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.

该研究提出了一个基于变分自编码器的方法，从 2D 脾脏分割图像中测量脾脏体积，优于线性回归和深度学习方法，可以集成到当前使用 2D 超声图像测量脾脏长度的标准临床工作流程中。该研究是第一个能够直接从 2D 脾脏分割图像估计 3D 脾脏体积的工作。