Open Collections

Porisky, Adam

University of British Columbia

Medical image segmentation is an essential part of a many healthcare services. While it is possible for an expert to manually label each pixel or voxel in an image, it is a time-consuming process that lacks reproducibility and doesn’t scale well — a problem that will only get worse as advancements in medical image acquisition yield greater quantities of images with higher resolution. To address this need, automatic deep learning-based segmentation has become a popular research direction. However, due to their complexity, deep learning solutions have a high level of variability both in terms of design and outcomes. In this thesis we present a workflow that leverages the core principles of the PCS framework, an existing set of data science recommendations, to improve the reproducibility and transparency of deep learning medical image segmentation results. We started with a conventional approach for comparison, evaluating the impact of the network depth, filter sizes, and presence of shortcut connections on a convolutional encoder network. We found shortcuts to have the greatest impact (DSC 0.20), while changes to the filter size (DSC 0.04) and the number of layers (DSC 0.03) also affected performance. We then implemented our proposed workflow, incorporating PCS principles, to evaluate the predictability of a broader range of convolutional encoder network architecture and algorithm variations, as well as characterize the performance of those models under reasonable perturbations. By exploring variations in loss functions, filter size, and hidden unit quantity as model perturbations, we determined a subset of models that met a standard of predictability. All of our selected model perturbations performed comparably well in terms of predictability (DSC [0.79 - 0.80]). Stability analysis of these models demonstrated poor hyper-parameter stability when using the sensitivity-specificity ratio as a loss function, and improved stability and distance-based metric performance for 5 x 5 filters. The additional information provided as a result of using our workflow directly improves upon the recommendations derived from the conventional approach.

Text

2023-01-05

Attribution-NonCommercial-NoDerivatives 4.0 International

http://hdl.handle.net/2429/83586

Biomedical Engineering

University of British Columbia

UBCV

http://creativecommons.org/licenses/by-nc-nd/4.0/