Interactive semiautomatic contour delineation using statistical conditional random fields framework

• Published in: Medical physics (Lancaster) 2012-07, Vol.39 (7), p.4547-4558
• Main Authors: Hu, Yu-Chi, Grossberg, Michael D., Wu, Abraham, Riaz, Nadeem, Perez, Carmen, Mageras, Gig S.
• Format: Article
• Language: English
• Subjects: Anatomy; Artificial Intelligence; Computed tomography; Computerised tomographs; computerised tomography; conditional random fields; Data Interpretation, Statistical; Digital computing or data processing equipment or methods, specially adapted for specific applications; graph cut; Humans; Image data processing or generation, in general; image segmentation; interpolation; Interpolation; curve fitting; kidney; Kidneys; liver; Medical image contrast; medical image processing; Medical image segmentation; Medical imaging; minimisation; Numerical optimization; Pattern Recognition, Automated - methods; phantoms; Phantoms, Imaging; Probability theory, stochastic processes, and statistics; radiation therapy; Radiation Therapy Physics; Radiation treatment; radiation treatment planning; Radiographic Image Enhancement - methods; Radiographic Image Interpretation, Computer-Assisted - methods; Reproducibility of Results; Segmentation; Sensitivity and Specificity; Single‐slice; statistics; Tissues; Tomography, X-Ray Computed - instrumentation; Tomography, X-Ray Computed - methods; Treatment planning; User-Computer Interface
• ISSN: 0094-2405; 2473-4209; 0094-2405
• DOI: 10.1118/1.4728979

Purpose: Contouring a normal anatomical structure during radiation treatment planning requires significant time and effort. The authors present a fast and accurate semiautomatic contour delineation method to reduce the time and effort required of expert users. Methods: Following an initial segmentation on one CT slice, the user marks the target organ and nontarget pixels with a few simple brush strokes. The algorithm calculates statistics from this information that, in turn, determines the parameters of an energy function containing both boundary and regional components. The method uses a conditional random field graphical model to define the energy function to be minimized for obtaining an estimated optimal segmentation, and a graph partition algorithm to efficiently solve the energy function minimization. Organ boundary statistics are estimated from the segmentation and propagated to subsequent images; regional statistics are estimated from the simple brush strokes that are either propagated or redrawn as needed on subsequent images. This greatly reduces the user input needed and speeds up segmentations. The proposed method can be further accelerated with graph-based interpolation of alternating slices in place of user-guided segmentation. CT images from phantom and patients were used to evaluate this method. The authors determined the sensitivity and specificity of organ segmentations using physician-drawn contours as ground truth, as well as the predicted-to-ground truth surface distances. Finally, three physicians evaluated the contours for subjective acceptability. Interobserver and intraobserver analysis was also performed and Bland–Altman plots were used to evaluate agreement. Results: Liver and kidney segmentations in patient volumetric CT images show that boundary samples provided on a single CT slice can be reused through the entire 3D stack of images to obtain accurate segmentation. In liver, our method has better sensitivity and specificity (0.925 and 0.995) than region growing (0.897 and 0.995) and level set methods (0.912 and 0.985) as well as shorter mean predicted-to-ground truth distance (2.13 mm) compared to regional growing (4.58 mm) and level set methods (8.55 mm and 4.74 mm). Similar results are observed in kidney segmentation. Physician evaluation of ten liver cases showed that 83% of contours did not need any modification, while 6% of contours needed modifications as assessed by two or more evaluators. In interobserver and intraobse