Deep Learning for Treatment Planning in Chronic Obstructive Pulmonary Diseases

W. Xie

• Promotor: B. van Ginneken
• Copromotor: C. Jacobs
• Graduation year: 2023
• Radboud University, Nijmegen

Abstract

In Chapter 1, we introduced chronic obstructive pulmonary disease (COPD) and gave background information on COPD diagnosis and treatment planning. We described the role of quantitative CT analysis in COPD treatment planning. Furthermore, we provided a short history of image analysis, from applying low-level image processing to deep learning-based CT analysis, explaining the reason behind deep learning prosperity on the road to being data-driven.

In Chapter 2, we presented a novel method using relational two-stage convolu-tion neural networks for segmenting pulmonary lobes in CT images. The proposed method uses a non-local neural network to capture visual and geometric correspondence between high-level convolution features, which represents the relationships between objects and object parts. Our results demonstrate that learning feature correspondence improves the lobe segmentation performance substantially than the baseline on the COPD and the COVID-19 data set.

In Chapter 3, we presented a method for labeling segmental airways given a segmented airway tree. First, we train a convolution neural network to extract features for representing airway branches. Then, these features are iteratively enriched in agraph neural network by collecting information from neighbors, where the graph is based on the airway tree connectivity. Furthermore, we leverage positional information in our graph neural network, where the position of each branch is encoded by its topological distance to a set of anchor branches. As a result, the learned features are structure- and position-aware, contributing to substantially improved branch classification results compared with methods that use only convolution features or standard graph neural networks.

In Chapter 4, we proposed a novel weakly-supervised segmentation framework trained end-to-end, using only image-level supervision. We show that this approach can produce high-resolution segmentation maps without voxel-level annotations.The proposed method substantially outperforms other weakly-supervised methods,although a gap with the fully-supervised performance remains. Our method trained a segmentation network to predict per-image lesion percentage. We made this training possible by proposing an interval regression loss, given only the upper and lower bound of the target percentage, not the exact percentage as supervision. Furthermore, we stabilized the regression training using equivariant regularization. In the refinement process, we proposed an attention neural network module that updated activation maps in one location using nearby activations, acting similar to random walkers, and seeded regional growth in standard post-processing pipelines, yet ours is trained end-to-end.

In Chapter 5, we expanded on the method outlined in Chapter 4 to predict emphysema subtypes. Our proposed algorithm generates high-resolution emphysema segmentation maps to aid in interpreting the prediction process, offering an improved model interpretability compared to the baseline. To predict both subtypes together, we employ the overlapping loss to ensure that each voxel is only assigned to onesubtype (centrilobular or paraseptal). We also use low-attenuation areas in the lung(LAA-950) as visual cues in regression training, providing the network with localized information. Our approach generates categorical visual scores, estimated emphysema percentages, and high-resolution segmentation maps for both centrilobularand paraseptal subtypes, making it more versatile than the baseline approach.

Finally, in Chapter 6, we reflected on this thesis's main findings and contributions.We also analyzed the advances and impact in the field and the existing limitations of the proposed methods. Additionally, we provided a future outlook for research opportunities in the field of deep learning for medical image analysis.