Ink Marker Segmentation in Histopathology Images Using Deep Learning
This addresses a specific issue in digital pathology for researchers and pathologists by enabling better processing of ink-marked slides, though it is incremental as it applies existing deep learning methods to a new domain-specific task.
The study tackled the problem of ink marker segmentation in histopathology images, which can degrade image quality and confuse computer methods, by proposing a deep learning approach that achieved an F1 score of 94.53% using an FPN model with EfficientNet-B3 backbone.
Due to the recent advancements in machine vision, digital pathology has gained significant attention. Histopathology images are distinctly rich in visual information. The tissue glass slide images are utilized for disease diagnosis. Researchers study many methods to process histopathology images and facilitate fast and reliable diagnosis; therefore, the availability of high-quality slides becomes paramount. The quality of the images can be negatively affected when the glass slides are ink-marked by pathologists to delineate regions of interest. As an example, in one of the largest public histopathology datasets, The Cancer Genome Atlas (TCGA), approximately $12\%$ of the digitized slides are affected by manual delineations through ink markings. To process these open-access slide images and other repositories for the design and validation of new methods, an algorithm to detect the marked regions of the images is essential to avoid confusing tissue pixels with ink-colored pixels for computer methods. In this study, we propose to segment the ink-marked areas of pathology patches through a deep network. A dataset from $79$ whole slide images with $4,305$ patches was created and different networks were trained. Finally, the results showed an FPN model with the EffiecentNet-B3 as the backbone was found to be the superior configuration with an F1 score of $94.53\%$.