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

• URL: http://arxiv.org/abs/2203.12290v1
• Date: Wed, 23 Mar 2022 09:20:30 GMT
• Title: Cell segmentation from telecentric bright-field transmitted light microscopic images using a Residual Attention U-Net: a case study on HeLa line
• Authors: Ali Ghaznavi, Renata Rychtarikova, Mohammadmehdi Saberioon, Dalibor Stys
• Abstract summary: Living cell segmentation from bright-field light microscopic 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 microscopic image segmentation tasks due to their success and promising outcomes. The main objective of this paper is to develop a deep learning, UNet-based method to segment the living cells of the HeLa line in bright-field transmitted light microscopy.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Living cell segmentation from bright-field light microscopic 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 microscopic image segmentation tasks due to their success and promising outcomes. The main objective of this paper is to develop a deep learning, UNet-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, we have proposed a residual attention U-Net and compared it 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. We achieved the most accurate semantic segmentation results 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.

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