Ink Marker Segmentation in Histopathology Images Using Deep Learning

Danial Maleki, Mehdi Afshari, Morteza Babaie, H. R. Tizhoosh

Research output: Chapter in Book/Report/Conference proceedingConference contribution


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 %.

Original languageEnglish (US)
Title of host publicationAdvances in Visual Computing - 15th International Symposium, ISVC 2020, Proceedings
EditorsGeorge Bebis, Zhaozheng Yin, Edward Kim, Jan Bender, Kartic Subr, Bum Chul Kwon, Jian Zhao, Denis Kalkofen, George Baciu
PublisherSpringer Science and Business Media Deutschland GmbH
Number of pages10
ISBN (Print)9783030645557
StatePublished - 2020
Event15th International Symposium on Visual Computing, ISVC 2020 - San Diego, United States
Duration: Oct 5 2020Oct 7 2020

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume12509 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference15th International Symposium on Visual Computing, ISVC 2020
Country/TerritoryUnited States
CitySan Diego


  • Artifact removal
  • Convolutional neural networks
  • FPN
  • Histopathology
  • Ink marker segmentation
  • U-Net
  • Whole slide images

ASJC Scopus subject areas

  • Theoretical Computer Science
  • General Computer Science


Dive into the research topics of 'Ink Marker Segmentation in Histopathology Images Using Deep Learning'. Together they form a unique fingerprint.

Cite this