoring as 0/1+, 2+ and 3+. In a set of surgically extracted cases of HER2-stained tissues, obtained from collaborative hospital for the testing and validation of the proposed approach AutoIHC-Analyzer and publicly available open source ImmunoMembrane software are compared for 90 set of randomly acquired images with the scores by expert pathologist where significant correlation is observed [(r = 0.9448; p < 0.001) and (r = 0.8521; p < 0.001)] respectively. The output shows promising quantification in automated scoring. LAY DESCRIPTION: In cancer prognosis amongst the patient of breast cancer, human epidermal growth factor receptor 2 (HER2) is used as Immunohistochemical (IHC) biomarker. The correct assessment of HER2 leads to the therapeutic decision making. In regular practice, the stained tissue sample is observed under a bright microscope and the expert pathologists score the sample as negative (0/1+), equivocal (2+) and positive (3+) case. The scoring is based on the standard guidelines relating the complete and broken cell membrane as well as intensity of staining in the membrane boundary. Such evaluation is time consuming, tedious and quite often results in interobserver variability. To assist in rapid HER2 cell membrane assessment, the proposed study aims at developing an automated IHC scoring system, termed as AutoIHC-Analyzer, for automated cell membrane extraction followed by HER2 molecular expression assessment from stained tissue images. The input image is preprocessed using modified white patch and CMYK and RGB colour space were used in extracting the haematoxylin (negatively stained cells) and diaminobenzidine (DAB) stain observed in the tumour cell membrane. Segmentation and postprocessing are applied to create the masks for each of the stain channels. The membrane mask is then quantified as complete or broken using skeletonisation and morphological operations. Six set of features were assessed for the classification from a set of 180 training images. These features are: complete to broken membrane ratio, amount of stain using area of Blue and Saturation channels to the image size, DAB to haematoxylin ratio from segmented masks and average R, G and B from five largest blobs in segmented DAB-masked image. These features are then used in training the SVM classifier with Gaussian kernel using 5-fold cross-validation. The accuracy in the training sample is found to be 88.3%. The model is then used for 90 set of unknown test sample images and the final labelling of stained cells and HER2 scores (as 0/1+, 2+ and 3+) are compared with the ground truth, that is expert pathologists' score from the collaborative hospital. The test sample images were also fed to ImmunoMembrane software for a comparative assessment. The results from the proposed AutoIHC-Analyzer and ImmunoMembrane software were compared with the expert pathologists' score where significant agreement using Pearson's correlation coefficient [(r = 0.9448; p < 0.001) and (r = 0.8521; p < 0.001) respectively] is observed. The results from AutoIHC-Analyzer show promising quantitative assessment of HER2 scoring.