# TSSP‐UNet: A Two‐Stage Weakly Supervised Pathological Image Segmentation With Point Annotations

## TL;DR

TSSP-UNet is a new method for segmenting cell nuclei in images using weak supervision and point annotations, achieving better results than existing methods.

## Contribution

The novel two-stage weakly supervised approach TSSP-UNet improves segmentation using constraint, attention, and confident learning mechanisms.

## Key findings

- TSSP-UNet outperforms baseline methods in weakly supervised cell nucleus segmentation.
- The two-stage approach effectively refines pseudo-labels and improves segmentation accuracy.
- Constraint and attention mechanisms enhance boundary and superpixel-based segmentation.

## Abstract

Deep convolutional neural networks have demonstrated remarkable effectiveness in image segmentation. However, segmentation becomes challenging when training on images with complex instances. Moreover, obtaining annotations for high‐precision data is also difficult. Weakly supervised learning can address this issue by using nonspecialised annotations or supervised information from segmentation algorithms. In this study, we proposed TSSP‐UNet: a two‐stage weakly supervised segmentation approach. In the first stage, we trained a segmentation network augmented with constraint and attention mechanisms. These mechanisms are designed to operate on boundaries and superpixels generated from pseudo‐labels. For the attention network, two pseudo‐labels were used with a binary mask to add contour information to the segmentation process. Furthermore, a feature aggregation segmentation network was applied to the prominent foreground area in the image by incrementally adding elements. In the second stage, a refined confident learning algorithm improved the pseudo‐labels at the pixel level and then TSSP‐UNet was retrained using the modified superpixel labels. Testing on the MoNuSeg and TNBC datasets demonstrates that the approach performs well in the weakly supervised cell nucleus segmentation task compared with baseline methods.

Deep convolutional neural networks excel at image segmentation but face challenges with complex instance training and high‐precision annotation acquisition. This study proposes TSSP‐UNet, a two‐stage weakly supervised segmentation approach: the first stage trains a segmentation network with constraint and attention mechanisms plus a feature aggregation network, whereas the second stage refines pseudo‐labels via a confident learning algorithm before retraining. Tests on the MoNuSeg and TNBC datasets show it outperforms baseline methods in weakly supervised cell nucleus segmentation.