# Tissue Cross-Section and Pen Marking Segmentation in Whole Slide Images

arXiv: https://arxiv.org/abs/2401.13511 · HTML: https://mlsanity.com/paper/2401.13511/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 13 references that are in the map.
- First posted on arXiv 24 Jan 2024.

## Builds on (most cited first, 6 of 6)

- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Data Efficient and Weakly Supervised Computational Pathology on Whole Slide Images](https://mlsanity.com/paper/2004.09666/) (2020)
- [HoVer-Net: Simultaneous Segmentation and Classification of Nuclei in Multi-Tissue Histology Images](https://mlsanity.com/paper/1812.06499/) (2018)
- [Virchow: A Million-Slide Digital Pathology Foundation Model](https://mlsanity.com/paper/2309.07778/) (2023)
- [A General-Purpose Self-Supervised Model for Computational Pathology](https://mlsanity.com/paper/2308.15474/) (2023)

## Built on by (most cited first, 4 of 4)

- [Pathology Report Generation and Multimodal Representation Learning for Cutaneous Melanocytic Lesions](https://mlsanity.com/paper/2502.19293/) (2025)
- [Artificial Intelligence-Based Triaging of Cutaneous Melanocytic Lesions](https://mlsanity.com/paper/2410.10509/) (2024)
- [On the Importance of Text Preprocessing for Multimodal Representation Learning and Pathology Report Generation](https://mlsanity.com/paper/2502.19285/) (2025)
- [Artificial Intelligence-Based Classification of Spitz Tumors](https://mlsanity.com/paper/2508.05391/) (2025)

## Abstract (arXiv)

Tissue segmentation is a routine preprocessing step to reduce the computational cost of whole slide image (WSI) analysis by excluding background regions. Traditional image processing techniques are commonly used for tissue segmentation, but often require manual adjustments to parameter values for atypical cases, fail to exclude all slide and scanning artifacts from the background, and are unable to segment adipose tissue. Pen marking artifacts in particular can be a potential source of bias for subsequent analyses if not removed. In addition, several applications require the separation of individual cross-sections, which can be challenging due to tissue fragmentation and adjacent positioning. To address these problems, we develop a convolutional neural network for tissue and pen marking segmentation using a dataset of 200 H&E stained WSIs. For separating tissue cross-sections, we propose a novel post-processing method based on clustering predicted centroid locations of the cross-sections in a 2D histogram. On an independent test set, the model achieved a mean Dice score of 0.981±0.033 for tissue segmentation and a mean Dice score of 0.912±0.090 for pen marking segmentation. The mean absolute difference between the number of annotated and separated cross-sections was 0.075±0.350. Our results demonstrate that the proposed model can accurately segment H&E stained tissue cross-sections and pen markings in WSIs while being robust to many common slide and scanning artifacts. The model with trained model parameters and post-processing method are made publicly available as a Python package called SlideSegmenter.