CellSegUNet: an improved deep segmentation model for the cell segmentation based on UNet++ and residual UNet models

1Citations
Citations of this article
15Readers
Mendeley users who have this article in their library.

This article is free to access.

Abstract

Cell nucleus segmentation is an important method that is widely used in the diagnosis and treatment of many diseases, as well as counting and identifying the cell nucleus. The main challenges when using this method are heterogeneous image intensities in the image, overlapping of cell nuclei, and noise. In order to overcome these difficulties, a hybrid segmentation model with attention block, CellSegUNet, is proposed, inspired by the advantageous points of UNet++ and Residual UNet models. With the proposed attention mechanism, semantic gaps that may occur are prevented by evaluating both horizontal and vertical features together. The serial and parallel connection of the convolutional blocks in the residual modules in the CellSegUNet model prevents data loss. Thus, features with stronger representation ability were obtained. The output layer, which is, especially proposed for the CellSegUNet model, calculated the differences between the data in each layer and the data in the input layer. The output value obtained from the layer level where the lowest value comes from constitutes the output of the whole system. At the same depth level, CellSegUNet versus UNet++ and ResUNet models were compared on Data Science Bowl (DSB), Sartorius Cell Instance Segmentation (SCIS), and Blood Cell Segmentation (BCS) datasets. With the CellSegUNet model, accuracy, dice, and jaccard metrics were obtained as 0.980, 0.970, 0.959 for the DSB dataset, 0.931, 0.957, 0.829 for the SCIS dataset and 0.976, 0.971, 0.927 for the BCS dataset, respectively. As a result, it is predicted that the proposed model can provide solutions to different segmentation problems.

References Powered by Scopus

Image Segmentation Using Deep Learning: A Survey

1740Citations
N/AReaders
Get full text

CE-Net: Context Encoder Network for 2D Medical Image Segmentation

1715Citations
N/AReaders
Get full text

GCNet: Non-local networks meet squeeze-excitation networks and beyond

1487Citations
N/AReaders
Get full text

Cited by Powered by Scopus

Improving Cell Image Segmentation by Using Isotropic Undecimated Wavelet Transform

0Citations
N/AReaders
Get full text

Register to see more suggestions

Mendeley helps you to discover research relevant for your work.

Already have an account?

Cite

CITATION STYLE

APA

Metlek, S. (2024). CellSegUNet: an improved deep segmentation model for the cell segmentation based on UNet++ and residual UNet models. Neural Computing and Applications, 36(11), 5799–5825. https://doi.org/10.1007/s00521-023-09374-3

Readers over time

‘2405101520

Readers' Seniority

Tooltip

Professor / Associate Prof. 2

40%

PhD / Post grad / Masters / Doc 2

40%

Researcher 1

20%

Readers' Discipline

Tooltip

Computer Science 2

50%

Engineering 2

50%

Save time finding and organizing research with Mendeley

Sign up for free
0