Recent Advances in Machine Learning Applied to Ultrasound Imaging

Machine learning (ML) methods are pervading an increasing number of fields of application because of their capacity to effectively solve a wide variety of challenging problems. The employment of ML techniques in ultrasound imaging applications started several years ago but the scientific interest in this issue has increased exponentially in the last few years. The present work reviews the most recent (2019 onwards) implementations of machine learning techniques for two of the most popular ultrasound imaging fields, medical diagnostics and non-destructive evaluation. The former, which covers the major part of the review, was analyzed by classifying studies according to the human organ investigated and the methodology (e.g., detection, segmentation, and/or classification) adopted, while for the latter, some solutions to the detection/classification of material defects or particular patterns are reported. Finally, the main merits of machine learning that emerged from the study analysis are summarized and discussed. © 2022 by the authors. Licensee MDPI, Basel, Switzerland

Ultrasound Elastography using Machine Learning

This thesis aims at solving two main problems that we face in ultrasound elastography, namely fast strain estimation and radio frequency (RF) frame selection. We rely on machine learning concepts such as Principal Components Analysis (PCA), multi-layer perceptron (MLP) and convolutional neural networks (CNN) to build 3 models that are trained on both phantom and in vivo data. In our first work, we developed a method to estimate the initial displacement between two ultrasound RF frames using PCA. We first compute an initial displacement estimate of around 1% of the samples, and then decompose the displacement into a linear combination of principal components (obtained offline during the training step). Our method assumes that the initial displacement of the whole image could also be described by this linear combination of principal components. This yields the same result that we could have had if we run dynamic programming (DP). The advantage of using PCA is that we could compute the same initial displacement image more than 10 times faster than DP. We then pass the result to GLobal Ultrasound Elastography (GLUE) for fine-tuning it, so we call the method PCA-GLUE. In our second work, we developed a novel method to address the problem of RF frame selection in ultrasound elastography. Intuitively, we would like to have a classifier that gives a binary 1 to RF frame pairs that yield high-quality strain images. We make use of our previous work where we decompose the initial displacement between two RF frames into a weight vector multiplied by some principal components. We consider the weight vector as our input feature vector to an MLP model. Given two RF frames I1 and I2, the MLP model predicts the normalized cross correlation (NCC) between the two RF frames I1 and I2′ (I2′ is I2 after being displaced according to the displacement of GLUE/PCA-GLUE). Our final contribution in this line of research is the introduction of a CNN-based method for RF frame selection as follows. First, we changed the architecture from an MLP model to a CNN that takes the two RF frames on two channels. The CNN has better results compared to the MLP model due to having more features. Second, we improved the automatic labelling of the data by having physical conditions that must be satisfied together in order to consider the pair as a suitable pair of RF frames

Tuberculosis diagnosis from pulmonary chest x-ray using deep learning.

Doctoral Degree. University of KwaZulu-Natal, Durban.Tuberculosis (TB) remains a life-threatening disease, and it is one of the leading causes of mortality in developing countries. This is due to poverty and inadequate medical resources. While treatment for TB is possible, it requires an accurate diagnosis first. Several screening tools are available, and the most reliable is Chest X-Ray (CXR), but the radiological expertise for accurately interpreting the CXR images is often lacking. Over the years, CXR has been manually examined; this process results in delayed diagnosis, is time-consuming, expensive, and is prone to misdiagnosis, which could further spread the disease among individuals. Consequently, an algorithm could increase diagnosis efficiency, improve performance, reduce the cost of manual screening and ultimately result in early/timely diagnosis. Several algorithms have been implemented to diagnose TB automatically. However, these algorithms are characterized by low accuracy and sensitivity leading to misdiagnosis. In recent years, Convolutional Neural Networks (CNN), a class of Deep Learning, has demonstrated tremendous success in object detection and image classification task. Hence, this thesis proposed an efficient Computer-Aided Diagnosis (CAD) system with high accuracy and sensitivity for TB detection and classification. The proposed model is based firstly on novel end-to-end CNN architecture, then a pre-trained Deep CNN model that is fine-tuned and employed as a features extractor from CXR. Finally, Ensemble Learning was explored to develop an Ensemble model for TB classification. The Ensemble model achieved a new stateof- the-art diagnosis accuracy of 97.44% with a 99.18% sensitivity, 96.21% specificity and 0.96% AUC. These results are comparable with state-of-the-art techniques and outperform existing TB classification models.Author's Publications listed on page iii

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

Advanced Sensing and Image Processing Techniques for Healthcare Applications

This Special Issue aims to attract the latest research and findings in the design, development and experimentation of healthcare-related technologies. This includes, but is not limited to, using novel sensing, imaging, data processing, machine learning, and artificially intelligent devices and algorithms to assist/monitor the elderly, patients, and the disabled population