Microscopic Nuclei Classification, Segmentation, and Detection with Improved Deep Convolutional Neural Networks (DCNN)

Author(s)

Md Zahangir Alom (0000-0002-2314-1207), St. Jude Children’s Research HospitalFollow
Vijayan K. Asari, University of DaytonFollow
Anil Parwani, The Ohio State University
Tarek M. Taha, University of DaytonFollow

Document Type

Article

Publication Date

4-19-2022

Publication Source

Diagnostic Pathology

Abstract

Background Nuclei classification, segmentation, and detection from pathological images are challenging tasks due to cellular heterogeneity in the Whole Slide Images (WSI). Methods In this work, we propose advanced DCNN models for nuclei classification, segmentation, and detection tasks. The Densely Connected Neural Network (DCNN) and Densely Connected Recurrent Convolutional Network (DCRN) models are applied for the nuclei classification tasks. The Recurrent Residual U-Net (R2U-Net) and the R2UNet-based regression model named the University of Dayton Net (UD-Net) are applied for nuclei segmentation and detection tasks respectively. The experiments are conducted on publicly available datasets, including Routine Colon Cancer (RCC) classification and detection and the Nuclei Segmentation Challenge 2018 datasets for segmentation tasks. The experimental results were evaluated with a five-fold cross-validation method, and the average testing results are compared against the existing approaches in terms of precision, recall, Dice Coefficient (DC), Mean Squared Error (MSE), F1-score, and overall testing accuracy by calculating pixels and cell-level analysis. Results The results demonstrate around 2.6% and 1.7% higher performance in terms of F1-score for nuclei classification and detection tasks when compared to the recently published DCNN based method. Also, for nuclei segmentation, the R2U-Net shows around 91.90% average testing accuracy in terms of DC, which is around 1.54% higher than the U-Net model. Conclusion The proposed methods demonstrate robustness with better quantitative and qualitative results in three different tasks for analyzing the WSI.

ISBN/ISSN

1746-1596

Document Version

Published Version

Comments

This open-access article is provided for download in compliance with the publisher’s policy on self-archiving. To view the version of record, use the DOI: https://doi.org/10.1186/s13000-022-01189-5

Publisher

BMC

Volume

17

Peer Reviewed

yes

Issue

1

eCommons Citation

Alom, Md Zahangir; Asari, Vijayan K.; Parwani, Anil; and Taha, Tarek M., "Microscopic Nuclei Classification, Segmentation, and Detection with Improved Deep Convolutional Neural Networks (DCNN)" (2022). Electrical and Computer Engineering Faculty Publications. 463.
https://ecommons.udayton.edu/ece_fac_pub/463