Feature‐based rectal contour propagation from planning CT to cone beam CT

Abstract

The purpose of this work is to develop a novel feature-based registration strategy to automatically map the rectal contours from planning computed tomography ͑CT͒ ͑pCT͒ to cone beam CT ͑CBCT͒. The rectal contours were manually outlined on the pCT. A narrow band with the outlined contour as its interior surface was then constructed, so that we can exclude the volume inside the rectum in the registration process. The corresponding contour in the CBCT was found by using a feature-based registration algorithm, which consists of two steps: ͑1͒ automatically searching for control points in the pCT and CBCT based on the features of the surrounding tissue and matching the homologous control points using the scale invariance feature transformation; and ͑2͒ using the control points for a thin plate spline transformation to warp the narrow band and mapping the corresponding contours from pCT to CBCT. The proposed contour propagation technique is applied to digital phantoms and clinical cases and, in all cases, the contour mapping results are found to be clinically acceptable. For clinical cases, the method yielded satisfactory results even when there were significant rectal content changes between the pCT and CBCT scans. As a consequence, the accordance between the rectal volumes after deformable registration and the manually segmented rectum was found to be more than 90%. The proposed technique provides a powerful tool for adaptive radiotherapy of prostate, rectal, and gynecological cancers in the future.

Clinical Evaluation of Soft Tissue Organ Boundary Visualization on Cone-Beam Computed Tomographic Imaging

“…IGART processes also require an automation of structure identification with sufficient image quality to enable reliable mapping of structures. Several methods have therefore been proposed to make CBCT soft tissue information more useful for IGART, by technically reducing imaging artifacts and by developing or applying tools for automated contouring such as feature-based image registration tools for automated rectum contouring (1,9,10,19–21). …”

Comparison of manual and automatic segmentation methods for brain structures in the presence of space-occupying lesions: a multi-expert study

“…Of late, however, a number of semi- and fully-automated methods have been developed to segment normal tissues in a radiotherapy clinical context (Gorthi, Duay, Houhou, Bach Cuadra, Schick, Becker, Allal & Thiran 2009, Malsch, Thieke, Huber & Bendl 2006, Lu, Chen, Olivera, Ruchala & Mackie 2004, Lu, Olivera, Chen, Ruchala, Haimerl, Meeks, Langen & Kupelian 2006, Xie, Chao & Xing 2008, Reed, Woodward, Zhang, Strom, Perkins, Tereffe, Oh, Yu, Bedrosian, Whitman, Bucholz & Dong 2008, Zhang, Chi, Meldolesi & Yan 2007, Pasquier, Lacornerie, Vermandel, Rosseeau, Lartigau & Betrouni 2007, Isambert, Dhermain, Bidault, Commowick, Bondiau, Malandain & Lefkopoulos 2008). Evaluation of these methods has been a persistent challenge as medical image segmentation unfortunately lacks a known ground truth, or gold standard, in its real world application.…”

Applications of edge preservation ratio in image processing

“…In practice, EPRa is more important and preferable than EPRr in IQA analysis, since accurate edge preservation is critical for follow-up visual tasks, such as object segmentation [1,2], feature extraction [3] and image registration [4,5,6]. On the other hand, higher EPRr values validate less introduced structures (in Figure 5).…”

“…Image processing is indispensable in many visual tasks, such as object segmentation [1,2], feature enhancement [3] and image registration [4,5,6]. After processing, image quality is inevitably changed.…”