Image segmentation is consistently an important task for computer vision and the analysis of medical images. The analysis and diagnosis of histopathology images by using efficient algorithms that separate hematoxylin and eosin-stained nuclei was the purpose of our proposed method. In this paper, we propose a deep learning model that automatically segments the complex nuclei present in histology images by implementing an effective encoder–decoder architecture with a separable convolution pyramid pooling network (SCPP-Net). The SCPP unit focuses on two aspects: first, it increases the receptive field by varying four different dilation rates, keeping the kernel size fixed, and second, it reduces the trainable parameter by using depth-wise separable convolution. Our deep learning model experimented with three publicly available histopathology image datasets. The proposed SCPP-Net provides better experimental segmentation results compared to other existing deep learning models and is evaluated in terms of F1-score and aggregated Jaccard index.

图像分割一直是计算机视觉和医学图像分析的重要任务。通过使用有效的算法将苏木精和曙红染色的核分开，对组织病理学图像进行分析和诊断是我们提出的方法的目的。在本文中，我们提出了一种深度学习模型，该模型通过使用可分离的卷积金字塔池网络（SCPP-Net）实现有效的编码器-解码器体系结构，自动分割组织学图像中存在的复杂核。SCPP单元着重于两个方面：首先，它通过改变四个不同的膨胀率来增加接收场，保持内核大小不变；其次，它通过使用深度方向可分离卷积来减小可训练参数。我们的深度学习模型对三个公开可用的组织病理学图像数据集进行了实验。与其他现有的深度学习模型相比，拟议的SCPP-Net提供了更好的实验分割结果，并根据F1分数和汇总的Jaccard指数进行了评估。