Translator Disclaimer
19 February 2021 Validation and estimation of spleen volume via computer-assisted segmentation on clinically acquired CT scans
Author Affiliations +
Abstract

Purpose: Deep learning is a promising technique for spleen segmentation. Our study aims to validate the reproducibility of deep learning-based spleen volume estimation by performing spleen segmentation on clinically acquired computed tomography (CT) scans from patients with myeloproliferative neoplasms.

Approach: As approved by the institutional review board, we obtained 138 de-identified abdominal CT scans. A sum of voxel volume on an expert annotator’s segmentations establishes the ground truth (estimation 1). We used our deep convolutional neural network (estimation 2) alongside traditional linear estimations (estimation 3 and 4) to estimate spleen volumes independently. Dice coefficient, Hausdorff distance, R2 coefficient, Pearson R coefficient, the absolute difference in volume, and the relative difference in volume were calculated for 2 to 4 against the ground truth to compare and assess methods’ performances. We re-labeled on scan–rescan on a subset of 40 studies to evaluate method reproducibility.

Results: Calculated against the ground truth, the R2 coefficients for our method (estimation 2) and linear method (estimation 3 and 4) are 0.998, 0.954, and 0.973, respectively. The Pearson R coefficients for the estimations against the ground truth are 0.999, 0.963, and 0.978, respectively (paired t-tests produced p  <  0.05 between 2 and 3, and 2 and 4).

Conclusion: The deep convolutional neural network algorithm shows excellent potential in rendering more precise spleen volume estimations. Our computer-aided segmentation exhibits reasonable improvements in splenic volume estimation accuracy.

© 2021 Society of Photo-Optical Instrumentation Engineers (SPIE) 2329-4302/2021/$28.00 © 2021 SPIE
Yiyuan Yang, Yucheng Tang, Riqiang Gao, Shunxing Bao, Yuankai Huo, Matthew T. McKenna, Michael R. Savona, Richard G. Abramson, and Bennett A. Landman "Validation and estimation of spleen volume via computer-assisted segmentation on clinically acquired CT scans," Journal of Medical Imaging 8(1), 014004 (19 February 2021). https://doi.org/10.1117/1.JMI.8.1.014004
Received: 6 August 2020; Accepted: 28 January 2021; Published: 19 February 2021
JOURNAL ARTICLE
11 PAGES


SHARE
Advertisement
Advertisement
Back to Top