Ink removal from histopathology whole slide images by combining classification, detection and image generation models

TL;DR

This paper presents a novel deep learning pipeline combining classification, detection, and image generation models to effectively remove ink markings from digitized histopathology slides, improving their usability for research.

Contribution

It introduces an integrated CNN-based approach for classifying, detecting, and restoring ink-marked regions in whole slide images, a novel solution for this specific problem.

Findings

Achieves visually coherent ink-free images

Effective in handling diverse ink contamination patterns

Maintains high image resolution after removal

Abstract

Histopathology slides are routinely marked by pathologists using permanent ink markers that should not be removed as they form part of the medical record. Often tumour regions are marked up for the purpose of highlighting features or other downstream processing such an gene sequencing. Once digitised there is no established method for removing this information from the whole slide images limiting its usability in research and study. Removal of marker ink from these high-resolution whole slide images is non-trivial and complex problem as they contaminate different regions and in an inconsistent manner. We propose an efficient pipeline using convolution neural networks that results in ink-free images without compromising information and image resolution. Our pipeline includes a sequential classical convolution neural network for accurate classification of contaminated image tiles, a fast region detector and a domain adaptive cycle consistent adversarial generative model for restoration of foreground pixels. Both quantitative and qualitative results on four different whole slide images show that our approach yields visually coherent ink-free whole slide images.