Human papilloma virus detection in oropharyngeal carcinomas with in situ hybridisation using hand crafted morphological features and deep central attention residual networks

Fouad, Shereen and Gabriel, Landini and Robinson, Max and Song, Tzu-Hsi and Mehanna, Hisham (2021) Human papilloma virus detection in oropharyngeal carcinomas with in situ hybridisation using hand crafted morphological features and deep central attention residual networks. Computerized Medical Imaging and Graphics, 88 (3). p. 101853. ISSN 0895-6111

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Abstract

Human Papilloma Virus (HPV) is a major risk factor for the development of oropharyngeal cancer.
Automatic detection of HPV in digitized pathology tissues using \textit{in situ} hybridisation (ISH) is a difficult task due to the variability and complexity of staining patterns as well as the presence of imaging and staining artefacts. This paper proposes an intelligent image analysis framework to determine HPV status in digitized samples of oropharyngeal cancer tissue micro-arrays (TMA).
The proposed pipeline mixes handcrafted feature extraction with a deep learning for epithelial region segmentation as a preliminary step.
We apply a deep central attention learning technique to segment epithelial regions and within those assess the presence of regions representing ISH products. We then extract relevant morphological measurements from those regions which are then input into a supervised learning model for the identification of HPV status.
The performance of the proposed method has been evaluated on 2,009 TMA images of oropharyngeal carcinoma tissues captured with a $\times$20 objective.
The experimental results show that our technique provides around 91\% classification accuracy in detecting HPV status when compared with the histopatholgist gold standard.
We also tested the performance of end-to-end deep learning classification methods to assess HPV status by learning directly from the original ISH processed images, rather than from the handcrafted features extracted from the segmented images. We examined the performance of sequential convolutional neural networks (CNN) architectures including {three popular image recognition networks (VGG-16, ResNet and Inception V3) in their pre-trained and trained from scratch versions, however their highest classification accuracy was inferior (78\%) to the hybrid pipeline presented here}.

Item Type:	Article
Identification Number:	10.1016/j.compmedimag.2021.101853
Dates:	Date Event 2 January 2021 Accepted 22 January 2021 Published Online
Uncontrolled Keywords:	Histology Human papilloma virus In situ hybridisation Deep learning Machine learning
Subjects:	CAH02 - subjects allied to medicine > CAH02-05 - medical sciences > CAH02-05-01 - medical technology CAH11 - computing > CAH11-01 - computing > CAH11-01-05 - artificial intelligence
Divisions:	Faculty of Computing, Engineering and the Built Environment > College of Computing
Depositing User:	Shereen Fouad
Date Deposited:	26 Jan 2021 09:24
Last Modified:	22 Jan 2022 03:00
URI:	https://www.open-access.bcu.ac.uk/id/eprint/10700

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