# EfficientCellSeg: Efficient Volumetric Cell Segmentation Using Context Aware Pseudocoloring

arXiv: https://arxiv.org/abs/2204.03014 · HTML: https://mlsanity.com/paper/2204.03014/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 24 references that are in the map.
- First posted on arXiv 6 Apr 2022.

## Builds on (most cited first, 12 of 12)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks](https://mlsanity.com/paper/1905.11946/) (2019)
- [V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentation](https://mlsanity.com/paper/1606.04797/) (2016)
- [Focal Loss for Dense Object Detection](https://mlsanity.com/paper/1708.02002/) (2017)
- [Cell Detection with Star-convex Polygons](https://mlsanity.com/paper/1806.03535/) (2018)
- [Attention-Based Transformers for Instance Segmentation of Cells in Microstructures](https://mlsanity.com/paper/2011.09763/) (2020)
- [Combining Fully Convolutional and Recurrent Neural Networks for 3D Biomedical Image Segmentation](https://mlsanity.com/paper/1609.01006/) (2016)
- [Microscopy Cell Segmentation via Convolutional LSTM Networks](https://mlsanity.com/paper/1805.11247/) (2018)
- [Red blood cell image generation for data augmentation using Conditional Generative Adversarial Networks](https://mlsanity.com/paper/1901.06219/) (2019)
- [J Regularization Improves Imbalanced Multiclass Segmentation](https://mlsanity.com/paper/1910.09783/) (2019)
- [Semi supervised segmentation and graph-based tracking of 3D nuclei in time-lapse microscopy](https://mlsanity.com/paper/2010.13343/) (2020)

## Built on by (most cited first, 2 of 2)

- [CellCentroidFormer: Combining Self-attention and Convolution for Cell Detection](https://mlsanity.com/paper/2206.00338/) (2022)
- [Self-supervised pseudo-colorizing of masked cells](https://mlsanity.com/paper/2302.05968/) (2023)

## Abstract (arXiv)

Volumetric cell segmentation in fluorescence microscopy images is important to study a wide variety of cellular processes. Applications range from the analysis of cancer cells to behavioral studies of cells in the embryonic stage. Like in other computer vision fields, most recent methods use either large convolutional neural networks (CNNs) or vision transformer models (ViTs). Since the number of available 3D microscopy images is typically limited in applications, we take a different approach and introduce a small CNN for volumetric cell segmentation. Compared to previous CNN models for cell segmentation, our model is efficient and has an asymmetric encoder-decoder structure with very few parameters in the decoder. Training efficiency is further improved via transfer learning. In addition, we introduce Context Aware Pseudocoloring to exploit spatial context in z-direction of 3D images while performing volumetric cell segmentation slice-wise. We evaluated our method using different 3D datasets from the Cell Segmentation Benchmark of the Cell Tracking Challenge. Our segmentation method achieves top-ranking results, while our CNN model has an up to 25x lower number of parameters than other top-ranking methods. Code and pretrained models are available at: https://github.com/roydenwa/efficient-cell-seg