Keywords
Membrane Segmentation, HER2 Assessment, Breast Cancer, Gastric Cancer Digital Pathology, ASCO/CAP
Methods
Automatic HER2 scoring is the ability to give consistent results on similar slides in a short time compared with the manual scoring performed by pathologists. Virasoft HER2 Analyzer is developed based on preprocessing, thresholding and segmentation techniques to score the whole slide images. The technique is comprised of three steps. In the first step, a superpixel-based support vector machine (SVM) feature learning classifier is proposed to classify epithelial and stromal regions from WSI. In the second stage, on classified epithelial regions, a convolutional neural network (CNN) based segmentation method is applied to segment membrane regions. Finally, divided tiles are merged and the overall score of each slide is evaluated.
References
[1] Loibl, S., & Gianni, L. (2017). HER2-positive breast cancer. The Lancet, 389(10087), 2415–2429
[2]Wolff, A. C., Hammond, M. E. H., Hicks, D. G., Dowsett, M., McShane, L. M., Allison, K. H., ... & Hanna, W. (2014). Recommendations for human epidermal growth factor receptor 2 testing in breast cancer: American Society of Clinical Oncology/College of American Pathologists clinical practice guideline update. Archives of Pathology and Laboratory Medicine, 138(2), 241-256.