Deep convolutional neural network based analysis methods for radiation therapy applications
Creators: name: Xiong, Xiaofan
Publisher: name: University of Iowa
Year: 2024
Subjects: deep learning
image processing
PET-CT
radiomics
segmentation
public abstract
Radiation Oncology

description: Medical imaging is crucial for many modern clinical applications such as diagnostics, disease progression monitoring, and treatment planning. For a large number of these applications, image segmentation serve as the first processing step, followed by quantitative image feature extraction to assess size, volume, shape characteristics, and intensity-related features. These quantitative features provide valuable information for interpretation of pathology and prognostics. However, the segmentation process is mostly done manually, which is time-consuming and lacks consistency. Automated and semi-automated methods have been proposed over the years to increase the efficiency, consistency, and accuracy of segmentation. Among the many methods, there are simple ones like thresholding, region-growing, watershed and more advanced ones like active contour methods, active shape model, and more recently, deep learning-based methods. Deep learning models have yielded a new generation of image segmentation methods with remarkable performance improvements, typically beating standard and traditional machine learning methods in various benchmarks. Another branch of medical image analysis is radiomics. A radiomics approach refers to methods that extracts features from medical images using data-characterisation algorithms, which can be utilized to discover and quantify characteristics of tumors. In this field, deep learning-based approaches are also appearing, showing promising improvements compared to using handcrafted features.
description: This thesis aims to investigate and develop deep learning-based methods for the use in radiation oncology to facilitate the efficient analysis of volumetric medical image datasets. In particular, the segmentation performance of several state-of-the-art convolutional neural networks (CNN) and more recent transformer-based models for segmenting both normal and abnormal structures in PET images were studied and compared. For cerebellum segmentation, we discovered a modified U-Net that has significantly better segmentation performance than the original U-Net and V-Net. For head and neck cancer (HNC) lesion segmentation, we proposed a U-Net-CBAM that slightly outperforms the U-Net and several state-of-the-art transformer-based models. We also proposed a combined localization and segmentation approach for efficient segmentation of the pelvis in CT images with variant volume sizes and image qualities, achieving up to four times faster processing speed compared to a standard approach. Furthermore, this single object combined localization and segmentation approach is extended to process multiple objects in a scan at once. Specifically, the algorithm is applied and studied in the context of lumbar and thoracic vertebrae segmentation. In addition, we explored the performance of using CNN as well as a transformer-based models for HNC outcome prediction and found that the transformer-based model shows the best prognostic performance.
description: In conclusion, our joint localization and segmentation approach for pelvis and lumbar and thoracic vertebrae demonstrated a novel way of utilizing CNNs to substantially improve the segmentation efficiency. We assessed the segmentation performance difference of several CNN and transformer based models on two different data sets. The difference can be substantial. Therefore, testing different models before starting a new project is imperative. In addition, comparison between CNN models and transformer based models on HNC outcome prediction performance showed the potential of transformer based models for radiomics applications. Furthermore, annotation quality, data size and class balance were found to play an important role in deep learning-based segmentation and classification performance, respectively.