Combining multiple FDG‐PET radiotherapy target segmentation methods to reduce the effect of variable performance of individual segmentation methods. Issue 4 (11th March 2013)

• Record Type: Journal Article
• Authors: McGurk, Ross J.; Bowsher, James; Lee, John A; Das, Shiva K.
• Is Part Of: Medical physics. Volume 40:Issue 4(2013)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2013-03-11
• Subjects: Positron emission tomography (PET) -- Computed tomography -- Probability theory -- Image quality -- Segmentation -- Therapeutic applications, including brachytherapy; adaptive estimation -- data acquisition -- image segmentation -- medical image processing -- phantoms -- polymers -- positron emission tomography -- probability -- radiation therapy; PET segmentation -- majority vote -- STAPLE -- Dice coefficient; Radiation therapy -- Data acquisition and logging -- Digital computing or data processing equipment or methods, specially adapted for specific applications -- Image data processing or generation, in general; Medical imaging -- Medical image segmentation -- Cancer -- Positron emission tomography -- Computed tomography -- Medical image noise -- Radiation therapy -- Nuclear medicine imaging -- Cluster analysis -- Medical image contrast; Medical physics -- Periodicals; Medical physics; Geneeskunde; Natuurkunde; Toepassingen; Biophysics; Periodicals; Periodicals; Electronic journals; 610.153
• DOI: 10.1118/1.4793721
• Languages: English
• ISSNs: 0094-2405

Abstract

Abstract : Purpose: : Many approaches have been proposed to segment high uptake objects in 18F‐fluoro‐deoxy‐glucose positron emission tomography images but none provides consistent performance across the large variety of imaging situations. This study investigates the use of two methods of combining individual segmentation methods to reduce the impact of inconsistent performance of the individual methods: simple majority voting and probabilistic estimation. Methods: : The National Electrical Manufacturers Association image quality phantom containing five glass spheres with diameters 13–37 mm and two irregularly shaped volumes (16 and 32 cc) formed by deforming high‐density polyethylene bottles in a hot water bath were filled with 18‐fluoro‐deoxyglucose and iodine contrast agent. Repeated 5‐min positron emission tomography (PET) images were acquired at 4:1 and 8:1 object‐to‐background contrasts for spherical objects and 4.5:1 and 9:1 for irregular objects. Five individual methods were used to segment each object: 40% thresholding, adaptive thresholding, k‐means clustering, seeded region‐growing, and a gradient based method. Volumes were combined using a majority vote (MJV) or Simultaneous Truth And Performance Level Estimate (STAPLE) method. Accuracy of segmentations relative to CT ground truth volumes were assessed using the Dice similarity coefficient (DSC) and the symmetric mean absolute surface distances (SMASDs). Results: : MJV had median DSC values of 0.886 and 0.875; and SMASD of 0.52 and 0.71 mm for spheres and irregular shapes, respectively. STAPLE provided similar results with median DSC of 0.886 and 0.871; and median SMASD of 0.50 and 0.72 mm for spheres and irregular shapes, respectively. STAPLE had significantly higher DSC and lower SMASD values than MJV for spheres (DSC, p < 0.0001; SMASD, p = 0.0101) but MJV had significantly higher DSC and lower SMASD values compared to STAPLE for irregular shapes (DSC, p < 0.0001; SMASD, p = 0.0027). DSC was not significantly different between 128 × 128 and 256 × 256 grid sizes for either method (MJV, p = 0.0519; STAPLE, p = 0.5672) but was for SMASD values (MJV, p < 0.0001; STAPLE, p = 0.0164). The best individual method varied depending on object characteristics. However, both MJV and STAPLE provided essentially equivalent accuracy to using the best independent method in every situation, with mean differences in DSC of 0.01–0.03, and 0.05–0.12 mm for SMASD. Conclusions: : Combining segmentations offers a robust approach to object segmentation in PET. Both MJV and STAPLE improved accuracy and were robust against the widely varying performance of individual segmentation methods. Differences between MJV and STAPLE are such that either offers good performance when combining volumes. Neither method requires a training dataset but MJV is simpler to interpret, easy to implement and fast.