Introduction: The placenta is a complex, disk-shaped organ vital to a successful pregnancy and responsible for materno-fetal exchange of vital gases and biochemicals. Instances of compromised placental development or function – collectively termed placenta dysfunction - underlies the most common and devastating pregnancy complications observed in North America, including preeclampsia (PE) and fetal growth restriction (FGR). A comprehensive histopathology examination of the placenta following delivery can help clarify obstetrical disease etiology and progression and offers tremendous potential in the identification of patients at risk of recurrence in subsequent pregnancies, as well as patients at high risk of chronic diseases in later life. However, these types of examinations require a high degree of specialized training and are resource intensive, limiting their availability to tertiary care centers in large city centres. The development of machine learning algorithms tailored to placenta histopathology applications may allow for automation and/or standardization of this important clinical exam – expanding its appropriate usage and impact on the health of mothers and infants. The primary objective of the current project is to develop and pilot the use of machine learning models capable of placental disease classification using digital histopathology images of the placenta.
Methods: 1) A systematic review was conducted to identify the current methods being applied to automate histopathology screening to inform experimental design for later components of the project. Of 230 peer-reviewed articles retrieved in the search, 18 articles met all inclusion criteria and were used to develop guidelines for best practices. 2) To facilitate machine learning model development on placenta histopathology samples, a villi segmentation algorithm was developed to aid with feature extraction by providing objective metrics to automatically quantify microscopic placenta images. The segmentation algorithm applied colour clustering and a tophat transform to delineate the boundaries between neighbouring villi. 3) As a proof-of-concept, 2 machine learning algorithms were tested to evaluated their ability to predict the clinical outcome of preeclampsia (PE) using placental histopathology specimens collected through the Research Centre for Women’s and Infant’s Health (RCWIH) BioBank. The sample set included digital images from 50 cases of early onset PE, 29 cases of late onset PE and 69 controls with matching gestational ages. All images were pre-processed using patch extraction, colour normalization, and image transformations. Features of interest were extracted using: a) villi segmentation algorithm; b) SIFT keypoint descriptors (textural features); c) integrated feature extraction (in the context of deep learning model development). Using the different methods of feature extraction, two different machine learning approaches were compared - Support Vector Machine (SVM) and Convolutional Neural Network (CNN, deep learning). To track model improvement during training, cross validation on 20% of the total dataset was used (deep learning algorithm only) and the trained algorithms were evaluated on a test dataset (20% of the original dataset previously unseen by the model).
Results: From the systematic review, 5 key steps were found to be essential for machine learning model development on histopathology images (image acquisition and preparation, image preprocessing, feature extraction, pattern recognition and classification model training, and model testing) and recommendations were provided for the optimal methods for each of the 5 steps. The segmentation algorithm was able to correctly identify individual villi with an F1 score of 80.76% - a significantly better performance than recently published methods. A maximum accuracy of 73% for the machine learning experiments was obtained when using textural features (SIFT keypoint descriptors) in an SVM model, using onset of PE disease (early vs. late) as the output classification of interest.
Conclusion: Three major outcomes came of this project: 1) the range of methods available to develop automated screening tools for histopathology images with machine learning were consolidated and a set of best practices were proposed to guide future projects, 2) a villi segmentation tool was developed that can automatically segment all individual villi from an image and extract biologically relevant features that can be used in machine learning model development, and 3) a prototype machine learning classification tool for placenta histopathology was developed that was able to achieve moderate classification accuracy when distinguishing cases of early onset PE and late onset PE cases from controls. The collective body of work has made significant contributions to the fields of placenta pathology and computer vision, laying the foundation for significant progress aimed at integrating machine learning tools into the clinical setting of perinatal pathology.
| Identifer | oai:union.ndltd.org:uottawa.ca/oai:ruor.uottawa.ca:10393/42731 |
| Date | 23 September 2021 |
| Creators | Mukherjee, Anika |
| Contributors | Bainbridge-Whiteside, Shannon, Grynspan, David |
| Publisher | Université d'Ottawa / University of Ottawa |
| Source Sets | Université d’Ottawa |
| Language | English |
| Detected Language | English |
| Type | Thesis |
| Format | application/pdf |
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