Accurate Segmentation of Ct Pelvic Organs via Incremental Cascade Learning and Regression-based Deformable Models

YAOZONG GAO: ACCURATE SEGMENTATION OF CT PELVIC ORGANS VIA INCREMENTAL CASCADE LEARNING AND REGRESSION-BASED DEFORMABLE MODELS. (Under the direction of Dinggang Shen.) Accurate segmentation of male pelvic organs from computed tomography (CT) images is important in image guided radiotherapy (IGRT) of prostate cancer. The efficacy of radiation treatment highly depends on the segmentation accuracy of planning and treatment CT images. Clinically manual delineation is still generally performed in most hospitals. However, it is time consuming and suffers large inter-operator variability due to the low tissue contrast of CT images. To reduce the manual efforts and improve the consistency of segmentation, it is desirable to develop an automatic method for rapid and accurate segmentation of pelvic organs from planning and treatment CT images. This dissertation marries machine learning and medical image analysis for addressing two fundamental yet challenging segmentation problems in image guided radiotherapy of prostate cancer. • Planning-CT Segmentation. Deformable models are popular methods for planningCT segmentation. However, they are well known to be sensitive to initialization and ineffective in segmenting organs with complex shapes. To address these limitations, this dissertation investigates a novel deformable model named regression-based deformable model (RDM). Instead of locally deforming the shape model, in RDM the deformation