Deep learning in structural and functional lung image analysis.

The recent resurgence of deep learning (DL) has dramatically influenced the medical imaging field. Medical image analysis applications have been at the forefront of DL research efforts applied to multiple diseases and organs, including those of the lungs. The aims of this review are twofold: (i) to briefly overview DL theory as it relates to lung image analysis; (ii) to systematically review the DL research literature relating to the lung image analysis applications of segmentation, reconstruction, registration and synthesis. The review was conducted following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. 479 studies were initially identified from the literature search with 82 studies meeting the eligibility criteria. Segmentation was the most common lung image analysis DL application (65.9% of papers reviewed). DL has shown impressive results when applied to segmentation of the whole lung and other pulmonary structures. DL has also shown great potential for applications in image registration, reconstruction and synthesis. However, the majority of published studies have been limited to structural lung imaging with only 12.9% of reviewed studies employing functional lung imaging modalities, thus highlighting significant opportunities for further research in this field. Although the field of DL in lung image analysis is rapidly expanding, concerns over inconsistent validation and evaluation strategies, intersite generalisability, transparency of methodological detail and interpretability need to be addressed before widespread adoption in clinical lung imaging workflow

Registration and analysis of dynamic magnetic resonance image series

Cystic fibrosis (CF) is an autosomal-recessive inherited metabolic disorder that affects all organs in the human body. Patients affected with CF suffer particularly from chronic inflammation and obstruction of the airways. Through early detection, continuous monitoring methods, and new treatments, the life expectancy of patients with CF has been increased drastically in the last decades. However, continuous monitoring of the disease progression is essential for a successful treatment. The current state-of-the-art method for lung disease detection and monitoring is computed tomography (CT) or X-ray. These techniques are ill-suited for the monitoring of disease progressions because of the ionizing radiation the patient is exposed during the examination. Through the development of new magnetic resonance imaging (MRI) sequences and evaluation methods, MRI is able to measure physiological changes in the lungs. The process to create physiological maps, i.e. ventilation and perfusion maps, of the lungs using MRI can be split up into three parts: MR-acquisition, image registration, and image analysis. In this work, we present different methods for the image registration part and the image analysis part. We developed a graph-based registration method for 2D dynamic MR image series of the lungs in order to overcome the problem of sliding motion at organ boundaries. Furthermore, we developed a human-inspired learning-based registration method. Here, the registration is defined as a sequence of local transformations. The sequence-based approach combines the advantage of dense transformation models, i.e. large space of transformations, and the advantage of interpolating transformation models, i.e. smooth local transformations. We also developed a general registration framework called Autograd Image Registration Laboratory (AIRLab), which performs automatic calculation of the gradients for the registration process. This allows rapid prototyping and an easy implementation of existing registration algorithms. For the image analysis part, we developed a deep-learning approach based on gated recurrent units that are able to calculate ventilation maps with less than a third of the number of images of the current method. Automatic defect detection in the estimated MRI ventilation and perfusion maps is essential for the clinical routine to automatically evaluate the treatment progression. We developed a weakly supervised method that is able to infer a pixel-wise defect segmentation by using only a continuous global label during training. In this case, we directly use the lung clearance index (LCI) as a global weak label, without any further manual annotations. The LCI is a global measure to describe ventilation inhomogeneities of the lungs and is obtained by a multiple breath washout test

The role of deep learning in structural and functional lung imaging

Background: Structural and functional lung imaging are critical components of pulmonary patient care. Image analysis methods, such as image segmentation, applied to structural and functional lung images, have significant benefits for patients with lung pathologies, including the computation of clinical biomarkers. Traditionally, machine learning (ML) approaches, such as clustering, and computational modelling techniques, such as CT-ventilation imaging, have been used for segmentation and synthesis, respectively. Deep learning (DL) has shown promise in medical image analysis tasks, often outperforming alternative methods. Purpose: To address the hypothesis that DL can outperform conventional ML and classical image analysis methods for the segmentation and synthesis of structural and functional lung imaging via: i. development and comparison of 3D convolutional neural networks (CNNs) for the segmentation of ventilated lung using hyperpolarised (HP) gas MRI. ii. development of a generalisable, multi-centre CNN for segmentation of the lung cavity using 1H-MRI. iii. the proposal of a framework for estimating the lung cavity in the spatial domain of HP gas MRI. iv. development of a workflow to synthesise HP gas MRI from multi-inflation, non-contrast CT. v. the proposal of a framework for the synthesis of fully-volumetric HP gas MRI ventilation from a large, diverse dataset of non-contrast, multi-inflation 1H-MRI scans. Methods: i. A 3D CNN-based method for the segmentation of ventilated lung using HP gas MRI was developed and CNN parameters, such as architecture, loss function and pre-processing were optimised. ii. A 3D CNN trained on a multi-acquisition dataset and validated on data from external centres was compared with a 2D alternative for the segmentation of the lung cavity using 1H-MRI. iii. A dual-channel, multi-modal segmentation framework was compared to single-channel approaches for estimation of the lung cavity in the domain of HP gas MRI. iv. A hybrid data-driven and model-based approach for the synthesis of HP gas MRI ventilation from CT was compared to approaches utilising DL or computational modelling alone. v. A physics-constrained, multi-channel framework for the synthesis of fully-volumetric ventilation surrogates from 1H-MRI was validated using five-fold cross-validation and an external test data set. Results: i. The 3D CNN, developed via parameterisation experiments, accurately segmented ventilation scans and outperformed conventional ML methods. ii. The 3D CNN produced more accurate segmentations than its 2D analogues for the segmentation of the lung cavity, exhibiting minimal variation in performance between centres, vendors and acquisitions. iii. Dual-channel, multi-modal approaches generate significant improvements compared to methods which use a single imaging modality for the estimation of the lung cavity. iv. The hybrid approach produced synthetic ventilation scans which correlate with HP gas MRI. v. The physics-constrained, 3D multi-channel synthesis framework outperformed approaches which did not integrate computational modelling, demonstrating generalisability to external data. Conclusion: DL approaches demonstrate the ability to segment and synthesise lung MRI across a range of modalities and pulmonary pathologies. These methods outperform computational modelling and classical ML approaches, reducing the time required to adequately edit segmentations and improving the modelling of synthetic ventilation, which may facilitate the clinical translation of DL in structural and functional lung imaging