Data efficient deep learning for medical image analysis: A survey

The rapid evolution of deep learning has significantly advanced the field of medical image analysis. However, despite these achievements, the further enhancement of deep learning models for medical image analysis faces a significant challenge due to the scarcity of large, well-annotated datasets. To address this issue, recent years have witnessed a growing emphasis on the development of data-efficient deep learning methods. This paper conducts a thorough review of data-efficient deep learning methods for medical image analysis. To this end, we categorize these methods based on the level of supervision they rely on, encompassing categories such as no supervision, inexact supervision, incomplete supervision, inaccurate supervision, and only limited supervision. We further divide these categories into finer subcategories. For example, we categorize inexact supervision into multiple instance learning and learning with weak annotations. Similarly, we categorize incomplete supervision into semi-supervised learning, active learning, and domain-adaptive learning and so on. Furthermore, we systematically summarize commonly used datasets for data efficient deep learning in medical image analysis and investigate future research directions to conclude this survey.Comment: Under Revie

Machine Learning for Biomedical Application

Biomedicine is a multidisciplinary branch of medical science that consists of many scientific disciplines, e.g., biology, biotechnology, bioinformatics, and genetics; moreover, it covers various medical specialties. In recent years, this field of science has developed rapidly. This means that a large amount of data has been generated, due to (among other reasons) the processing, analysis, and recognition of a wide range of biomedical signals and images obtained through increasingly advanced medical imaging devices. The analysis of these data requires the use of advanced IT methods, which include those related to the use of artificial intelligence, and in particular machine learning. It is a summary of the Special Issue “Machine Learning for Biomedical Application”, briefly outlining selected applications of machine learning in the processing, analysis, and recognition of biomedical data, mostly regarding biosignals and medical images

Medical Image Analysis using Deep Relational Learning

In the past ten years, with the help of deep learning, especially the rapid development of deep neural networks, medical image analysis has made remarkable progress. However, how to effectively use the relational information between various tissues or organs in medical images is still a very challenging problem, and it has not been fully studied. In this thesis, we propose two novel solutions to this problem based on deep relational learning. First, we propose a context-aware fully convolutional network that effectively models implicit relation information between features to perform medical image segmentation. The network achieves the state-of-the-art segmentation results on the Multi Modal Brain Tumor Segmentation 2017 (BraTS2017) and Multi Modal Brain Tumor Segmentation 2018 (BraTS2018) data sets. Subsequently, we propose a new hierarchical homography estimation network to achieve accurate medical image mosaicing by learning the explicit spatial relationship between adjacent frames. We use the UCL Fetoscopy Placenta dataset to conduct experiments and our hierarchical homography estimation network outperforms the other state-of-the-art mosaicing methods while generating robust and meaningful mosaicing result on unseen frames.Comment: arXiv admin note: substantial text overlap with arXiv:2007.0778

Inferring Geodesic Cerebrovascular Graphs: Image Processing, Topological Alignment and Biomarkers Extraction

A vectorial representation of the vascular network that embodies quantitative features - location, direction, scale, and bifurcations - has many potential neuro-vascular applications. Patient-specific models support computer-assisted surgical procedures in neurovascular interventions, while analyses on multiple subjects are essential for group-level studies on which clinical prediction and therapeutic inference ultimately depend. This first motivated the development of a variety of methods to segment the cerebrovascular system. Nonetheless, a number of limitations, ranging from data-driven inhomogeneities, the anatomical intra- and inter-subject variability, the lack of exhaustive ground-truth, the need for operator-dependent processing pipelines, and the highly non-linear vascular domain, still make the automatic inference of the cerebrovascular topology an open problem. In this thesis, brain vessels’ topology is inferred by focusing on their connectedness. With a novel framework, the brain vasculature is recovered from 3D angiographies by solving a connectivity-optimised anisotropic level-set over a voxel-wise tensor field representing the orientation of the underlying vasculature. Assuming vessels joining by minimal paths, a connectivity paradigm is formulated to automatically determine the vascular topology as an over-connected geodesic graph. Ultimately, deep-brain vascular structures are extracted with geodesic minimum spanning trees. The inferred topologies are then aligned with similar ones for labelling and propagating information over a non-linear vectorial domain, where the branching pattern of a set of vessels transcends a subject-specific quantized grid. Using a multi-source embedding of a vascular graph, the pairwise registration of topologies is performed with the state-of-the-art graph matching techniques employed in computer vision. Functional biomarkers are determined over the neurovascular graphs with two complementary approaches. Efficient approximations of blood flow and pressure drop account for autoregulation and compensation mechanisms in the whole network in presence of perturbations, using lumped-parameters analog-equivalents from clinical angiographies. Also, a localised NURBS-based parametrisation of bifurcations is introduced to model fluid-solid interactions by means of hemodynamic simulations using an isogeometric analysis framework, where both geometry and solution profile at the interface share the same homogeneous domain. Experimental results on synthetic and clinical angiographies validated the proposed formulations. Perspectives and future works are discussed for the group-wise alignment of cerebrovascular topologies over a population, towards defining cerebrovascular atlases, and for further topological optimisation strategies and risk prediction models for therapeutic inference. Most of the algorithms presented in this work are available as part of the open-source package VTrails

Deep Representation-aligned Graph Multi-view Clustering for Limited Labeled Multi-modal Health Data

Today, many fields are characterised by having extensive quantities of data from a wide range of dissimilar sources and domains. One such field is medicine, in which data contain exhaustive combinations of spatial, temporal, linear, and relational data. Often lacking expert-assessed labels, much of this data would require analysis within the fields of unsupervised or semi-supervised learning. Thus, reasoned by the notion that higher view-counts provide more ways to recognise commonality across views, contrastive multi-view clustering may be utilised to train a model to suppress redundancy and otherwise medically irrelevant information. Yet, standard multi-view clustering approaches do not account for relational graph data. Recent developments aim to solve this by utilising various graph operations including graph-based attention. And within deep-learning graph-based multi-view clustering on a sole view-invariant affinity graph, representation alignment remains unexplored. We introduce Deep Representation-Aligned Graph Multi-View Clustering (DRAGMVC), a novel attention-based graph multi-view clustering model. Comparing maximal performance, our model surpassed the state-of-the-art in eleven out of twelve metrics on Cora, CiteSeer, and PubMed. The model considers view alignment on a sample-level by employing contrastive loss and relational data through a novel take on graph attention embeddings in which we use a Markov chain prior to increase the receptive field of each layer. For clustering, a graph-induced DDC module is used. GraphSAINT sampling is implemented to control our mini-batch space to capitalise on our Markov prior. Additionally, we present the MIMIC pleural effusion graph multi-modal dataset, consisting of two modalities registering 3520 chest X-ray images along with two static views registered within a one-day time frame: vital signs and lab tests. These making up the, in total, three views of the dataset. We note a significant improvement in terms of separability, view mixing, and clustering performance comparing DRAGMVC to preceding non-graph multi-view clustering models, suggesting a possible, largely unexplored use case of unsupervised graph multi-view clustering on graph-induced, multi-modal, and complex medical data

Advanced Computational Methods for Oncological Image Analysis

[Cancer is the second most common cause of death worldwide and encompasses highly variable clinical and biological scenarios. Some of the current clinical challenges are (i) early diagnosis of the disease and (ii) precision medicine, which allows for treatments targeted to specific clinical cases. The ultimate goal is to optimize the clinical workflow by combining accurate diagnosis with the most suitable therapies. Toward this, large-scale machine learning research can define associations among clinical, imaging, and multi-omics studies, making it possible to provide reliable diagnostic and prognostic biomarkers for precision oncology. Such reliable computer-assisted methods (i.e., artificial intelligence) together with clinicians’ unique knowledge can be used to properly handle typical issues in evaluation/quantification procedures (i.e., operator dependence and time-consuming tasks). These technical advances can significantly improve result repeatability in disease diagnosis and guide toward appropriate cancer care. Indeed, the need to apply machine learning and computational intelligence techniques has steadily increased to effectively perform image processing operations—such as segmentation, co-registration, classification, and dimensionality reduction—and multi-omics data integration.

Segmentation of images by color features: a survey

En este articulo se hace la revisión del estado del arte sobre la segmentación de imagenes de colorImage segmentation is an important stage for object recognition. Many methods have been proposed in the last few years for grayscale and color images. In this paper, we present a deep review of the state of the art on color image segmentation methods; through this paper, we explain the techniques based on edge detection, thresholding, histogram-thresholding, region, feature clustering and neural networks. Because color spaces play a key role in the methods reviewed, we also explain in detail the most commonly color spaces to represent and process colors. In addition, we present some important applications that use the methods of image segmentation reviewed. Finally, a set of metrics frequently used to evaluate quantitatively the segmented images is shown

Ischemic Stroke Thrombus Characterization through Quantitative Magnetic Resonance Imaging

Stroke is a pervasive, devastating disease and remains one of the most challenging conditions to treat. In particular, risk of recurrence is dramatically heightened after a primary stroke and requires urgent preventative therapy to effectively mitigate. However, the appropriate preventative therapy depends on the underlying source of the stroke, known as etiology, which is challenging to determine promptly. Current diagnostic tests for determining etiology underwhelm in both sensitivity and specificity, and in as much as 35% of cases etiology is never determined. In ischemic stroke, the composition of the occluding thrombus, specifically its red blood cell (RBC) content, has been shown to be indicative of etiology but remains largely ignored within clinical practice. Currently, composition can only be quantified through histological analysis, an involved process that can be completed in only the minority of cases where a thrombus has been physically retrieved from the patient during treatment. The goal of this thesis is to develop a quantitative MR imaging method which is capable of non-invasive prediction of ischemic stroke etiology through assessment of thrombus RBC content. To achieve this goal, we employed quantitative MR parameters that are sensitive to both RBC content and oxygenation, R2* and quantitative susceptibility mapping (QSM), as well as fat fraction (FF) mapping, and evaluated the ability of modern artificial intelligence techniques to form predictions of RBC content and etiology based on these quantitative MR parameters alone and in combination with patient clinical data. First, we performed an in vitro blood clot imaging experiment, which sought to explicitly define the relationship between clot RBC content, oxygenation and our quantitative MR parameters. We show that both R2* and QSM are sensitive to RBC content and oxygenation, as expected, and that the relationship between R2* and QSM can be used to predict clot RBC content independent of oxygenation status, a necessary step for stroke thrombus characterization where oxygenation is an unknown quantity. Second, we trained a deep convolutional neural network to predict histological RBC content from ex vivo MR images of ischemic stroke thrombi. We demonstrate that a convolutional neural network is capable of RBC content prediction with 66% accuracy and 8% mean absolute error when trained on a limited thrombus dataset, and that prediction accuracy can be improved to up to 74% through data augmentation. Finally, we used a random forest classifier to predict clinical stroke etiology using the same ex vivo thrombus MR image dataset. Here, quantitative thrombus R2*, QSM and FF image texture features were used to train the classifier, and we demonstrate that this method is capable of accurate etiology prediction from thrombus texture information alone (accuracy = 67%, area under the curve (AUC) = 0.68), but that when combined with patient clinical data the performance of the classifier improves dramatically to an accuracy and AUC of 93% and 0.89, respectively. Together, the works presented in this thesis offer extensive ex vivo evidence that quantitative MR imaging is capable of effective stroke thrombus etiology characterization. Such a technique could enable clinical consideration of thrombus composition and bolster stroke etiology determination, thereby improving the management and care of acute ischemic stroke patients