# Instance Segmentation of Dense and Overlapping Objects via Layering

arXiv: https://arxiv.org/abs/2210.03551 · HTML: https://mlsanity.com/paper/2210.03551/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 23 references that are in the map.
- First posted on arXiv 7 Oct 2022.

## Builds on (most cited first, 13 of 13)

- [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)
- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks](https://mlsanity.com/paper/1506.01497/) (2015)
- [V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentation](https://mlsanity.com/paper/1606.04797/) (2016)
- [Rich feature hierarchies for accurate object detection and semantic segmentation](https://mlsanity.com/paper/1311.2524/) (2013)
- [Cell Detection with Star-convex Polygons](https://mlsanity.com/paper/1806.03535/) (2018)
- [SSAP: Single-Shot Instance Segmentation With Affinity Pyramid](https://mlsanity.com/paper/1909.01616/) (2019)
- [DCAN: Deep Contour-Aware Networks for Accurate Gland Segmentation](https://mlsanity.com/paper/1604.02677/) (2016)
- [Deep Watershed Transform for Instance Segmentation](https://mlsanity.com/paper/1611.08303/) (2016)
- [Recurrent Pixel Embedding for Instance Grouping](https://mlsanity.com/paper/1712.08273/) (2017)
- [Instance Segmentation of Biomedical Images with an Object-aware Embedding Learned with Local Constraints](https://mlsanity.com/paper/2004.09821/) (2020)
- [Improving Pixel Embedding Learning through Intermediate Distance Regression Supervision for Instance Segmentation](https://mlsanity.com/paper/2007.06660/) (2020)

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

- [SortedAP: Rethinking evaluation metrics for instance segmentation](https://mlsanity.com/paper/2309.04887/) (2023)
- [Segmentation and Characterization of Macerated Fibers and Vessels Using Deep Learning](https://mlsanity.com/paper/2401.16937/) (2024)
- [CloudTracks: A Dataset for Localizing Ship Tracks in Satellite Images of Clouds](https://mlsanity.com/paper/2401.14486/) (2024)
- [MultiSolSegment: Multi-channel segmentation of overlapping features in electroluminescence images of photovoltaic cells](https://mlsanity.com/paper/2603.13337/) (2026)
- [QCell: Recombining and Aligning Cell Queries for Overlapping Instance Segmentation](https://mlsanity.com/paper/2608.29253/) (2026)

## Abstract (arXiv)

Instance segmentation aims to delineate each individual object of interest in an image. State-of-the-art approaches achieve this goal by either partitioning semantic segmentations or refining coarse representations of detected objects. In this work, we propose a novel approach to solve the problem via object layering, i.e. by distributing crowded, even overlapping objects into different layers. By grouping spatially separated objects in the same layer, instances can be effortlessly isolated by extracting connected components in each layer. In comparison to previous methods, our approach is not affected by complex object shapes or object overlaps. With minimal post-processing, our method yields very competitive results on a diverse line of datasets: C. elegans (BBBC), Overlapping Cervical Cells (OCC) and cultured neuroblastoma cells (CCDB). The source code is publicly available.