FRE-Net: Full-region enhanced network for nuclei segmentation in histopathology images

Accurate nuclei segmentation is a critical step for physicians to achieve essential informa-tion about a patient's disease through digital pathology images, enabling an effective diag-nosis and evaluation of subsequent treatments. Since pathology images contain many nuclei, manual segmentation is time-consuming and error-prone. Therefore, developing a precise and automatic method for nuclei segmentation is urgent. This paper proposes a novel multi-task segmentation network that incorporates background and contour seg-mentation into the nuclei segmentation method and produces more accurate segmenta-tion results. The convolution and attention modules are merged with the model to increase its global focus and enhance good segmentation results indirectly. We propose a reverse feature enhance module for contour extraction that facilitates feature integration between auxiliary tasks. The multi-feature fusion module is embedded in the final decod-ing branch to use different levels of features from auxiliary segmentation branches with varying concerns. We evaluate the proposed method on four challenging nuclei segmenta-tion datasets. The proposed method achieves excellent performance on all four datasets. We found that the Dice coefficient reached 0.8563 +/- 0.0323, 0.8183 +/- 0.0383, 0.9222 +/- 0.0216, and 0.9220 +/- 0.0602 on the TNBC, MoNuSeg, KMC, and Glas. Our method produces better boundary accuracy and less sticking than other end-to-end segmentation methods. The results show that our method can perform better than other proposed state-of-the-art methods.(c) 2023 Nalecz Institute of Biocybernetics and Biomedical Engineering of the Polish Academy of Sciences. Published by Elsevier B.V. All rights reserved.