基于深度学习框架从二维横截面视图估计脾脏体积
Deep Learning Framework for Spleen Volume Estimation from 2D Cross-sectional Views
AI总结：
本文提出基于变分自编码器的框架，从单/双视图二维脾脏分割直接估计三维脾脏体积并给出置信区间，性能优于临床线性回归及二维到三维重建方法。
摘要：
脾脏异常增大（脾肿大）被视为多种疾病的临床指标，包括肝病、癌症和血液疾病。尽管从超声图像测得的脾脏长度是常用的脾脏大小替代指标，但脾脏体积仍是评估脾肿大及相关临床疾病严重程度的金标准指标。计算机断层扫描是测量脾脏体积的主要成像方式，但在脾肿大高发地区（例如全球南方），其可及性较低。我们的目标是实现从可通过超声成像获得的二维横截面分割结果中自动测量脾脏体积。在本研究中，我们描述了一种基于变分自编码器的框架，用于从单视图或双视图二维脾脏分割结果测量脾脏体积。我们在该框架内提出并评估了三种体积估计方法。我们还展示了如何生成体积估计值的95%置信区间，以使我们的方法更具临床实用性。我们的最佳模型在单视图和双视图分割上分别取得了86.62%和92.58%的平均相对体积准确率，优于使用人工测量进行线性回归的临床标准方法，以及一种基于深度学习的二维到三维重建对比方法。所提出的脾脏体积估计框架可以集成到当前使用二维超声图像测量脾脏长度的标准临床工作流程中。据我们所知，这是首个实现从二维脾脏分割直接估计三维脾脏体积的工作。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
