Biotechnology and Bioprocess Engineering 2024; 29(6): 1034-1047  
Automatic cancer nuclei segmentation on histological images: comparison study of deep learning methods
Maratbek T. Gabdullin 1 · Assel Mukasheva 2 · Dina Koishiyeva 2 · Timur Umarov 2 · Alibek Bissembayev 2 · Ki-Sub Kim 3 · Jeong Won Kang 4
1 School of Materials Science and Green Technologies , Kazakh-British Technical University , 050000 Almaty , Republic of Kazakhstan
2 School of Information Technology and Engineering , Kazakh-British Technical University , 050000 Almaty , Republic of Kazakhstan
3 Department of Chemical and Biological Engineering , Korea National University of Transportation , Chungju 27469 , Korea
4 Department of Transportation System Engineering , Korea National University of Transportation , Uiwang 16106 , Korea
Correspondence to: ✉ Assel Mukasheva
mukashevascience@gmail.com

✉ Ki-Sub Kim
kks1114@ut.ac.kr

✉ Jeong Won Kang
jwkang@ut.ac.kr
Received: January 17, 2024; Revised: June 21, 2024; Accepted: June 26, 2024; Published online: July 4, 0204.
© The Korean Society for Biotechnology and Bioengineering. All rights reserved.

This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/3.0/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.
Abstract
Cancer is one of the most common health problems affecting individuals worldwide. In the field of biomedical engineering, one of the main methods for cancer diagnosis is the analysis of histological images of tissue structures and cell nuclei using artificial intelligence. Here, we compared the performance of 15 deep learning methods viz: UNet, Deep-UNet, UNet-CBAM, RA-UNet, SA-Unet and Nuclei-SegNet, UNet-VGG2016, UNet-Resnet-101, TransResUNet, Inception-UNet, Att-UNet++ , FF-UNet, Att-UNet, Res-UNet and a new model, DanNucNet, in pathological nuclei segmentation on tissue slices from different organs on five open datasets: MoNuSeg, CoNSeP, CryoNuSeg, Data Science Bowl, and NuInsSeg. Before training on the data, the pixel intensity and color distribution were analyzed, and different augmentation techniques were applied. The results showed that the UNet-based model with 34.57 million Deep-UNet parameters performed the best, outperforming all models in terms of the Dice coefficient from 3.13 to 22.91%. The implementation of Deep-UNet in this context provides a valuable tool for accurate extraction of cancer cell nuclei from histological images, which in turn will contribute to further developments in cancer pathology and digital histology.
Keywords: Cancer · Medical segmentation · Histology · Convolutional neural networks · Augmentation

This Article


Cited By Articles
  • CrossRef (0)

Services
Social Network Service
Search for

Archives