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9. Automatic analysis of lung adenocarcinoma histology whole slide images

Author

Alsubaie, Najah Mohammed

Subjects
004, QA76 Electronic computers. Computer science. Computer software, RC0254 Neoplasms. Tumors. Oncology (including Cancer)
Abstract

Histology is the backbone in the diagnosis and prognosis pipeline of most types of cancer, especially lung adenocarcinoma (LUAD). However, a pathologist's assessment of histology slides is often subjective, semi-quantitative, and limited to selected regions of the tumour. Recently, automatic tools are being widely employed by utilising digital slide scanners. These tools exploit a large amount of information captured from the tumour which could not be achieved by human observers. In addition, the automatic tools enable an objective and reproducible way of conducting the clinical experiments, and provide the opportunity to discover new potential image-based features. Utilising these features could provide a second opinion for the pathologists in different tasks such as distinguishing between various types of lung cancer, predicting possible metastases and others. Therefore, a solid foundation is provided towards objective, comparable histology assessments, and personalised treatment of the disease. In this thesis, we examine different stages of LUAD automatic analysis. We begin with preprocessing of the WSI, then cell analysis, and finally tumour morphology analysis. We use scanned slides of tissue sections stained with Haematoxylin and Eosin (H&E). In the first part of the thesis, we start with preprocessing of histology images. We propose two methods for stain deconvolution: First, a supervised method where a classifier is trained to distinguish between different stain colours. We transfer the stain colours into another chromatic colour space such that the intensity variations between pixels of one stain colour are minimised. Second, we present an unsupervised method which directly estimates the separation between different stain colours by filtering the image such that the dependency between the stain colours is reduced. In the second part of the thesis, we propose two novel automatic tools to quantify the heterogeneity of tumour cells and tissue morphology. The first tool performs WSI analysis of cellular features by extracting statistics from the WSI heat map and then examining these statistics to find the correlation with survival of LUAD patients. In the second tool, we propose an automatic method for quantification of LUAD growth patterns in WSI using a deep-learning-based method. The proposed framework is applied to automatically locate and classify tumour growth patterns within the WSI. Finally, we quantify the percentages of each pattern and analyse the impact of these percentages on the survival of LUAD.

Published
2018

14. A Multi-resolution Deep Learning Framework for Lung Adenocarcinoma Growth Pattern Classification

Author

Alsubaie, Najah, Shaban, Muhammad, Snead, David, Khurram, Ali, Rajpoot, Nasir, Barbosa, Simone Diniz Junqueira, Series Editor, Filipe, Joaquim, Series Editor, Kotenko, Igor, Series Editor, Sivalingam, Krishna M., Series Editor, Washio, Takashi, Series Editor, Yuan, Junsong, Series Editor, Zhou, Lizhu, Series Editor, Nixon, Mark, editor, Mahmoodi, Sasan, editor, and Zwiggelaar, Reyer, editor

Published
2018
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34. Automatic analysis of lung adenocarcinoma histology whole slide images

Author

Alsubaie, Najah Mohammed

Subjects
RC0254, QA76
Abstract

Histology is the backbone in the diagnosis and prognosis pipeline of most types of cancer, especially lung adenocarcinoma (LUAD). However, a pathologist's assessment of histology slides is often subjective, semi-quantitative, and limited to selected regions of the tumour. Recently, automatic tools are being widely employed by utilising digital slide scanners. These tools exploit a large amount of information captured from the tumour which could not be achieved by human observers. In addition, the automatic tools enable an objective and reproducible way of conducting the clinical experiments, and provide the opportunity to discover new potential image-based features. Utilising these features could provide a second opinion for the pathologists in different tasks such as distinguishing between various types of lung cancer, predicting possible metastases and others. Therefore, a solid foundation is provided towards objective, comparable histology assessments, and personalised treatment of the disease. \ud \ud In this thesis, we examine different stages of LUAD automatic analysis. We begin with preprocessing of the WSI, then cell analysis, and finally tumour morphology analysis. We use scanned slides of tissue sections stained with Haematoxylin and Eosin (H&E). In the first part of the thesis, we start with preprocessing of histology images. We propose two methods for stain deconvolution: First, a supervised method where a classifier is trained to distinguish between different stain colours. We transfer the stain colours into another chromatic colour space such that the intensity variations between pixels of one stain colour are minimised. Second, we present an unsupervised method which directly estimates the separation between different stain colours by filtering the image such that the dependency between the stain colours is reduced. \ud \ud In the second part of the thesis, we propose two novel automatic tools to quantify the heterogeneity of tumour cells and tissue morphology. The first tool performs WSI analysis of cellular features by extracting statistics from the WSI heat map and then examining these statistics to find the correlation with survival of LUAD patients. In the second tool, we propose an automatic method for quantification of LUAD growth patterns in WSI using a deep-learning-based method. The proposed framework is applied to automatically locate and classify tumour growth patterns within the WSI. Finally, we quantify the percentages of each pattern and analyse the impact of these percentages on the survival of LUAD.

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