# J Regularization Improves Imbalanced Multiclass Segmentation

arXiv: https://arxiv.org/abs/1910.09783 · HTML: https://mlsanity.com/paper/1910.09783/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Oct 2019.

## Built on by (most cited first, 7 of 7)

- [The Multi-modality Cell Segmentation Challenge: Towards Universal Solutions](https://mlsanity.com/paper/2308.05864/) (2023)
- [Cell Tracking according to Biological Needs -- Strong Mitosis-aware Multi-Hypothesis Tracker with Aleatoric Uncertainty](https://mlsanity.com/paper/2403.15011/) (2024)
- [EfficientCellSeg: Efficient Volumetric Cell Segmentation Using Context Aware Pseudocoloring](https://mlsanity.com/paper/2204.03014/) (2022)
- [H-EMD: A Hierarchical Earth Mover's Distance Method for Instance Segmentation](https://mlsanity.com/paper/2206.01309/) (2022)
- [Training a universal instance segmentation network for live cell images of various cell types and imaging modalities](https://mlsanity.com/paper/2207.14347/) (2022)
- [PhagoStat a scalable and interpretable end to end framework for efficient quantification of cell phagocytosis in neurodegenerative disease studies](https://mlsanity.com/paper/2304.13764/) (2023)
- [Cell as Point: One-Stage Framework for Efficient Cell Tracking](https://mlsanity.com/paper/2411.14833/) (2024)

## Abstract (arXiv)

We propose a new loss formulation to further advance the multiclass segmentation of cluttered cells under weakly supervised conditions. We improve the separation of touching and immediate cells, obtaining sharp segmentation boundaries with high adequacy, when we add Youden's J statistic regularization term to the cross entropy loss. This regularization intrinsically supports class imbalance thus eliminating the necessity of explicitly using weights to balance training. Simulations demonstrate this capability and show how the regularization leads to better results by helping advancing the optimization when cross entropy stalls. We build upon our previous work on multiclass segmentation by adding yet another training class representing gaps between adjacent cells. This addition helps the classifier identify narrow gaps as background and no longer as touching regions. We present results of our methods for 2D and 3D images, from bright field to confocal stacks containing different types of cells, and we show that they accurately segment individual cells after training with a limited number of annotated images, some of which are poorly annotated.