Microscopic nuclei classification, segmentation, and detection with improved deep convolutional neural networks (DCNN)

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.