# Unsupervised Data-Driven Nuclei Segmentation For Histology Images

arXiv: https://arxiv.org/abs/2110.07147 · HTML: https://mlsanity.com/paper/2110.07147/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 20 references that are in the map.
- First posted on arXiv 14 Oct 2021.

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

- [Path Aggregation Network for Instance Segmentation](https://mlsanity.com/paper/1803.01534/) (2018)
- [Weakly Supervised Deep Nuclei Segmentation Using Partial Points Annotation in Histopathology Images](https://mlsanity.com/paper/2007.05448/) (2020)
- [CIA-Net: Robust Nuclei Instance Segmentation with Contour-aware Information Aggregation](https://mlsanity.com/paper/1903.05358/) (2019)
- [The Domain Shift Problem of Medical Image Segmentation and Vendor-Adaptation by Unet-GAN](https://mlsanity.com/paper/1910.13681/) (2019)
- [Self-Supervised Nuclei Segmentation in Histopathological Images Using Attention](https://mlsanity.com/paper/2007.08373/) (2020)
- [Instance-aware Self-supervised Learning for Nuclei Segmentation](https://mlsanity.com/paper/2007.11186/) (2020)

## Built on by (most cited first, 1 of 1)

- [HUNIS: High-Performance Unsupervised Nuclei Instance Segmentation](https://mlsanity.com/paper/2203.14887/) (2022)

## Abstract (arXiv)

An unsupervised data-driven nuclei segmentation method for histology images, called CBM, is proposed in this work. CBM consists of three modules applied in a block-wise manner: 1) data-driven color transform for energy compaction and dimension reduction, 2) data-driven binarization, and 3) incorporation of geometric priors with morphological processing. CBM comes from the first letter of the three modules - "Color transform", "Binarization" and "Morphological processing". Experiments on the MoNuSeg dataset validate the effectiveness of the proposed CBM method. CBM outperforms all other unsupervised methods and offers a competitive standing among supervised models based on the Aggregated Jaccard Index (AJI) metric.