A Survey on Deep Learning in Medical Image Analysis

Deep learning algorithms, in particular convolutional networks, have rapidly become a methodology of choice for analyzing medical images. This paper reviews the major deep learning concepts pertinent to medical image analysis and summarizes over 300 contributions to the field, most of which appeared in the last year. We survey the use of deep learning for image classification, object detection, segmentation, registration, and other tasks and provide concise overviews of studies per application area. Open challenges and directions for future research are discussed.Comment: Revised survey includes expanded discussion section and reworked introductory section on common deep architectures. Added missed papers from before Feb 1st 201

Comparative Performance Analysis of State-of-the-Art Classification Algorithms Applied to Lung Tissue Categorization

In this paper, we compare five common classifier families in their ability to categorize six lung tissue patterns in high-resolution computed tomography (HRCT) images of patients affected with interstitial lung diseases (ILD) and with healthy tissue. The evaluated classifiers are naive Bayes, k-nearest neighbor, J48 decision trees, multilayer perceptron, and support vector machines (SVM). The dataset used contains 843 regions of interest (ROI) of healthy and five pathologic lung tissue patterns identified by two radiologists at the University Hospitals of Geneva. Correlation of the feature space composed of 39 texture attributes is studied. A grid search for optimal parameters is carried out for each classifier family. Two complementary metrics are used to characterize the performances of classification. These are based on McNemar's statistical tests and global accuracy. SVM reached best values for each metric and allowed a mean correct prediction rate of 88.3% with high class-specific precision on testing sets of 423 ROI

Deep Learning with Limited Labels for Medical Imaging

Recent advancements in deep learning-based AI technologies provide an automatic tool to revolutionise medical image computing. Training a deep learning model requires a large amount of labelled data. Acquiring labels for medical images is extremely challenging due to the high cost in terms of both money and time, especially for the pixel-wise segmentation task of volumetric medical scans. However, obtaining unlabelled medical scans is relatively easier compared to acquiring labels for those images. This work addresses the pervasive issue of limited labels in training deep learning models for medical imaging. It begins by exploring different strategies of entropy regularisation in the joint training of labelled and unlabelled data to reduce the time and cost associated with manual labelling for medical image segmentation. Of particular interest are consistency regularisation and pseudo labelling. Specifically, this work proposes a well-calibrated semi-supervised segmentation framework that utilises consistency regularisation on different morphological feature perturbations, representing a significant step towards safer AI in medical imaging. Furthermore, it reformulates pseudo labelling in semi-supervised learning as an Expectation-Maximisation framework. Building upon this new formulation, the work explains the empirical successes of pseudo labelling and introduces a generalisation of the technique, accompanied by variational inference to learn its true posterior distribution. The applications of pseudo labelling in segmentation tasks are also presented. Lastly, this work explores unsupervised deep learning for parameter estimation of diffusion MRI signals, employing a hierarchical variational clustering framework and representation learning

PET/MRI attenuation estimation in the lung: A review of past, present, and potential techniques

Positron emission tomography/magnetic resonance imaging (PET/MRI) potentially offers several advantages over positron emission tomography/computed tomography (PET/CT), for example, no CT radiation dose and soft tissue images from MR acquired at the same time as the PET. However, obtaining accurate linear attenuation correction (LAC) factors for the lung remains difficult in PET/MRI. LACs depend on electron density and in the lung, these vary significantly both within an individual and from person to person. Current commercial practice is to use a single-valued population-based lung LAC, and better estimation is needed to improve quantification. Given the under-appreciation of lung attenuation estimation as an issue, the inaccuracy of PET quantification due to the use of single-valued lung LACs, the unique challenges of lung estimation, and the emerging status of PET/MRI scanners in lung disease, a review is timely. This paper highlights past and present methods, categorizing them into segmentation, atlas/mapping, and emission-based schemes. Potential strategies for future developments are also presented

Genomic analysis of macrophage gene signatures during idiopathic pulmonary fibrosis development

Idiopathic Pulmonary Fibrosis (IPF) is a chronic, progressive, irreversible lung disease. After diagnosis, the interstitial condition commonly presents 3-5 years of life expectancy if untreated. Despite the limited capacity of recapitulating IPF, animal models have been useful for identifying related pathways relevant for drug discovery and diagnostic tools development. Using these techniques, several immune-related mechanisms have been implicated to IPF. For instance, subpopulations of macrophages and monocytes-derived cells are recognized as centrally active in pulmonary immunological processes. One of the most used technologies is high-throughput gene expression analysis, which has been available for almost two decades now. The “omics” revolution has presented major impacts on macrophage and pulmonary fibrosis research. The present study aims to investigate macrophage dynamics within the context of IPF at the transcriptomic level. Using publicly available gene-expression data, we applied modern data science approaches to (1) understand longitudinal profiles within IPF models; (2) investigate correlation between macrophage genomic dynamics and IPF development; and (3) apply longitudinal profiles uncovered through multivariate data analysis to the development of new sets of predictors able to classify IPF and control samples accordingly. Principal Component Analysis and Hierarchical Clustering showed that our pipeline was able to construct a complex set of biomarker candidates that together outperformed gene expression alone in separating treatment groups in an IPF animal model dataset. We further assessed the predictive performance of our candidates on publicly available gene expression data from IPF patients. Once again, the constructed biomarker candidates were significantly differentiated between IPF and control samples. The data presented in this work strongly suggest that longitudinal data analysis holds major unappreciated potentials for translational medicine research

Deep Learning in Medical Image Analysis

The computer-assisted analysis for better interpreting images have been longstanding issues in the medical imaging field. On the image-understanding front, recent advances in machine learning, especially, in the way of deep learning, have made a big leap to help identify, classify, and quantify patterns in medical images. Specifically, exploiting hierarchical feature representations learned solely from data, instead of handcrafted features mostly designed based on domain-specific knowledge, lies at the core of the advances. In that way, deep learning is rapidly proving to be the state-of-the-art foundation, achieving enhanced performances in various medical applications. In this article, we introduce the fundamentals of deep learning methods; review their successes to image registration, anatomical/cell structures detection, tissue segmentation, computer-aided disease diagnosis or prognosis, and so on. We conclude by raising research issues and suggesting future directions for further improvements

A modeling platform to predict cancer survival and therapy outcomes using tumor tissue derived metabolomics data.

Cancer is a complex and broad disease that is challenging to treat, partially due to the vast molecular heterogeneity among patients even within the same subtype. Currently, no reliable method exists to determine which potential first-line therapy would be most effective for a specific patient, as randomized clinical trials have concluded that no single regimen may be significantly more effective than others. One ongoing challenge in the field of oncology is the search for personalization of cancer treatment based on patient data. With an interdisciplinary approach, we show that tumor-tissue derived metabolomics data is capable of predicting clinical response to systemic therapy classified as disease control vs. progressive disease and pathological stage classified as stage I/II/III vs. stage IV via data analysis with machine-learning techniques (AUROC = 0.970; AUROC=0.902). Patient survival was also analyzed via statistical methods and machine-learning, both of which show that tumor-tissue derived metabolomics data is capable of risk stratifying patients in terms of long vs. short survival (OS AUROC = 0.940TEST; PFS AUROC = 0.875TEST). A set of key metabolites as potential biomarkers and associated metabolic pathways were also found for each outcome, which may lead to insight into biological mechanisms. Additionally, we developed a methodology to calibrate tumor growth related parameters in a well-established mathematical model of cancer to help predict the potential nuances of chemotherapeutic response. The proposed methodology shows results consistent with clinical observations in predicting individual patient response to systemic therapy and helps lay the foundation for further investigation into the calibration of mathematical models of cancer with patient-tissue derived molecular data. Chapters 6 and 8 were published in the Annals of Biomedical Engineering. Chapters 2, 3, and 7 were published in Metabolomics, Lung Cancer, and Pharmaceutical Research, respectively. Chapters 4 has been accepted for publication at the journal Metabolomics (in press) and Chapter 5 is in review at the journal Metabolomics. Chapter 9 is currently undergoing preparation for submission

Statement on imaging and pulmonary hypertension from the Pulmonary Vascular Research Institute (PVRI)

Pulmonary hypertension (PH) is highly heterogeneous and despite treatment advances it remains a life-shortening condition. There have been significant advances in imaging technologies, but despite evidence of their potential clinical utility, practice remains variable, dependent in part on imaging availability and expertise. This statement summarizes current and emerging imaging modalities and their potential role in the diagnosis and assessment of suspected PH. It also includes a review of commonly encountered clinical and radiological scenarios, and imaging and modeling-based biomarkers. An expert panel was formed including clinicians, radiologists, imaging scientists, and computational modelers. Section editors generated a series of summary statements based on a review of the literature and professional experience and, following consensus review, a diagnostic algorithm and 55 statements were agreed. The diagnostic algorithm and summary statements emphasize the key role and added value of imaging in the diagnosis and assessment of PH and highlight areas requiring further research