title: Improving Spleen Volume Estimation Via Computer-assisted Segmentation on Clinically Acquired CT Scans.
abstract: Multi-atlas fusion is a promising approach for computer-assisted segmentation of anatomic structures. The purpose of this study was to evaluate the accuracy and time efficiency of multi-atlas segmentation for estimating spleen volumes on clinically acquired computed tomography (CT) scans.
  
  
  
  Under an institutional review board approval, we obtained 294 de-identified (Health Insurance Portability and Accountability Act-compliant) abdominal CT scans on 78 subjects from a recent clinical trial. We compared five pipelines for obtaining splenic volumes: Pipeline 1 - manual segmentation of all scans, Pipeline 2 - automated segmentation of all scans, Pipeline 3 - automated segmentation of all scans with manual segmentation for outliers on a rudimentary visual quality check, and Pipelines 4 and 5 - volumes derived from a unidimensional measurement of craniocaudal spleen length and three-dimensional splenic index measurements, respectively. Using Pipeline 1 results as ground truth, the accuracies of Pipelines 2-5 (Dice similarity coefficient, Pearson correlation, R-squared, and percent and absolute deviation of volume from ground truth) were compared for point estimates of splenic volume and for change in splenic volume over time. Time cost was also compared for Pipelines 1-5.
  
  
  
  Pipeline 3 was dominant in terms of both accuracy and time cost. With a Pearson correlation coefficient of 0.99, average absolute volume deviation of 23.7 cm(3), and time cost of 1 minute per scan, Pipeline 3 yielded the best results. The second-best approach was Pipeline 5, with a Pearson correlation coefficient of 0.98, absolute deviation of 46.92 cm(3), and time cost of 1 minute 30 seconds per scan. Manual segmentation (Pipeline 1) required 11 minutes per scan.
  
  
  
  A computer-automated segmentation approach with manual correction of outliers generated accurate splenic volumes with reasonable time efficiency.
authors: Electrical Engineering, Vanderbilt University EECS, 2301 Vanderbilt Pl., PO Box 351679 Station B, Nashville, TN 37235-1679. Electronic address: zhoubing.xu@vanderbilt.edu.
authors: Zhoubing
authors: Z
authors: Xu
authors: Neuroscience, Vanderbilt University, Nashville, Tennessee.
authors: Adam L
authors: AL
authors: Gertz
authors: Biomedical Engineering, Vanderbilt University, Nashville, Tennessee.
authors: Ryan P
authors: RP
authors: Burke
authors: Diagnostic Radiology, NYU Langone Medical Center, New York, New York.
authors: Neil
authors: N
authors: Bansal
authors: Biostatistics, Vanderbilt University, Nashville, Tennessee.
authors: Hakmook
authors: H
authors: Kang
authors: Electrical Engineering, Vanderbilt University EECS, 2301 Vanderbilt Pl., PO Box 351679 Station B, Nashville, TN 37235-1679; Biomedical Engineering, Vanderbilt University, Nashville, Tennessee; Radiology and Radiological Science, Vanderbilt University, Nashville, Tennessee.
authors: Bennett A
authors: BA
authors: Landman
authors: Radiology and Radiological Science, Vanderbilt University, Nashville, Tennessee.
authors: Richard G
authors: RG
authors: Abramson
mesh_terms: D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D005260:Female; D006801:Humans; D008297:Male; D008875:Middle Aged; D009929:Organ Size; D013154:Spleen / Q000033:anatomy & histology* / Q000000981:diagnostic imaging*; D014057:Tomography, X-Ray Computed / Q000379:methods*
keywords: Multi-atlas segmentation; computed tomography; computer-aided diagnosis; spleen