The integrated Positron Emission Tomography and Computed Tomography (PET-CT) has become an indispensable tool in modern cancer therapy. Accurate target delineation is an inevitable first step towards fully making use of the potentials of PET-CT. However, in current clinical practice, this important task is typically performed visually on a slice-by-slice basis with very limited support of automated segmentation tools. The state-of-the-art PET-CT segmentation techniques rely on either a single modality or the fused PET-CT data, which may not fully take advantage of both modalities, thus compromising the segmentation accuracy. In addition, the state-of-the-art therapeutic response prediction methods highly rely on the handcrafted image features and parameters, which poses a limiting factor for their wide use in clinic. This research proposes to develop fast and objective PET-CT analysis methods to facilitate the utilization of the dual modality imaging for both large-scale clinical trial research and daily clinical care. The novel feature of the proposed methods is the first time to introduce co-segmentation for PET-CT tumor delineation, which recognizes the contour difference of tumors in PET from those in CT. New PET-CT specific priors will be explored and incorporated into the segmentation framework, further improving the accuracy of segmentation. The proposed response prediction method is built on the accurate tumor definition from our PET-CT co- segmentation approach, with an innovative design of a convolutional neural network for automatically learning hierarchical features directly from the PET-CT scans, leading to highly accurate prediction of response. The developed methods will be tested in comparison with state-of-the-art methods utilized today. The performance of the methods will be statistically assessed in data samples of sufficient sizes.
This study addresses important problems of PET-CT image segmentation and therapeutic response prediction that are vital to many applications of PET-CT in modern cancer therapy. The methods developed in this project will greatly accelerate the pace of PET-CT image processing and thus help fulfill the compelling clinical needs of analyzing a large amount of volumetric PET-CT data, potentially improving clinical outcome.
