US-net for robust and efficient nuclei instance segmentation
This work addresses robust nuclei segmentation for medical image analysis, but it appears incremental as it builds on existing detection and segmentation networks.
The authors tackled the problem of nuclei instance segmentation in histopathology images by proposing US-Net, an integrated neural network architecture that shares outputs between detection and segmentation branches, resulting in enhanced performance that outperforms most state-of-the-art methods.
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.