# Validated respiratory drug deposition predictions from 2D and 3D medical images with statistical shape models and convolutional neural networks

arXiv: https://arxiv.org/abs/2303.01036 · HTML: https://mlsanity.com/paper/2303.01036/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 18 of its 151 references that are in the map.
- First posted on arXiv 2 Mar 2023.

## Builds on (most cited first, 18 of 18)

- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [3D U-Net: Learning Dense Volumetric Segmentation from Sparse Annotation](https://mlsanity.com/paper/1606.06650/) (2016)
- [Geometric deep learning: going beyond Euclidean data](https://mlsanity.com/paper/1611.08097/) (2016)
- [ENet: A Deep Neural Network Architecture for Real-Time Semantic Segmentation](https://mlsanity.com/paper/1606.02147/) (2016)
- [Validation, comparison, and combination of algorithms for automatic detection of pulmonary nodules in computed tomography images: the LUNA16 challenge](https://mlsanity.com/paper/1612.08012/) (2016)
- [X2CT-GAN: Reconstructing CT from Biplanar X-Rays with Generative Adversarial Networks](https://mlsanity.com/paper/1905.06902/) (2019)
- [Hidden Fluid Mechanics: A Navier-Stokes Informed Deep Learning Framework for Assimilating Flow Visualization Data](https://mlsanity.com/paper/1808.04327/) (2018)
- [A joint 3D UNet-Graph Neural Network-based method for Airway Segmentation from chest CTs](https://mlsanity.com/paper/1908.08588/) (2019)
- [Computational Anatomy for Multi-Organ Analysis in Medical Imaging: A Review](https://mlsanity.com/paper/1812.08577/) (2018)
- [Automatic airway segmentation from Computed Tomography using robust and efficient 3-D convolutional neural networks](https://mlsanity.com/paper/2103.16328/) (2021)
- [A survey on shape-constraint deep learning for medical image segmentation](https://mlsanity.com/paper/2101.07721/) (2021)
- [Automatic Pulmonary Lobe Segmentation Using Deep Learning](https://mlsanity.com/paper/1903.09879/) (2019)
- [Black-Box Optimization Revisited: Improving Algorithm Selection Wizards through Massive Benchmarking](https://mlsanity.com/paper/2010.04542/) (2020)
- [Lung Structures Enhancement in Chest Radiographs via CT based FCNN Training](https://mlsanity.com/paper/1810.05989/) (2018)
- [FastVentricle: Cardiac Segmentation with ENet](https://mlsanity.com/paper/1704.04296/) (2017)
- [Bone Suppression on Chest Radiographs With Adversarial Learning](https://mlsanity.com/paper/2002.03073/) (2020)
- [pyssam -- a Python library for statistical modelling of biomedical shape and appearance](https://mlsanity.com/paper/2301.04416/) (2023)

## Abstract (arXiv)

For the one billion sufferers of respiratory disease, managing their disease with inhalers crucially influences their quality of life. Generic treatment plans could be improved with the aid of computational models that account for patient-specific features such as breathing pattern, lung pathology and morphology. Therefore, we aim to develop and validate an automated computational framework for patient-specific deposition modelling. To that end, an image processing approach is proposed that could produce 3D patient respiratory geometries from 2D chest X-rays and 3D CT images. We evaluated the airway and lung morphology produced by our image processing framework, and assessed deposition compared to in vivo data. The 2D-to-3D image processing reproduces airway diameter to 9% median error compared to ground truth segmentations, but is sensitive to outliers of up to 33% due to lung outline noise. Predicted regional deposition gave 5% median error compared to in vivo measurements. The proposed framework is capable of providing patient-specific deposition measurements for varying treatments, to determine which treatment would best satisfy the needs imposed by each patient (such as disease and lung/airway morphology). Integration of patient-specific modelling into clinical practice as an additional decision-making tool could optimise treatment plans and lower the burden of respiratory diseases.