EDNFC-Net: Convolutional Neural Network with Nested Feature Concatenation for Nuclei-Instance Segmentation

Abstract

Accurate nuclei identification is an important step in diagnosis of several diseases. The problem is complex due to heterogeneity in structure, color, and texture among the different categories of cells. The problem is further complicated due to overlapped/clustered nuclei. To address these challenges, we propose an Encoder-Decoder based Convolutional Neural Network (CNN) with Nested-Feature Concatenation (EDNFC-Net) for automatic nuclei segmentation. The feature concatenation cell (FCC) of the EDNFC-Net is made up of two stacks of convolutional filters combined with non-linearity, followed by a concatenation of features. Apart from intra-FCC feature concatenation, a mechanism is also provided for inter-FCC feature concatenation. This arrangement leads to better feature flow and feature-reusability. Similarly, direct feature flow is provided between the encoder and decoder module that preserves the context information. A new loss function with better-penalizing capability is also proposed that helps in the better background and foreground separation. Qualitative and quantitative results are provided on two datasets to validate the proposed architecture and loss function.