No abstract

The segmentation of cellular membranes is essential for getting crucial information in diagnosing several cancers, including lung, breast, colon, gastric cancer, etc. Manual segmentation of cellular membranes is a tedious, time-consuming routine and prone to error and inter-observer variation. So, it is one of the challenges that pathologists face in immunohistochemical (IHC) tissue images. Although automated segmentation of cellular membranes has recently gained considerable attention in digital pathology applications, little research is based on machine learning approaches. Therefore, this study proposes a deep framework for semantic segmenting cellular membranes using an end-to-end trainable Convolutional Neural Network (CNN) based on encoder and decoder architecture with Atreus Spatial Pyramid Pooling (ASPP). The backbone of the encoder depends on the residual architecture. The performance of the proposed framework was evalu ated and compared to other benchmark methods. As a result, we show that the proposed framework exhibits significant potential for cellular membranes segmentation in IHC images.