# Deep Learning Framework for Spleen Volume Estimation from 2D Cross-sectional Views

arXiv: https://arxiv.org/abs/2308.08038 · HTML: https://mlsanity.com/paper/2308.08038/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 13 of its 40 references that are in the map.
- First posted on arXiv 15 Aug 2023.

## Builds on (most cited first, 13 of 13)

- [The Medical Segmentation Decathlon](https://mlsanity.com/paper/2106.05735/) (2021)
- [DeepOrgan: Multi-level Deep Convolutional Networks for Automated Pancreas Segmentation](https://mlsanity.com/paper/1506.06448/) (2015)
- [Pix2Vox++: Multi-scale Context-aware 3D Object Reconstruction from Single and Multiple Images](https://mlsanity.com/paper/2006.12250/) (2020)
- [3D Shape Reconstruction from 2D Images with Disentangled Attribute Flow](https://mlsanity.com/paper/2203.15190/) (2022)
- [Variational AutoEncoder For Regression: Application to Brain Aging Analysis](https://mlsanity.com/paper/1904.05948/) (2019)
- [Learning Interpretable Anatomical Features Through Deep Generative Models: Application to Cardiac Remodeling](https://mlsanity.com/paper/1807.06843/) (2018)
- [Interpretable Deep Models for Cardiac Resynchronisation Therapy Response Prediction](https://mlsanity.com/paper/2006.13811/) (2020)
- [Attri-VAE: attribute-based interpretable representations of medical images with variational autoencoders](https://mlsanity.com/paper/2203.10417/) (2022)
- [Splenomegaly Segmentation on Multi-modal MRI using Deep Convolutional Networks](https://mlsanity.com/paper/1811.04045/) (2018)
- [Efficient Pix2Vox++ for 3D Cardiac Reconstruction from 2D echo views](https://mlsanity.com/paper/2207.13424/) (2022)
- [Estimation of the volume of the left ventricle from MRI images using deep neural networks](https://mlsanity.com/paper/1702.03833/) (2017)
- [Assessing the Impact of Blood Pressure on Cardiac Function Using Interpretable Biomarkers and Variational Autoencoders](https://mlsanity.com/paper/1908.04538/) (2019)
- [Deep Learning for Automatic Spleen Length Measurement in Sickle Cell Disease Patients](https://mlsanity.com/paper/2009.02704/) (2020)

## Abstract (arXiv)

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.