Improving Spleen Volume Estimation Via Computer-assisted Segmentation on Clinically Acquired CT Scans

Academic Radiology
Zhoubing XuRichard G Abramson

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 cos...Continue Reading

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Citations

Jan 10, 2021·Medical Image Analysis·Yucheng TangBennett A Landman

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