# Instance Segmentation of Biomedical Images with an Object-aware Embedding Learned with Local Constraints

arXiv: https://arxiv.org/abs/2004.09821 · HTML: https://mlsanity.com/paper/2004.09821/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Apr 2020.

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

- [CPP-Net: Context-aware Polygon Proposal Network for Nucleus Segmentation](https://mlsanity.com/paper/2102.06867/) (2021)
- [Modality specific U-Net variants for biomedical image segmentation: A survey](https://mlsanity.com/paper/2107.04537/) (2021)
- [How Shift Equivariance Impacts Metric Learning for Instance Segmentation](https://mlsanity.com/paper/2101.05846/) (2021)
- [A Survey of Computer Vision Technologies In Urban and Controlled-environment Agriculture](https://mlsanity.com/paper/2210.11318/) (2022)
- [Instance Segmentation of Dense and Overlapping Objects via Layering](https://mlsanity.com/paper/2210.03551/) (2022)
- [H-EMD: A Hierarchical Earth Mover's Distance Method for Instance Segmentation](https://mlsanity.com/paper/2206.01309/) (2022)
- [Semi-supervised Instance Segmentation with a Learned Shape Prior](https://mlsanity.com/paper/2309.04888/) (2023)
- [FISBe: A real-world benchmark dataset for instance segmentation of long-range thin filamentous structures](https://mlsanity.com/paper/2404.00130/) (2024)
- [Spatial-Temporal Deep Embedding for Vehicle Trajectory Reconstruction from High-Angle Video](https://mlsanity.com/paper/2209.08417/) (2022)
- [Graph Relation Distillation for Efficient Biomedical Instance Segmentation](https://mlsanity.com/paper/2401.06370/) (2024)
- [Instance segmentation with the number of clusters incorporated in embedding learning](https://mlsanity.com/paper/2103.14869/) (2021)
- [MORI-Seg: Learning Morphological Geometry for Instance Segmentation without Instance Annotations](https://mlsanity.com/paper/2605.28261/) (2026)

## Abstract (arXiv)

Automatic instance segmentation is a problem that occurs in many biomedical applications. State-of-the-art approaches either perform semantic segmentation or refine object bounding boxes obtained from detection methods. Both suffer from crowded objects to varying degrees, merging adjacent objects or suppressing a valid object. In this work, we assign an embedding vector to each pixel through a deep neural network. The network is trained to output embedding vectors of similar directions for pixels from the same object, while adjacent objects are orthogonal in the embedding space, which effectively avoids the fusion of objects in a crowd. Our method yields state-of-the-art results even with a light-weighted backbone network on a cell segmentation (BBBC006 + DSB2018) and a leaf segmentation data set (CVPPP2017). The code and model weights are public available.