Atlas ranking and selection for automatic segmentation of the esophagus from CT scans. (14th November 2017)

• Record Type: Journal Article
• Authors: Yang, Jinzhong; Haas, Benjamin; Fang, Raymond; Beadle, Beth M; Garden, Adam S; Liao, Zhongxing; Zhang, Lifei; Balter, Peter; Court, Laurence
• Is Part Of: Physics in medicine & biology. Volume 62:Number 23(2017:Dec.)
• Page Start: 9140
• Page End: 9158
• Publication Date: 2017-11-14
• Subjects: atlas selection -- multi-atlas segmentation -- esophagus -- radiotherapy; Biophysics -- Periodicals; Medical physics -- Periodicals; 610.153
• DOI: 10.1088/1361-6560/aa94ba
• Languages: English
• ISSNs: 0031-9155

Abstract

Abstract: In radiation treatment planning, the esophagus is an important organ-at-risk that should be spared in patients with head and neck cancer or thoracic cancer who undergo intensity-modulated radiation therapy. However, automatic segmentation of the esophagus from CT scans is extremely challenging because of the structure's inconsistent intensity, low contrast against the surrounding tissues, complex and variable shape and location, and random air bubbles. The goal of this study is to develop an online atlas selection approach to choose a subset of optimal atlases for multi-atlas segmentation to the delineate esophagus automatically. We performed atlas selection in two phases. In the first phase, we used the correlation coefficient of the image content in a cubic region between each atlas and the new image to evaluate their similarity and to rank the atlases in an atlas pool. A subset of atlases based on this ranking was selected, and deformable image registration was performed to generate deformed contours and deformed images in the new image space. In the second phase of atlas selection, we used Kullback–Leibler divergence to measure the similarity of local-intensity histograms between the new image and each of the deformed images, and the measurements were used to rank the previously selected atlases. Deformed contours were overlapped sequentially, from the most to the least similar, and the overlap ratio was examined. We further identified a subset of optimal atlases by analyzing the variation of the overlap ratio versus the number of atlases. The deformed contours from these optimal atlases were fused together using a modified simultaneous truth and performance level estimation algorithm to produce the final segmentation. The approach was validated with promising results using both internal data sets (21 head and neck cancer patients and 15 thoracic cancer patients) and external data sets (30 thoracic patients).