Tissue Artifact Segmentation and Severity Assessment for Automatic Analysis Using WSI

Traditionally, pathological analysis and diagnosis are performed by manually eyeballing glass-slide specimen under a microscope by an expert. Whole slide image (WSI) is the digital specimen produced from the glass-slide. WSI enabled specimen to be observed on a computer-screen and led to computational pathology where computer-vision and artificial intelligence are utilized for automated analysis and diagnosis. With the current computational advancement, entire WSI can be analyzed autonomously without human supervision. However, the analysis could fail or lead to wrong diagnosis if the WSI is affected by tissue artifacts such as tissue fold or air bubble depending on the severity. Existing artifact detection methods rely on experts for severity assessment to eliminate artifact-affected regions from analysis. This process is time-consuming, exhausting…