Multi-Path Dilated Residual Network for Nuclei Segmentation and Detection
Multi-Path Dilated Residual Network for Nuclei Segmentation and Detection
复制标题
用于细胞核分割和检测的多路径扩张残差网络
DOI：
10.3390/cells8050499
复制
发表时间：
2019-05-01
期刊：
影响因子：
6
通讯作者：
Zhe, Nie
中科院分区：
文献类型：
--
作者：
Wang, Eric Ke;Zhang, Xun;Zhe, Nie
细胞核检测作为一种典型的生物医学检测任务，在人类健康管理、疾病诊断等领域有着广泛的应用。然而，显微图像中的细胞检测的任务仍然是具有挑战性的，因为细胞核通常是小而密集的，在图像中有许多重叠的细胞核。为了检测细胞核，最重要的关键步骤是准确地分割细胞目标。基于Mask RCNN模型，我们设计了一个多路径扩张残差网络，实现了一种分割和检测密集小目标的网络结构，有效解决了深度神经网络中小目标信息丢失的问题。在两个典型核分割数据集上的实验结果表明，该模型对密集小目标具有较好的识别和分割能力。
As a typical biomedical detection task, nuclei detection has been widely used in human health management, disease diagnosis and other fields. However, the task of cell detection in microscopic images is still challenging because the nuclei are commonly small and dense with many overlapping nuclei in the images. In order to detect nuclei, the most important key step is to segment the cell targets accurately. Based on Mask RCNN model, we designed a multi-path dilated residual network, and realized a network structure to segment and detect dense small objects, and effectively solved the problem of information loss of small objects in deep neural network. The experimental results on two typical nuclear segmentation data sets show that our model has better recognition and segmentation capability for dense small targets.