# Multiclass Weighted Loss for Instance Segmentation of Cluttered Cells

arXiv: https://arxiv.org/abs/1802.07465 · HTML: https://mlsanity.com/paper/1802.07465/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Feb 2018.

## Built on by (most cited first, 10 of 10)

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- [Enforcing Morphological Information in Fully Convolutional Networks to Improve Cell Instance Segmentation in Fluorescence Microscopy Images](https://mlsanity.com/paper/2106.05843/) (2021)
- [Interactive segmentation using U-Net with weight map and dynamic user interactions](https://mlsanity.com/paper/2111.09740/) (2021)
- [Steerable Pyramid Weighted Loss: Multi-Scale Adaptive Weighting for Semantic Segmentation](https://mlsanity.com/paper/2503.06604/) (2025)
- [SYNTA: A novel approach for deep learning-based image analysis in muscle histopathology using photo-realistic synthetic data](https://mlsanity.com/paper/2207.14650/) (2022)
- [Deep learning-based method for segmenting epithelial layer of tubules in histopathological images of testicular tissue](https://mlsanity.com/paper/2301.09887/) (2023)
- [Enhancing Cell Instance Segmentation in Scanning Electron Microscopy Images via a Deep Contour Closing Operator](https://mlsanity.com/paper/2407.15817/) (2024)
- [Single Tensor Cell Segmentation using Scalar Field Representations](https://mlsanity.com/paper/2511.13947/) (2025)

## Abstract (arXiv)

We propose a new multiclass weighted loss function for instance segmentation of cluttered cells. We are primarily motivated by the need of developmental biologists to quantify and model the behavior of blood T-cells which might help us in understanding their regulation mechanisms and ultimately help researchers in their quest for developing an effective immuno-therapy cancer treatment. Segmenting individual touching cells in cluttered regions is challenging as the feature distribution on shared borders and cell foreground are similar thus difficulting discriminating pixels into proper classes. We present two novel weight maps applied to the weighted cross entropy loss function which take into account both class imbalance and cell geometry. Binary ground truth training data is augmented so the learning model can handle not only foreground and background but also a third touching class. This framework allows training using U-Net. Experiments with our formulations have shown superior results when compared to other similar schemes, outperforming binary class models with significant improvement of boundary adequacy and instance detection. We validate our results on manually annotated microscope images of T-cells.