OBJECTIVES Multi-atlas fusion is a promising approach for computer-assisted segmentation of anatomical 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 CT scans. MATERIALS AND METHODS Under IRB approval, we obtained 294 deidentified (HIPAA-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, 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 accuracy of Pipelines 2–5 (Dice similarity coefficient [DSC], 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. RESULTS Pipeline 3 was dominant in terms of both accuracy and time cost. With a Pearson correlation coefficient of 0.99, average absolute volume deviation 23.7 cm3, and 1 minute per scan, Pipeline 3 yielded the best results. The second-best approach was Pipeline 5, with a Pearson correlation coefficient 0.98, absolute deviation 46.92 cm3, and 1 minute 30 seconds per scan. Manual segmentation (Pipeline 1) required 11 minutes per scan. CONCLUSION A computer-automated segmentation approach with manual correction of outliers generated accurate splenic volumes with reasonable time efficiency.

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