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

Abstract

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.

Fully convolutional neural networks improve abdominal organ segmentation

“…Additionally, the appearance of those organs depends greatly on the quality of the image, which differs for each scanner. Traditionally, multi-atlas methods have been able to segment abdominal organs with reasonable accuracy [1, 5, 6, 10, 11]. More recently, researchers have demonstrated that fully convolutional neural networks (FCNN) show great promise in both general image segmentation and abdominal organ segmentation of CT scans [1, 12, 13].…”

Splenic Volume Index Determined Using Computed Tomography upon Admission Is Associated with Readmission for Heart Failure Among Patients with Acute Decompensated Heart Failure

“…Among these, all 3D-CT scans closely correlated with manual segmentation for splenic volume and the measurement requires approximately one minute. 17) We determined splenic volume using 3D-CT volumetry within 3 minutes and the results closely correlated with the splenic volume estimated using standard formulae.…”

Robust Multicontrast MRI Spleen Segmentation for Splenomegaly Using Multi-Atlas Segmentation

“…In clinical diagnosis of splenomegaly, one dimensional (1D) measurements had been used to estimate spleen volume efficiently. Following [32], the 1D craniocaudal spleen length (L) yielded 0.8613 Pearson correlation with ground truth on spleen volume estimation using ≈ 1 minute manual efforts. Therefore, the craniocaudal spleen length was employed in Pipeline 3 to guide the atlas selection.…”

“…In this paper, (1) we evaluate the performance of Selective and Iterative Method for Performance Level Estimation (SIMPLE) atlas selection method [36] based on our previous efforts on CT spleen segmentation [31, 32]. (2) For the particular concerns for MRI clinical splenomegaly images, we propose the L-SIMPLE method to achieve the robust spleen segmentation using craniocaudal spleen length (L).…”