14,371 research outputs found
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
Multimodal Machine Learning-based Knee Osteoarthritis Progression Prediction from Plain Radiographs and Clinical Data
Knee osteoarthritis (OA) is the most common musculoskeletal disease without a
cure, and current treatment options are limited to symptomatic relief.
Prediction of OA progression is a very challenging and timely issue, and it
could, if resolved, accelerate the disease modifying drug development and
ultimately help to prevent millions of total joint replacement surgeries
performed annually. Here, we present a multi-modal machine learning-based OA
progression prediction model that utilizes raw radiographic data, clinical
examination results and previous medical history of the patient. We validated
this approach on an independent test set of 3,918 knee images from 2,129
subjects. Our method yielded area under the ROC curve (AUC) of 0.79 (0.78-0.81)
and Average Precision (AP) of 0.68 (0.66-0.70). In contrast, a reference
approach, based on logistic regression, yielded AUC of 0.75 (0.74-0.77) and AP
of 0.62 (0.60-0.64). The proposed method could significantly improve the
subject selection process for OA drug-development trials and help the
development of personalized therapeutic plans
A Survey on Artificial Intelligence Techniques for Biomedical Image Analysis in Skeleton-Based Forensic Human Identification
This paper represents the first survey on the application of AI techniques for the analysis
of biomedical images with forensic human identification purposes. Human identification is of
great relevance in today’s society and, in particular, in medico-legal contexts. As consequence,
all technological advances that are introduced in this field can contribute to the increasing necessity
for accurate and robust tools that allow for establishing and verifying human identity. We first
describe the importance and applicability of forensic anthropology in many identification scenarios.
Later, we present the main trends related to the application of computer vision, machine learning
and soft computing techniques to the estimation of the biological profile, the identification through
comparative radiography and craniofacial superimposition, traumatism and pathology analysis,
as well as facial reconstruction. The potentialities and limitations of the employed approaches are
described, and we conclude with a discussion about methodological issues and future research.Spanish Ministry of Science, Innovation and UniversitiesEuropean Union (EU)
PGC2018-101216-B-I00Regional Government of Andalusia under grant EXAISFI
P18-FR-4262Instituto de Salud Carlos IIIEuropean Union (EU)
DTS18/00136European Commission H2020-MSCA-IF-2016 through the Skeleton-ID Marie Curie Individual Fellowship
746592Spanish Ministry of Science, Innovation and Universities-CDTI, Neotec program 2019
EXP-00122609/SNEO-20191236European Union (EU)Xunta de Galicia
ED431G 2019/01European Union (EU)
RTI2018-095894-B-I0
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A Novel Approach for the Visualisation and Progression Tracking of Metastatic Bone Disease
Metastatic bone disease (MBD) is a common secondary feature of cancer that can cause significant complications, including severe pain and death. Current methods of diagnosis require a highly trained radiologist capable of interpreting medical images and recognising the sites of MBD. These medical images are often noisy, two dimensional, greyscale and usually have a poor resolution.
In order to help assist with these issues, several studies have shown that computer aided methods can locate MBD within medical images. However these methods are limited in scope, accuracy, sensitivity, explainability and do not improve upon the poor visualisations of the underlying medical imaging data.
To address these limitations, I have developed a novel method of automatic MBD assessment and visualisation using computed tomography (CT) imaging data as the input. The method is fully automated and does not require any human interaction -- although users can interact with a viewer that visualises the results. This method has been tested on CT data from prostate cancer patients as prostate cancer is one of the most common sources of MBD.
The method described in this thesis has a sensitivity of 0.871 when detecting sclerotic and lytic lesions within a single data set. This sensitivity is comparable to existing methods, however the scope in detecting these lesions was limited to the vertebrae in previous studies. My method significantly expands this scope to include the ribs, vertebrae, pelvis and proximal femurs.
The work in this thesis also provides novel visualisations of the disease and does not suffer from explainability issues that plague modern machine learning algorithms.
In addition, I developed a novel method of tracking the spread of MBD at multiple time points using longitudinal CT data. This method is capable of calculating the change in lesion volume size across multiple time points, providing a novel numerical assessment.The Armstrong Trus
Computational Anatomy for Multi-Organ Analysis in Medical Imaging: A Review
The medical image analysis field has traditionally been focused on the
development of organ-, and disease-specific methods. Recently, the interest in
the development of more 20 comprehensive computational anatomical models has
grown, leading to the creation of multi-organ models. Multi-organ approaches,
unlike traditional organ-specific strategies, incorporate inter-organ relations
into the model, thus leading to a more accurate representation of the complex
human anatomy. Inter-organ relations are not only spatial, but also functional
and physiological. Over the years, the strategies 25 proposed to efficiently
model multi-organ structures have evolved from the simple global modeling, to
more sophisticated approaches such as sequential, hierarchical, or machine
learning-based models. In this paper, we present a review of the state of the
art on multi-organ analysis and associated computation anatomy methodology. The
manuscript follows a methodology-based classification of the different
techniques 30 available for the analysis of multi-organs and multi-anatomical
structures, from techniques using point distribution models to the most recent
deep learning-based approaches. With more than 300 papers included in this
review, we reflect on the trends and challenges of the field of computational
anatomy, the particularities of each anatomical region, and the potential of
multi-organ analysis to increase the impact of 35 medical imaging applications
on the future of healthcare.Comment: Paper under revie
Deep Learning in Cardiology
The medical field is creating large amount of data that physicians are unable
to decipher and use efficiently. Moreover, rule-based expert systems are
inefficient in solving complicated medical tasks or for creating insights using
big data. Deep learning has emerged as a more accurate and effective technology
in a wide range of medical problems such as diagnosis, prediction and
intervention. Deep learning is a representation learning method that consists
of layers that transform the data non-linearly, thus, revealing hierarchical
relationships and structures. In this review we survey deep learning
application papers that use structured data, signal and imaging modalities from
cardiology. We discuss the advantages and limitations of applying deep learning
in cardiology that also apply in medicine in general, while proposing certain
directions as the most viable for clinical use.Comment: 27 pages, 2 figures, 10 table
Automated Distinct Bone Segmentation from Computed Tomography Images using Deep Learning
Large-scale CT scans are frequently performed for forensic and diagnostic purposes, to plan and
direct surgical procedures, and to track the development of bone-related diseases. This often
involves radiologists who have to annotate bones manually or in a semi-automatic way, which is
a time consuming task. Their annotation workload can be reduced by automated segmentation
and detection of individual bones. This automation of distinct bone segmentation not only has
the potential to accelerate current workflows but also opens up new possibilities for processing
and presenting medical data for planning, navigation, and education.
In this thesis, we explored the use of deep learning for automating the segmentation of all
individual bones within an upper-body CT scan. To do so, we had to find a network architec-
ture that provides a good trade-off between the problem’s high computational demands and the
results’ accuracy. After finding a baseline method and having enlarged the dataset, we set out
to eliminate the most prevalent types of error. To do so, we introduced an novel method called
binary-prediction-enhanced multi-class (BEM) inference, separating the task into two: Distin-
guishing bone from non-bone is conducted separately from identifying the individual bones.
Both predictions are then merged, which leads to superior results. Another type of error is tack-
led by our developed architecture, the Sneaky-Net, which receives additional inputs with larger
fields of view but at a smaller resolution. We can thus sneak more extensive areas of the input
into the network while keeping the growth of additional pixels in check.
Overall, we present a deep-learning-based method that reliably segments most of the over
one hundred distinct bones present in upper-body CT scans in an end-to-end trained matter
quickly enough to be used in interactive software. Our algorithm has been included in our
groups virtual reality medical image visualisation software SpectoVR with the plan to be used
as one of the puzzle piece in surgical planning and navigation, as well as in the education of
future doctors
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