  Abnormal spleen enlargement (splenomegaly) is regarded as a clinical
indicator for a range of conditions, including liver disease, cancer and blood
diseases. While spleen length measured from ultrasound images is a commonly
used surrogate for spleen size, spleen volume remains the gold standard metric
for assessing splenomegaly and the severity of related clinical conditions.
Computed tomography is the main imaging modality for measuring spleen volume,
but it is less accessible in areas where there is a high prevalence of
splenomegaly (e.g., the Global South). Our objective was to enable automated
spleen volume measurement from 2D cross-sectional segmentations, which can be
obtained from ultrasound imaging. In this study, we describe a variational
autoencoder-based framework to measure spleen volume from single- or dual-view
2D spleen segmentations. We propose and evaluate three volume estimation
methods within this framework. We also demonstrate how 95% confidence intervals
of volume estimates can be produced to make our method more clinically useful.
Our best model achieved mean relative volume accuracies of 86.62% and 92.58%
for single- and dual-view segmentations, respectively, surpassing the
performance of the clinical standard approach of linear regression using manual
measurements and a comparative deep learning-based 2D-3D reconstruction-based
approach. The proposed spleen volume estimation framework can be integrated
into standard clinical workflows which currently use 2D ultrasound images to
measure spleen length. To the best of our knowledge, this is the first work to
achieve direct 3D spleen volume estimation from 2D spleen segmentations.
