Quantitative histology for assessing organ health

Thesis

This thesis aims to take the first step in developing a quantitative analysis workflow for characterising histological changes in pre-implantation renal biopsies. Solving this is highly beneficial as there is a significant disparity between organ supply and demand worldwide. It can also help to alleviate the workload caused by a shortage of pathologists. This work would allow quantitative measurement of histological features related to chronic and acute injuries in pre-implantation biopsies. These features can then be linked to scores given by pathologists, post-transplant clinical endpoints, and proteomic biomarkers to help researchers better understand the mechanisms of organ injury and repair.

First, we build the main feature extraction workflow based on convolutional neural networks (CNN) to automatically segment different tissue compartments in renal biopsies. We design handcrafted features and qualitatively show that some of these features can capture visual changes related to chronic and acute injuries.

Then, we find that many tissue compartments in the primary dataset have artefacts not present in the training data. We discuss Bayesian Neural Networks (BNNs) to quantify uncertainties in segmentation and propose a way to improve histological estimates.

Next, we propose using tissue compartment-based features to improve classification and interpretability in a multi-instance learning setting. In addition to learned attention, tissue compartments are weighted by segmentation quality to reduce the impact of instances that bear little resemblance to the training data.

Finally, we combine techniques developed in this project to detect acute injuries from biopsies. We propose an algorithmic score consisting of models trained on pathologists’ assessments and donors’ clinical states. The score is significantly associated with Delayed Graft Function (DGF) in the test data.

These findings suggest that our workflow can facilitate research in kidney transplant where pathologist time is limited. It also shows histology has great potential in predicting post-transplant complications.

Authors: Tam, KH

• DOI: 10.5287/ora-2akk4qgjw
• Type of award: DPhil
• Level of award: Doctoral
• Awarding institution: University of Oxford
• Language: English
• Keywords: histopathology; transplant; computer vision; histology; machine learning; image analysis; deep learning
• Subjects: Transplantation of organs, tissues, etc.; Deep learning (Machine learning); Computer vision; Image analysis; Tissue banks; Biomedical engineering