An open source pipeline for quantitative immunohistochemistry image analysis of inflammatory skin disease using artificial intelligence. (3rd December 2022) - Ding, Yuchun; Dhawan, Gaurav; Jones, Claire; Ness, Thomas; Nichols, Esme; Krasnogor, Natalio; Reynolds, Nick J.
Abstract: Background: The application of artificial intelligence (AI) to whole slide images has the potential to improve research reliability and ultimately diagnostic efficiency and service capacity. Image annotation plays a key role in AI and digital pathology. However, the work‐streams required for tissue‐specific (skin) and immunostain‐specific annotation has not been extensively studied compared with the development of AI algorithms. Objectives: The objective of this study is to develop a common workflow for annotating whole slide images of biopsies from inflammatory skin disease immunostained with a variety of epidermal and dermal markers prior to the development of the AI‐assisted analysis pipeline. Methods: A total of 45 slides containing 3–5 sections each were scanned using Aperio AT2 slide scanner (Leica Biosystems). These slides were annotated by hand using a commonly used image analysis tool which resulted in more than 4000 images blocks. We used deep learning (DL) methodology to first sequentially segment (epidermis and upper dermis), with the exclusion of common artefacts and second to quantify the immunostained signal in those two compartments of skin biopsies and the ratio of positive cells. Results: We validated two DL models using 10‐fold validation runs and by comparing to ground truth manually annotated data. The models achieved an average (global) accuracy of 95.0% for the segmentation of epidermis and dermis and 86.1% for the segmentation of positive/negative cells. Conclusions: The application of two DL models in sequence facilitates accurate segmentation of epidermal and dermal structures, exclusion of common artefacts and enables the quantitative analysis of the immunostained signal. However, inaccurate annotation of the slides for training the DL model can decrease the accuracy of the output. Our open source code will facilitate further external validation across different immunostaining platforms and slide scanners.