29 research outputs found
Deep Learning Paradigm and Its Bias for Coronary Artery Wall Segmentation in Intravascular Ultrasound Scans: A Closer Look
Background and motivation: Coronary artery disease (CAD) has the highest mortality rate; therefore, its diagnosis is vital. Intravascular ultrasound (IVUS) is a high-resolution imaging solution that can image coronary arteries, but the diagnosis software via wall segmentation and quantification has been evolving. In this study, a deep learning (DL) paradigm was explored along with its bias. Methods: Using a PRISMA model, 145 best UNet-based and non-UNet-based methods for wall segmentation were selected and analyzed for their characteristics and scientific and clinical validation. This study computed the coronary wall thickness by estimating the inner and outer borders of the coronary artery IVUS cross-sectional scans. Further, the review explored the bias in the DL system for the first time when it comes to wall segmentation in IVUS scans. Three bias methods, namely (i) ranking, (ii) radial, and (iii) regional area, were applied and compared using a Venn diagram. Finally, the study presented explainable AI (XAI) paradigms in the DL framework. Findings and conclusions: UNet provides a powerful paradigm for the segmentation of coronary walls in IVUS scans due to its ability to extract automated features at different scales in encoders, reconstruct the segmented image using decoders, and embed the variants in skip connections. Most of the research was hampered by a lack of motivation for XAI and pruned AI (PAI) models. None of the UNet models met the criteria for bias-free design. For clinical assessment and settings, it is necessary to move from a paper-to-practice approach
Advanced deep learning methodology for accurate, real-time segmentation of high-resolution intravascular ultrasound images
AIMS: The aim of this study is to develop and validate a deep learning (DL) methodology capable of automated and accurate segmentation of intravascular ultrasound (IVUS) image sequences in real-time. METHODS AND RESULTS: IVUS segmentation was performed by two experts who manually annotated the external elastic membrane (EEM) and lumen borders in the end-diastolic frames of 197 IVUS sequences portraying the native coronary arteries of 65 patients. The IVUS sequences of 177 randomly-selected vessels were used to train and optimise a novel DL model for the segmentation of IVUS images. Validation of the developed methodology was performed in 20 vessels using the estimations of two expert analysts as the reference standard. The mean difference for the EEM, lumen and plaque area between the DL-methodology and the analysts was â€0.23mm2 (standard deviation â€0.85mm2), while the Hausdorff and mean distance differences for the EEM and lumen borders was â€0.19 mm (standard deviationâ€0.17 mm). The agreement between DL and experts was similar to experts' agreement (Williams Index ranges: 0.754-1.061) with similar results in frames portraying calcific plaques or side branches. CONCLUSIONS: The developed DL-methodology appears accurate and capable of segmenting high-resolution real-world IVUS datasets. These features are expected to facilitate its broad adoption and enhance the applications of IVUS in clinical practice and research
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
Data-efficient deep representation learning
Current deep learning methods succeed in many data-intensive applications, but they are still not able to produce robust performance due to the lack of training samples. To investigate how to improve the performance of deep learning paradigms when training samples are limited, data-efficient deep representation learning (DDRL) is proposed in this study. DDRL as a sub area of representation learning mainly addresses the following problem: How can the performance of a deep learning method be maintained when the number of training samples is significantly reduced? This is vital for many applications where collecting data is highly costly, such as medical image analysis. Incorporating a certain kind of prior knowledge into the learning paradigm is key to achieving data efficiency.
Deep learning as a sub-area of machine learning can be divided into three parts (locations) in its learning process, namely Data, Optimisation and Model. Integrating prior knowledge into these three locations is expected to bring data efficiency into a learning paradigm, which can dramatically increase the model performance under the condition of limited training data.
In this thesis, we aim to develop novel deep learning methods for achieving data-efficient training, each of which integrates a certain kind of prior knowledge into three different locations respectively. We make the following contributions. First, we propose an iterative solution based on deep learning for medical image segmentation tasks, where dynamical systems are integrated into the segmentation labels in order to improve both performance and data efficiency. The proposed method not only shows a superior performance and better data efficiency compared to the state-of-the-art methods, but also has better interpretability and rotational invariance which are desired for medical imagining applications. Second, we propose a novel training framework which adaptively selects more informative samples for training during the optimization process. The adaptive selection or sampling is performed based on a hardness-aware strategy in the latent space constructed by a generative model.
We show that the proposed framework outperforms a random sampling method, which demonstrates effectiveness of the proposed framework. Thirdly, we propose a deep neural network model which produces the segmentation maps in a coarse-to-fine manner. The proposed architecture is a sequence of computational blocks containing a number of convolutional layers in which each block provides its successive block with a coarser segmentation map as a reference. Such mechanisms enable us to train the network with limited training samples and produce more interpretable results.Open Acces
Automatic Spatiotemporal Analysis of Cardiac Image Series
RĂSUMĂ
Ă ce jour, les maladies cardiovasculaires demeurent au premier rang des principales causes de
dĂ©cĂšs en AmĂ©rique du Nord. Chez lâadulte et au sein de populations de plus en plus jeunes,
la soi-disant Ă©pidĂ©mie dâobĂ©sitĂ© entraĂźnĂ©e par certaines habitudes de vie tels que la mauvaise
alimentation, le manque dâexercice et le tabagisme est lourde de consĂ©quences pour les personnes
affectées, mais aussi sur le systÚme de santé. La principale cause de morbidité et de
mortalitĂ© chez ces patients est lâathĂ©rosclĂ©rose, une accumulation de plaque Ă lâintĂ©rieur des
vaisseaux sanguins à hautes pressions telles que les artÚres coronaires. Les lésions athérosclérotiques
peuvent entraĂźner lâischĂ©mie en bloquant la circulation sanguine et/ou en provoquant
une thrombose. Cela mĂšne souvent Ă de graves consĂ©quences telles quâun infarctus. Outre les
problÚmes liés à la sténose, les parois artérielles des régions criblées de plaque augmentent la
rigidité des parois vasculaires, ce qui peut aggraver la condition du patient. Dans la population
pédiatrique, la pathologie cardiovasculaire acquise la plus fréquente est la maladie de
Kawasaki. Il sâagit dâune vasculite aigĂŒe pouvant affecter lâintĂ©gritĂ© structurale des parois des
artĂšres coronaires et mener Ă la formation dâanĂ©vrismes. Dans certains cas, ceux-ci entravent
lâhĂ©modynamie artĂ©rielle en engendrant une perfusion myocardique insuffisante et en activant
la formation de thromboses.
Le diagnostic de ces deux maladies coronariennes sont traditionnellement effectuĂ©s Ă lâaide
dâangiographies par fluoroscopie. Pendant ces examens paracliniques, plusieurs centaines de
projections radiographiques sont acquises en sĂ©ries suite Ă lâinfusion artĂ©rielle dâun agent de
contraste. Ces images révÚlent la lumiÚre des vaisseaux sanguins et la présence de lésions
potentiellement pathologiques, sâil y a lieu. Parce que les sĂ©ries acquises contiennent de lâinformation
trĂšs dynamique en termes de mouvement du patient volontaire et involontaire (ex.
battements cardiaques, respiration et dĂ©placement dâorganes), le clinicien base gĂ©nĂ©ralement
son interprĂ©tation sur une seule image angiographique oĂč des mesures gĂ©omĂ©triques sont effectuĂ©es
manuellement ou semi-automatiquement par un technicien en radiologie. Bien que
lâangiographie par fluoroscopie soit frĂ©quemment utilisĂ© partout dans le monde et souvent
considĂ©rĂ© comme lâoutil de diagnostic âgold-standardâ pour de nombreuses maladies vasculaires,
la nature bidimensionnelle de cette modalitĂ© dâimagerie est malheureusement trĂšs
limitante en termes de spécification géométrique des différentes régions pathologiques. En effet,
la structure tridimensionnelle des stĂ©noses et des anĂ©vrismes ne peut pas ĂȘtre pleinement
appréciée en 2D car les caractéristiques observées varient selon la configuration angulaire de
lâimageur. De plus, la prĂ©sence de lĂ©sions affectant les artĂšres coronaires peut ne pas reflĂ©ter
la véritable santé du myocarde, car des mécanismes compensatoires naturels (ex. vaisseaux----------ABSTRACT
Cardiovascular disease continues to be the leading cause of death in North America. In adult
and, alarmingly, ever younger populations, the so-called obesity epidemic largely driven by
lifestyle factors that include poor diet, lack of exercise and smoking, incurs enormous stresses
on the healthcare system. The primary cause of serious morbidity and mortality for these
patients is atherosclerosis, the build up of plaque inside high pressure vessels like the coronary
arteries. These lesions can lead to ischemic disease and may progress to precarious blood
flow blockage or thrombosis, often with infarction or other severe consequences. Besides
the stenosis-related outcomes, the arterial walls of plaque-ridden regions manifest increased
stiffness, which may exacerbate negative patient prognosis. In pediatric populations, the
most prevalent acquired cardiovascular pathology is Kawasaki disease. This acute vasculitis
may affect the structural integrity of coronary artery walls and progress to aneurysmal lesions.
These can hinder the blood flowâs hemodynamics, leading to inadequate downstream
perfusion, and may activate thrombus formation which may lead to precarious prognosis.
Diagnosing these two prominent coronary artery diseases is traditionally performed using
fluoroscopic angiography. Several hundred serial x-ray projections are acquired during selective
arterial infusion of a radiodense contrast agent, which reveals the vesselsâ luminal
area and possible pathological lesions. The acquired series contain highly dynamic information
on voluntary and involuntary patient movement: respiration, organ displacement and
heartbeat, for example. Current clinical analysis is largely limited to a single angiographic
image where geometrical measures will be performed manually or semi-automatically by a
radiological technician. Although widely used around the world and generally considered
the gold-standard diagnosis tool for many vascular diseases, the two-dimensional nature of
this imaging modality is limiting in terms of specifying the geometry of various pathological
regions. Indeed, the 3D structures of stenotic or aneurysmal lesions may not be fully appreciated
in 2D because their observable features are dependent on the angular configuration of
the imaging gantry. Furthermore, the presence of lesions in the coronary arteries may not
reflect the true health of the myocardium, as natural compensatory mechanisms may obviate
the need for further intervention. In light of this, cardiac magnetic resonance perfusion
imaging is increasingly gaining attention and clinical implementation, as it offers a direct
assessment of myocardial tissue viability following infarction or suspected coronary artery
disease. This type of modality is plagued, however, by motion similar to that present in fluoroscopic
imaging. This issue predisposes clinicians to laborious manual intervention in order
to align anatomical structures in sequential perfusion frames, thus hindering automation o
Automatic Spatiotemporal Analysis of Cardiac Image Series
RĂSUMĂ
Ă ce jour, les maladies cardiovasculaires demeurent au premier rang des principales causes de
dĂ©cĂšs en AmĂ©rique du Nord. Chez lâadulte et au sein de populations de plus en plus jeunes,
la soi-disant Ă©pidĂ©mie dâobĂ©sitĂ© entraĂźnĂ©e par certaines habitudes de vie tels que la mauvaise
alimentation, le manque dâexercice et le tabagisme est lourde de consĂ©quences pour les personnes
affectées, mais aussi sur le systÚme de santé. La principale cause de morbidité et de
mortalitĂ© chez ces patients est lâathĂ©rosclĂ©rose, une accumulation de plaque Ă lâintĂ©rieur des
vaisseaux sanguins à hautes pressions telles que les artÚres coronaires. Les lésions athérosclérotiques
peuvent entraĂźner lâischĂ©mie en bloquant la circulation sanguine et/ou en provoquant
une thrombose. Cela mĂšne souvent Ă de graves consĂ©quences telles quâun infarctus. Outre les
problÚmes liés à la sténose, les parois artérielles des régions criblées de plaque augmentent la
rigidité des parois vasculaires, ce qui peut aggraver la condition du patient. Dans la population
pédiatrique, la pathologie cardiovasculaire acquise la plus fréquente est la maladie de
Kawasaki. Il sâagit dâune vasculite aigĂŒe pouvant affecter lâintĂ©gritĂ© structurale des parois des
artĂšres coronaires et mener Ă la formation dâanĂ©vrismes. Dans certains cas, ceux-ci entravent
lâhĂ©modynamie artĂ©rielle en engendrant une perfusion myocardique insuffisante et en activant
la formation de thromboses.
Le diagnostic de ces deux maladies coronariennes sont traditionnellement effectuĂ©s Ă lâaide
dâangiographies par fluoroscopie. Pendant ces examens paracliniques, plusieurs centaines de
projections radiographiques sont acquises en sĂ©ries suite Ă lâinfusion artĂ©rielle dâun agent de
contraste. Ces images révÚlent la lumiÚre des vaisseaux sanguins et la présence de lésions
potentiellement pathologiques, sâil y a lieu. Parce que les sĂ©ries acquises contiennent de lâinformation
trĂšs dynamique en termes de mouvement du patient volontaire et involontaire (ex.
battements cardiaques, respiration et dĂ©placement dâorganes), le clinicien base gĂ©nĂ©ralement
son interprĂ©tation sur une seule image angiographique oĂč des mesures gĂ©omĂ©triques sont effectuĂ©es
manuellement ou semi-automatiquement par un technicien en radiologie. Bien que
lâangiographie par fluoroscopie soit frĂ©quemment utilisĂ© partout dans le monde et souvent
considĂ©rĂ© comme lâoutil de diagnostic âgold-standardâ pour de nombreuses maladies vasculaires,
la nature bidimensionnelle de cette modalitĂ© dâimagerie est malheureusement trĂšs
limitante en termes de spécification géométrique des différentes régions pathologiques. En effet,
la structure tridimensionnelle des stĂ©noses et des anĂ©vrismes ne peut pas ĂȘtre pleinement
appréciée en 2D car les caractéristiques observées varient selon la configuration angulaire de
lâimageur. De plus, la prĂ©sence de lĂ©sions affectant les artĂšres coronaires peut ne pas reflĂ©ter
la véritable santé du myocarde, car des mécanismes compensatoires naturels (ex. vaisseaux----------ABSTRACT
Cardiovascular disease continues to be the leading cause of death in North America. In adult
and, alarmingly, ever younger populations, the so-called obesity epidemic largely driven by
lifestyle factors that include poor diet, lack of exercise and smoking, incurs enormous stresses
on the healthcare system. The primary cause of serious morbidity and mortality for these
patients is atherosclerosis, the build up of plaque inside high pressure vessels like the coronary
arteries. These lesions can lead to ischemic disease and may progress to precarious blood
flow blockage or thrombosis, often with infarction or other severe consequences. Besides
the stenosis-related outcomes, the arterial walls of plaque-ridden regions manifest increased
stiffness, which may exacerbate negative patient prognosis. In pediatric populations, the
most prevalent acquired cardiovascular pathology is Kawasaki disease. This acute vasculitis
may affect the structural integrity of coronary artery walls and progress to aneurysmal lesions.
These can hinder the blood flowâs hemodynamics, leading to inadequate downstream
perfusion, and may activate thrombus formation which may lead to precarious prognosis.
Diagnosing these two prominent coronary artery diseases is traditionally performed using
fluoroscopic angiography. Several hundred serial x-ray projections are acquired during selective
arterial infusion of a radiodense contrast agent, which reveals the vesselsâ luminal
area and possible pathological lesions. The acquired series contain highly dynamic information
on voluntary and involuntary patient movement: respiration, organ displacement and
heartbeat, for example. Current clinical analysis is largely limited to a single angiographic
image where geometrical measures will be performed manually or semi-automatically by a
radiological technician. Although widely used around the world and generally considered
the gold-standard diagnosis tool for many vascular diseases, the two-dimensional nature of
this imaging modality is limiting in terms of specifying the geometry of various pathological
regions. Indeed, the 3D structures of stenotic or aneurysmal lesions may not be fully appreciated
in 2D because their observable features are dependent on the angular configuration of
the imaging gantry. Furthermore, the presence of lesions in the coronary arteries may not
reflect the true health of the myocardium, as natural compensatory mechanisms may obviate
the need for further intervention. In light of this, cardiac magnetic resonance perfusion
imaging is increasingly gaining attention and clinical implementation, as it offers a direct
assessment of myocardial tissue viability following infarction or suspected coronary artery
disease. This type of modality is plagued, however, by motion similar to that present in fluoroscopic
imaging. This issue predisposes clinicians to laborious manual intervention in order
to align anatomical structures in sequential perfusion frames, thus hindering automation o
Vascular Segmentation Algorithms for Generating 3D Atherosclerotic Measurements
Atherosclerosis manifests as plaques within large arteries of the body and remains as a leading cause of mortality and morbidity in the world. Major cardiovascular events may occur in patients without known preexisting symptoms, thus it is important to monitor progression and regression of the plaque burden in the arteries for evaluating patient\u27s response to therapy. In this dissertation, our main focus is quantification of plaque burden from the carotid and femoral arteries, which are major sites for plaque formation, and are straight forward to image noninvasively due to their superficial location. Recently, 3D measurements of plaque burden have shown to be more sensitive to the changes of plaque burden than one-/two-dimensional measurements. However, despite the advancements of 3D noninvasive imaging technology with rapid acquisition capabilities, and the high sensitivity of the 3D plaque measurements of plaque burden, they are still not widely used due to the inordinate amount of time and effort required to delineate artery walls plus plaque boundaries to obtain 3D measurements from the images. Therefore, the objective of this dissertation is developing novel semi-automated segmentation methods to alleviate measurement burden from the observer for segmentation of the outer wall and lumen boundaries from: (1) 3D carotid ultrasound (US) images, (2) 3D carotid black-blood magnetic resonance (MR) images, and (3) 3D femoral black-blood MR images.
Segmentation of the carotid lumen and outer wall from 3DUS images is a challenging task due to low image contrast, for which no method has been previously reported. Initially, we developed a 2D slice-wise segmentation algorithm based on the level set method, which was then extended to 3D. The 3D algorithm required fewer user interactions than manual delineation and the 2D method. The algorithm reduced user time by â79% (1.72 vs. 8.3 min) compared to manual segmentation for generating 3D-based measurements with high accuracy (Dice similarity coefficient (DSC)\u3e90%). Secondly, we developed a novel 3D multi-region segmentation algorithm, which simultaneously delineates both the carotid lumen and outer wall surfaces from MR images by evolving two coupled surfaces using a convex max-flow-based technique. The algorithm required user interaction only on a single transverse slice of the 3D image for generating 3D surfaces of the lumen and outer wall. The algorithm was parallelized using graphics processing units (GPU) to increase computational speed, thus reducing user time by 93% (0.78 vs. 12 min) compared to manual segmentation. Moreover, the algorithm yielded high accuracy (DSC \u3e 90%) and high precision (intra-observer CV \u3c 5.6% and inter-observer CV \u3c 6.6%). Finally, we developed and validated an algorithm based on convex max-flow formulation to segment the femoral arteries that enforces a tubular shape prior and an inter-surface consistency of the outer wall and lumen to maintain a minimum separation distance between the two surfaces. The algorithm required the observer to choose only about 11 points on its medial axis of the artery to yield the 3D surfaces of the lumen and outer wall, which reduced the operator time by 97% (1.8 vs. 70-80 min) compared to manual segmentation. Furthermore, the proposed algorithm reported DSC greater than 85% and small intra-observer variability (CV â 6.69%). In conclusion, the development of robust semi-automated algorithms for generating 3D measurements of plaque burden may accelerate translation of 3D measurements to clinical trials and subsequently to clinical care