# Cell segmentation from telecentric bright-field transmitted light microscopy images using a Residual Attention U-Net: a case study on HeLa line

Source: [Cell segmentation from telecentric bright-field transmitted light microscopy images using a Residual Attention U-Net: a case study on HeLa line](<https://www.sota2.com/research/paper/68fa5f2a1e209131ce9e7fa7>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-28T13:56:21.317Z

Authors: Ali Ghaznavi, Renata Rychtarikova, Mohammadmehdi Saberioon, Dalibor Stys

Published: 2022-03-23T09:20:30.000Z

## Abstract

Living cell segmentation from bright-field light microscopy images is challenging due to the image complexity and temporal changes in the living cells. Recently developed deep learning (DL)-based methods became popular in medical and microscopy image segmentation tasks due to their success and promising outcomes. The main objective of this paper is to develop a deep learning, U-Net-based method to segment the living cells of the HeLa line in bright-field transmitted light microscopy. To find the most suitable architecture for our datasets, a residual attention U-Net was proposed and compared with an attention and a simple U-Net architecture.   The attention mechanism highlights the remarkable features and suppresses activations in the irrelevant image regions. The residual mechanism overcomes with vanishing gradient problem. The Mean-IoU score for our datasets reaches 0.9505, 0.9524, and 0.9530 for the simple, attention, and residual attention U-Net, respectively. The most accurate semantic segmentation results was achieved in the Mean-IoU and Dice metrics by applying the residual and attention mechanisms together. The watershed method applied to this best -- Residual Attention -- semantic segmentation result gave the segmentation with the specific information for each cell.

## Recorded benchmark results

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## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2203.12290>)

[PDF](<https://arxiv.org/pdf/2203.12290.pdf>)