Human papilloma virus detection in oropharyngeal carcinomas with in situ hybridisation using hand crafted morphological features and deep central attention residual networks
Copyright © 2021 Elsevier Ltd. All rights reserved..
Human Papilloma Virus (HPV) is a major risk factor for the development of oropharyngeal cancer. Automatic detection of HPV in digitized pathology tissues using 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 2009 TMA images of oropharyngeal carcinoma tissues captured with a ×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.
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2021 |
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| Erschienen: |
2021 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:88 |
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| Enthalten in: |
Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society - 88(2021) vom: 01. März, Seite 101853 |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Fouad, Shereen [VerfasserIn] |
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| Links: |
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| Themen: |
Deep learning |
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| Anmerkungen: |
Date Completed 25.10.2021 Date Revised 23.02.2022 published: Print-Electronic Citation Status MEDLINE |
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| doi: |
10.1016/j.compmedimag.2021.101853 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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NLM320701751 |
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| 520 | |a Copyright © 2021 Elsevier Ltd. All rights reserved. | ||
| 520 | |a Human Papilloma Virus (HPV) is a major risk factor for the development of oropharyngeal cancer. Automatic detection of HPV in digitized pathology tissues using 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 2009 TMA images of oropharyngeal carcinoma tissues captured with a ×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 | ||
| 650 | 4 | |a Journal Article | |
| 650 | 4 | |a Research Support, Non-U.S. Gov't | |
| 650 | 4 | |a Deep learning | |
| 650 | 4 | |a Histology | |
| 650 | 4 | |a Human papilloma virus | |
| 650 | 4 | |a In situ hybridisation | |
| 650 | 4 | |a Machine learning | |
| 700 | 1 | |a Landini, Gabriel |e verfasserin |4 aut | |
| 700 | 1 | |a Robinson, Max |e verfasserin |4 aut | |
| 700 | 1 | |a Song, Tzu-Hsi |e verfasserin |4 aut | |
| 700 | 1 | |a Mehanna, Hisham |e verfasserin |4 aut | |
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