Segmentation of Cone Beam CT in Stereotactic Radiosurgery

Sammanfattning: C-arm Cone Beam CT (CBCT) systems – due to compact size, flexible geometry and low radiation exposure – inaugurated the era of on-board 3D image guidance in therapeutic and surgical procedures. Leksell Gamma Knife Icon by Elekta introduced an integrated CBCT system to determine patient position prior to surgical session, thus advancing to a paradigm shift in facilitating frameless stereotactic radiosurgeries. While CBCT offers a quick imaging facility with high spatial accuracy, the quantitative values tend to be distorted due to various physics based artifacts such as scatter, beam hardening and cone beam effect. Several 3D reconstruction algorithms targeting these artifacts involve an accurate and fast segmentation of craniofacial CBCT images into air, tissue and bone. The objective of the thesis is to investigate the performance of deep learning based convolutional neural networks (CNN) in relation to conventional image processing and machine learning algorithms in segmenting CBCT images. CBCT data for training and testing procedures was provided by Elekta. A framework of segmentation algorithms including multilevel automatic thresholding, fuzzy clustering, multilayer perceptron and CNN is developed and tested against pre-defined evaluation metrics carrying pixel-wise prediction accuracy, statistical tests and execution times among others. CNN has proven its ability to outperform other segmentation algorithms throughout the evaluation metrics except for execution times. Mean segmentation error for CNN is found to be 0.4% with a standard deviation of 0.07%, followed by fuzzy clustering with mean segmentation error of 0.8% and a standard deviation of 0.12%. CNN based segmentation takes 500s compared to multilevel thresholding which requires ~1s on similar sized CBCT image. The present work demonstrates the ability of CNN in handling artifacts and noise in CBCT images and maintaining a high semantic segmentation performance. However, further efforts targeting CNN execution speed are required to utilize the segmentation framework within real-time 3D reconstruction algorithms.