MSCA-UNet: Multi-Scale Convolutional Attention UNet for Automatic Cell Counting Using Density Regression

The quantification of cell numbers in microscopy images plays a vital role in biomedical research and medical diagnosis. Presently, deep regression networks are widely employed to generate cell density maps, and the number of cells is obtained by integrating the density maps. However, automating cel...

Full description

Saved in:
Bibliographic Details
Published in:IEEE access 2023, Vol.11, p.85990-86001
Main Authors: Qian, Like, Qian, Wei, Tian, Dingcheng, Zhu, Yaqi, Zhao, Heng, Yao, Yudong
Format: Article
Language:English
Subjects:
Adipose tissue
Automatic cell counting
Bone marrow
Cells (biology)
Coders
Datasets
Decoding
Density
density map
Diseases
Estimation
Feature extraction
Image restoration
Medical imaging
Medical research
Microscopy
microscopy images
multi-scale convolutional attention
Regression
Spatial data
Task analysis
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:The quantification of cell numbers in microscopy images plays a vital role in biomedical research and medical diagnosis. Presently, deep regression networks are widely employed to generate cell density maps, and the number of cells is obtained by integrating the density maps. However, automating cell counting remains challenging due to the variability in cell morphology, the diversity of cell types, and the interference of image backgrounds. This paper aims to address the central question: 'Can we design a robust and efficient deep learning model that can effectively count cells in microscopy images, regardless of these challenges?' To tackle this issue, we propose a novel multi-scale convolutional attention UNet (MSCA-UNet) based on density regression. Compared with other advanced density regression methods, our method introduces two key innovations. Firstly, we employ an MSCA block with multi-scale interaction ability as an encoder component, which, when combined with spatial attention, enhances the extraction of cell details and spatial information. Secondly, the design of the asymmetric UNet allows the encoder to extract more global information and better understand the image. In the meantime, using smaller convolutional kernels and strides in the decoder helps to restore image details and edge information, resulting in improved network performance. Our method outperformed other advanced methods on three publicly available benchmark cell datasets, including the synthetic bacterial (VGG) dataset, the modified bone marrow (MBM) dataset, and the human subcutaneous adipose tissue (ADI) dataset.
ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2023.3304993