title: Performance evaluation of automatic anatomy segmentation algorithm on repeat or four-dimensional computed tomography images using deformable image registration method.
abstract: OBJECTIVE
  Auto-propagation of anatomic regions of interest from the planning computed tomography (CT) scan to the daily CT is an essential step in image-guided adaptive radiotherapy. The goal of this study was to quantitatively evaluate the performance of the algorithm in typical clinical applications.
  
  
  METHODS
  We had previously adopted an image intensity-based deformable registration algorithm to find the correspondence between two images. In the present study, the regions of interest delineated on the planning CT image were mapped onto daily CT or four-dimensional CT images using the same transformation. Postprocessing methods, such as boundary smoothing and modification, were used to enhance the robustness of the algorithm. Auto-propagated contours for 8 head-and-neck cancer patients with a total of 100 repeat CT scans, 1 prostate patient with 24 repeat CT scans, and 9 lung cancer patients with a total of 90 four-dimensional CT images were evaluated against physician-drawn contours and physician-modified deformed contours using the volume overlap index and mean absolute surface-to-surface distance.
  
  
  RESULTS
  The deformed contours were reasonably well matched with the daily anatomy on the repeat CT images. The volume overlap index and mean absolute surface-to-surface distance was 83% and 1.3 mm, respectively, compared with the independently drawn contours. Better agreement (>97% and <0.4 mm) was achieved if the physician was only asked to correct the deformed contours. The algorithm was also robust in the presence of random noise in the image.
  
  
  CONCLUSIONS
  The deformable algorithm might be an effective method to propagate the planning regions of interest to subsequent CT images of changed anatomy, although a final review by physicians is highly recommended.
authors: Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, TX 77030-4009, USA.
authors: He
authors: H
authors: Wang
authors: Adam S
authors: AS
authors: Garden
authors: Lifei
authors: L
authors: Zhang
authors: Xiong
authors: X
authors: Wei
authors: Anesa
authors: A
authors: Ahamad
authors: Deborah A
authors: DA
authors: Kuban
authors: Ritsuko
authors: R
authors: Komaki
authors: Jennifer
authors: J
authors: O'Daniel
authors: Yongbin
authors: Y
authors: Zhang
authors: Radhe
authors: R
authors: Mohan
authors: Lei
authors: L
authors: Dong
mesh_terms: D000465:Algorithms; D006258:Head and Neck Neoplasms / Q000000981:diagnostic imaging / Q000532:radiotherapy; D006801:Humans; D008175:Lung Neoplasms / Q000000981:diagnostic imaging / Q000532:radiotherapy; D008297:Male; D009369:Neoplasms / Q000000981:diagnostic imaging* / Q000532:radiotherapy; D011471:Prostatic Neoplasms / Q000000981:diagnostic imaging / Q000532:radiotherapy; D011873:Radiology Information Systems; D011880:Radiotherapy Planning, Computer-Assisted / Q000379:methods* / Q000592:standards; D014057:Tomography, X-Ray Computed / Q000379:methods*