Harbin University of Science and Technology, School of Computer Science and Technology, No.52 Xuefu Road, Harbin, 150080, China.
Harbin University of Science and Technology, School of Computer Science and Technology, No.52 Xuefu Road, Harbin, 150080, China.
Comput Biol Med. 2022 Jul;146:105546. doi: 10.1016/j.compbiomed.2022.105546. Epub 2022 Apr 22.
Nuclei segmentation is a key technique for automatic pathological screening. Although many methods have been proposed, it remains a challenge because of numerous nuclei clusters, high variability of object appearances and complex backgrounds. To address these issues, we propose a novel multi-task region-enhanced nuclei segmentation network (REU-Net). It stacks three U-shaped structures by combining serial and parallel approaches to construct a multi-task architecture. The model employs two auxiliary tasks, i.e., contour extraction and rough segmentation to help the main task of fine segmentation. The saliency regions are enhanced by the prediction results of the auxiliary tasks, and the enhanced images are further segmented through the main task. In addition, the spatial and texture features in auxiliary tasks are aggregated by attention gates, helping the main task to refine the details of nuclei and contours. Extensive experiments are conducted to evaluate the proposed method qualitatively and quantitatively. Experimental results show that REU-Net outperforms the state-of-the-art methods on HUSTS, MoNuSeg, CoNSep and CPM-17 datasets.
核分割是自动病理筛选的关键技术。尽管已经提出了许多方法，但由于存在大量核簇、对象外观的高度可变性和复杂的背景，因此仍然是一项挑战。针对这些问题，我们提出了一种新的多任务区域增强核分割网络（REU-Net）。它通过串联和并行两种方法堆叠三个 U 型结构，构建了一个多任务架构。该模型采用了两个辅助任务，即轮廓提取和粗略分割，以帮助精细分割的主要任务。通过辅助任务的预测结果增强显著区域，并通过主要任务进一步分割增强后的图像。此外，通过注意力门聚合辅助任务中的空间和纹理特征，帮助主要任务细化核和轮廓的细节。通过定性和定量实验评估了所提出的方法。实验结果表明，REU-Net 在 HUSTS、MoNuSeg、CoNSep 和 CPM-17 数据集上的表现优于最先进的方法。
