# Instance Segmentation of Biological Images Using Harmonic Embeddings

arXiv: https://arxiv.org/abs/1904.05257 · HTML: https://mlsanity.com/paper/1904.05257/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Apr 2019.

## Built on by (most cited first, 14 of 14)

- [Understanding metric-related pitfalls in image analysis validation](https://mlsanity.com/paper/2302.01790/) (2023)
- [Hierarchical Approach for Joint Semantic, Plant Instance, and Leaf Instance Segmentation in the Agricultural Domain](https://mlsanity.com/paper/2210.07879/) (2022)
- [Sparse Object-level Supervision for Instance Segmentation with Pixel Embeddings](https://mlsanity.com/paper/2103.14572/) (2021)
- [InstanSeg: an embedding-based instance segmentation algorithm optimized for accurate, efficient and portable cell segmentation](https://mlsanity.com/paper/2408.15954/) (2024)
- [BioFors: A Large Biomedical Image Forensics Dataset](https://mlsanity.com/paper/2108.12961/) (2021)
- [How Shift Equivariance Impacts Metric Learning for Instance Segmentation](https://mlsanity.com/paper/2101.05846/) (2021)
- [H-EMD: A Hierarchical Earth Mover's Distance Method for Instance Segmentation](https://mlsanity.com/paper/2206.01309/) (2022)
- [FISBe: A real-world benchmark dataset for instance segmentation of long-range thin filamentous structures](https://mlsanity.com/paper/2404.00130/) (2024)
- [GMT: Guided Mask Transformer for Leaf Instance Segmentation](https://mlsanity.com/paper/2406.17109/) (2024)
- [Embedding-based Instance Segmentation in Microscopy](https://mlsanity.com/paper/2101.10033/) (2021)
- [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)
- [ISG: I can See Your Gene Expression](https://mlsanity.com/paper/2210.16728/) (2022)

## Abstract (arXiv)

We present a new instance segmentation approach tailored to biological images, where instances may correspond to individual cells, organisms or plant parts. Unlike instance segmentation for user photographs or road scenes, in biological data object instances may be particularly densely packed, the appearance variation may be particularly low, the processing power may be restricted, while, on the other hand, the variability of sizes of individual instances may be limited. The proposed approach successfully addresses these peculiarities. Our approach describes each object instance using an expectation of a limited number of sine waves with frequencies and phases adjusted to particular object sizes and densities. At train time, a fully-convolutional network is learned to predict the object embeddings at each pixel using a simple pixelwise regression loss, while at test time the instances are recovered using clustering in the embedding space. In the experiments, we show that our approach outperforms previous embedding-based instance segmentation approaches on a number of biological datasets, achieving state-of-the-art on a popular CVPPP benchmark. This excellent performance is combined with computational efficiency that is needed for deployment to domain specialists. The source code of the approach is available at https://github.com/kulikovv/harmonic