**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.

**Keywords:** cellular membrane segmentation, immunohistochemistry (IHC) staining images, GLUT-1 protein expression, deep learning approach, Convolutional Neural Network (CNN)

### **1. Introduction**

Immunohistochemistry (IHC) is an efficient staining technique used in pathology to localise a certain antigen in a tissue specimen. Hence, It is now employed in state-of-the-art research to identify specific antigens within a tissue sample from formalin-fixed paraffin-embedded (FFPE) tissue, e.g. in tissue microarrays (TMAs) and 3D dimensional spheroids grown from cells. The cellular membranes segmentation of IHC images is usually required in histopathology to provide more relevant information for diagnosing particular cancers because specific tumour antigens are

expressed in certain cancers. Glucose transporter-1 (GLUT-1) is one of the wellknown biomarkers bound with the cellular membrane that induces and increases hypoxic conditions in different solid tumours, including breast, prostate, and colorectal cancer [1]. The production of Hypoxia-Inducible Factors (HIFs) proteins in tumour hypoxia regions activates GLUT-1 genes that promote hypoxia [2]. Oxygen gradient, supply and distribution in hypoxic areas lead to the difference in size and extension in all solid tumour regions [3]. Tumour hypoxia is a unique hallmark of cancer due to the difference in oxygen demand and supply, which produces cancer stem cell niche, resistance to therapy (chemotherapy and radiotherapy), immune damping, poor clinical prognosis and genomic instability [3, 4]. Currently, tumour hypoxia is receiving significant attention as the centre for the hallmarks of cancer; this is because of its many characteristics of chemotherapy and radiotherapy resistance and a primary prognostic factor [5]. In clinical practice, assessing the development and spread of hypoxia across solid tumours is an essential routine performed by pathologists to describe the appropriate therapy. In the deep hypoxic environment, It is getting more difficult for chemotherapy and radiotherapy to reach tumour sites. Thus, the need for using hypoxia-activated pro-drugs (HAPs) as targeted therapy is needed.

In recent years, Computer-Aided Diagnosis (CAD) technologies have emerged as one of the potential solutions for histopathological image analysis. The CAD technologies have been used to quantitatively and objectively evaluate IHC biomarkers in a whole tissue slide or a specific region of interest delineated by a pathologist. So, they have been employed to assist histopathologists in some laborious routines, such as visual examination of IHC images for scoring and segmenting the cellular membrane. Thus, the advantages of CAD technologies are avoiding inconsistency in the diagnosis among pathologists, improving the diagnosis quality and reducing the diagnostic time. On the other hand, machine learning-based CAD technologies rely heavily on hand-crafted features that can be significantly prone to feature extractor bias. In addition, relevant domain knowledge is necessary to select the valuable features. Thus, hand-crafted techniques can only deal with some low-level information of images. In contrast, deep learning-based CAD techniques are characterised by; 1) Their ability to extract high-level abstract features from images automatically in a standardised way [6, 7], 2) Their ability to analyse entire slides in detail rather than focusing on a region of interest (ROI) [8], 3) Their ability to learn complex mapping functions directly from the input data and 4) Their ability to avoid personal user bias, as it does not require manual extraction of specific visual features [9]. Hence, they deliver unbiased outcomes for dataset images [9]. So, this work proposes deep learning based-segmentation of the cellular membranes in colorectal IHC images.

The remainder of this chapter is organised as follows. Section 2 presents the related works. Section 3 provides the materials, proposed framework and evaluation indices that are used in this work. Section 4 reports the experimental results from the proposed model. Finally, in Section 5, we discuss analyses of the results and conclude the chapter.
