US-net for robust and efficient nuclei instance segmentation

TL;DR

US-Net is a novel neural network architecture that combines detection and segmentation for nuclei in histopathology images, improving accuracy and robustness over existing methods.

Contribution

The paper introduces US-Net, a unified framework that integrates detection and segmentation networks sharing a common foundation, enhancing performance in nuclei instance segmentation.

Findings

Outperforms most state-of-the-art nuclei detection and segmentation networks

Enhances both detection and segmentation performance through shared architecture

Demonstrates robustness and efficiency in histopathology image analysis

Abstract

We present a novel neural network architecture, US-Net, for robust nuclei instance segmentation in histopathology images. The proposed framework integrates the nuclei detection and segmentation networks by sharing their outputs through the same foundation network, and thus enhancing the performance of both. The detection network takes into account the high-level semantic cues with contextual information, while the segmentation network focuses more on the low-level details like the edges. Extensive experiments reveal that our proposed framework can strengthen the performance of both branch networks in an integrated architecture and outperforms most of the state-of-the-art nuclei detection and segmentation networks.