# BriFiSeg: a deep learning-based method for semantic and instance segmentation of nuclei in brightfield images

arXiv: https://arxiv.org/abs/2211.03072 · HTML: https://mlsanity.com/paper/2211.03072/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 22 references that are in the map.
- First posted on arXiv 6 Nov 2022.

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

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Squeeze-and-Excitation Networks](https://mlsanity.com/paper/1709.01507/) (2017)
- [The Importance of Skip Connections in Biomedical Image Segmentation](https://mlsanity.com/paper/1608.04117/) (2016)
- [TernausNet: U-Net with VGG11 Encoder Pre-Trained on ImageNet for Image Segmentation](https://mlsanity.com/paper/1801.05746/) (2018)
- [Feature Pyramid Network for Multi-Class Land Segmentation](https://mlsanity.com/paper/1806.03510/) (2018)

## Built on by (most cited first, 5 of 5)

- [MedGround: Bridging the Evidence Gap in Medical Vision-Language Models with Verified Grounding Data](https://mlsanity.com/paper/2601.06847/) (2026)
- [CHAMMI-75: Pre-training multi-channel models with heterogeneous microscopy images](https://mlsanity.com/paper/2512.20833/) (2025)
- [MieDB-100k: A Comprehensive Dataset for Medical Image Editing](https://mlsanity.com/paper/2602.09587/) (2026)
- [Personalized Cell Segmentation: Benchmark and Framework for Reference-Guided Cell Type Segmentation](https://mlsanity.com/paper/2603.14321/) (2026)
- [Count Anything](https://mlsanity.com/paper/2605.30846/) (2026)

## Abstract (arXiv)

Generally, microscopy image analysis in biology relies on the segmentation of individual nuclei, using a dedicated stained image, to identify individual cells. However stained nuclei have drawbacks like the need for sample preparation, and specific equipment on the microscope but most importantly, and as it is in most cases, the nuclear stain is not relevant to the biological questions of interest but is solely used for the segmentation task. In this study, we used non-stained brightfield images for nuclei segmentation with the advantage that they can be acquired on any microscope from both live or fixed samples and do not necessitate specific sample preparation. Nuclei semantic segmentation from brightfield images was obtained, on four distinct cell lines with U-Net-based architectures. We tested systematically deep pre-trained encoders to identify the best performing in combination with the different neural network architectures used. Additionally, two distinct and effective strategies were employed for instance segmentation, followed by thorough instance evaluation. We obtained effective semantic and instance segmentation of nuclei in brightfield images from standard test sets as well as from very diverse biological contexts triggered upon treatment with various small molecule inhibitor. The code used in this study was made public to allow further use by the community.