# Adapting Mask-RCNN for Automatic Nucleus Segmentation

arXiv: https://arxiv.org/abs/1805.00500 · HTML: https://mlsanity.com/paper/1805.00500/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 May 2018.

## Built on by (most cited first, 12 of 12)

- [Circle Representation for Medical Object Detection](https://mlsanity.com/paper/2110.12093/) (2021)
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- [Automatic characterization of boulders on planetary surfaces from high-resolution satellite images](https://mlsanity.com/paper/2401.07528/) (2024)
- [Segmentation and Characterization of Macerated Fibers and Vessels Using Deep Learning](https://mlsanity.com/paper/2401.16937/) (2024)
- [MemX: An Attention-Aware Smart Eyewear System for Personalized Moment Auto-capture](https://mlsanity.com/paper/2105.00916/) (2021)
- [Transformer Assisted Convolutional Network for Cell Instance Segmentation](https://mlsanity.com/paper/2110.02270/) (2021)
- [Advanced Multi-Microscopic Views Cell Semi-supervised Segmentation](https://mlsanity.com/paper/2303.11661/) (2023)
- [Improving Generalization Capability of Deep Learning-Based Nuclei Instance Segmentation by Non-deterministic Train Time and Deterministic Test Time Stain Normalization](https://mlsanity.com/paper/2309.06143/) (2023)
- [Weak-to-Strong Generalization Enables Fully Automated De Novo Training of Multi-head Mask-RCNN Model for Segmenting Densely Overlapping Cell Nuclei in Multiplex Whole-slice Brain Images](https://mlsanity.com/paper/2512.11722/) (2025)

## Abstract (arXiv)

Automatic segmentation of microscopy images is an important task in medical image processing and analysis. Nucleus detection is an important example of this task. Mask-RCNN is a recently proposed state-of-the-art algorithm for object detection, object localization, and object instance segmentation of natural images. In this paper we demonstrate that Mask-RCNN can be used to perform highly effective and efficient automatic segmentations of a wide range of microscopy images of cell nuclei, for a variety of cells acquired under a variety of conditions.