35 research outputs found
RGN-Net: A Global Contextual and Multiscale Information Association Network for Medical Image Segmentation
Segmentation of medical images is a necessity for the development of healthcare systems, particularly for illness diagnosis and treatment planning. Recently, convolutional neural networks (CNNs) have gained amazing success in automatically segmenting medical images to identify organs or lesions. However, the majority of these approaches are incapable of segmenting objects of varying sizes and training on tiny, skewed datasets, both of which are typical in biomedical applications. Existing solutions use multi-scale fusion strategies to handle the difficulties posed by varying sizes, but they often employ complicated models more suited to broad semantic segmentation computer vision issues. In this research, we present an end-to-end dual-branch split architecture RGN-Net that takes the benefits of the two networks into greater account. Our technique may successfully create long-term functional relationships and collect global context data. Experiments on Lung, MoNuSeg, and DRIVE reveal that our technique reaches state-of-the-art benchmarks in order to evaluate the performance of RGN-Net
Deep Generative Adversarial Networks: Applications in Musculoskeletal Imaging
In recent years, deep learning techniques have been applied in musculoskeletal radiology to increase the diagnostic potential of acquired images. Generative adversarial networks (GANs), which are deep neural networks that can generate or transform images, have the potential to aid in faster imaging by generating images with a high level of realism across multiple contrast and modalities from existing imaging protocols. This review introduces the key architectures of GANs as well as their technical background and challenges. Key research trends are highlighted, including: (a) reconstruction of high-resolution MRI; (b) image synthesis with different modalities and contrasts; (c) image enhancement that efficiently preserves high-frequency information suitable for human interpretation; (d) pixel-level segmentation with annotation sharing between domains; and (e) applications to different musculoskeletal anatomies. In addition, an overview is provided of the key issues wherein clinical applicability is challenging to capture with conventional performance metrics and expert evaluation. When clinically validated, GANs have the potential to improve musculoskeletal imaging.ope
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
Deep learning for fast and robust medical image reconstruction and analysis
Medical imaging is an indispensable component of modern medical research as well as clinical practice. Nevertheless, imaging techniques such as magnetic resonance imaging (MRI) and computational tomography (CT) are costly and are less accessible to the majority of the world. To make medical devices more accessible, affordable and efficient, it is crucial to re-calibrate our current imaging paradigm for smarter imaging. In particular, as medical imaging techniques have highly structured forms in the way they acquire data, they provide us with an opportunity to optimise the imaging techniques holistically by leveraging data. The central theme of this thesis is to explore different opportunities where we can exploit data and deep learning to improve the way we extract information for better, faster and smarter imaging.
This thesis explores three distinct problems. The first problem is the time-consuming nature of dynamic MR data acquisition and reconstruction. We propose deep learning methods for accelerated dynamic MR image reconstruction, resulting in up to 10-fold reduction in imaging time. The second problem is the redundancy in our current imaging pipeline. Traditionally, imaging pipeline treated acquisition, reconstruction and analysis as separate steps. However, we argue that one can approach them holistically and optimise the entire pipeline jointly for a specific target goal. To this end, we propose deep learning approaches for obtaining high fidelity cardiac MR segmentation directly from significantly undersampled data, greatly exceeding the undersampling limit for image reconstruction. The final part of this thesis tackles the problem of interpretability of the deep learning algorithms. We propose attention-models that can implicitly focus on salient regions in an image to improve accuracy for ultrasound scan plane detection and CT segmentation. More crucially, these models can provide explainability, which is a crucial stepping stone for the harmonisation of smart imaging and current clinical practice.Open Acces
Deep learning-based diagnostic system for malignant liver detection
Cancer is the second most common cause of death of human beings, whereas liver cancer is the fifth most
common cause of mortality. The prevention of deadly diseases in living beings requires timely, independent,
accurate, and robust detection of ailment by a computer-aided diagnostic (CAD) system. Executing such intelligent CAD requires some preliminary steps, including preprocessing, attribute analysis, and identification.
In recent studies, conventional techniques have been used to develop computer-aided diagnosis algorithms.
However, such traditional methods could immensely affect the structural properties of processed images with
inconsistent performance due to variable shape and size of region-of-interest. Moreover, the unavailability of sufficient datasets makes the performance of the proposed methods doubtful for commercial use.
To address these limitations, I propose novel methodologies in this dissertation. First, I modified a
generative adversarial network to perform deblurring and contrast adjustment on computed tomography
(CT) scans. Second, I designed a deep neural network with a novel loss function for fully automatic precise
segmentation of liver and lesions from CT scans. Third, I developed a multi-modal deep neural network
to integrate pathological data with imaging data to perform computer-aided diagnosis for malignant liver
detection.
The dissertation starts with background information that discusses the proposed study objectives and the workflow. Afterward, Chapter 2 reviews a general schematic for developing a computer-aided algorithm, including image acquisition techniques, preprocessing steps, feature extraction approaches, and machine learning-based prediction methods.
The first study proposed in Chapter 3 discusses blurred images and their possible effects on classification.
A novel multi-scale GAN network with residual image learning is proposed to deblur images. The second
method in Chapter 4 addresses the issue of low-contrast CT scan images. A multi-level GAN is utilized
to enhance images with well-contrast regions. Thus, the enhanced images improve the cancer diagnosis
performance. Chapter 5 proposes a deep neural network for the segmentation of liver and lesions from
abdominal CT scan images. A modified Unet with a novel loss function can precisely segment minute lesions.
Similarly, Chapter 6 introduces a multi-modal approach for liver cancer variants diagnosis. The pathological data are integrated with CT scan images to diagnose liver cancer variants.
In summary, this dissertation presents novel algorithms for preprocessing and disease detection. Furthermore,
the comparative analysis validates the effectiveness of proposed methods in computer-aided diagnosis
Measurement Variability in Treatment Response Determination for Non-Small Cell Lung Cancer: Improvements using Radiomics
Multimodality imaging measurements of treatment response are critical for clinical practice, oncology trials, and the evaluation of new treatment modalities. The current standard for determining treatment response in non-small cell lung cancer (NSCLC) is based on tumor size using the RECIST criteria. Molecular targeted agents and immunotherapies often cause morphological change without reduction of tumor size. Therefore, it is difficult to evaluate therapeutic response by conventional methods. Radiomics is the study of cancer imaging features that are extracted using machine learning and other semantic features. This method can provide comprehensive information on tumor phenotypes and can be used to assess therapeutic response in this new age of immunotherapy. Delta radiomics, which evaluates the longitudinal changes in radiomics features, shows potential in gauging treatment response in NSCLC. It is well known that quantitative measurement methods may be subject to substantial variability due to differences in technical factors and require standardization. In this review, we describe measurement variability in the evaluation of NSCLC and the emerging role of radiomics. © 2019 Wolters Kluwer Health, Inc. All rights reserved
Recuperação de informação multimodal em repositórios de imagem médica
The proliferation of digital medical imaging modalities in hospitals and other
diagnostic facilities has created huge repositories of valuable data, often
not fully explored. Moreover, the past few years show a growing trend
of data production. As such, studying new ways to index, process and
retrieve medical images becomes an important subject to be addressed by
the wider community of radiologists, scientists and engineers. Content-based
image retrieval, which encompasses various methods, can exploit the visual
information of a medical imaging archive, and is known to be beneficial to
practitioners and researchers. However, the integration of the latest systems
for medical image retrieval into clinical workflows is still rare, and their
effectiveness still show room for improvement.
This thesis proposes solutions and methods for multimodal information
retrieval, in the context of medical imaging repositories. The major
contributions are a search engine for medical imaging studies supporting
multimodal queries in an extensible archive; a framework for automated
labeling of medical images for content discovery; and an assessment and
proposal of feature learning techniques for concept detection from medical
images, exhibiting greater potential than feature extraction algorithms that
were pertinently used in similar tasks. These contributions, each in their
own dimension, seek to narrow the scientific and technical gap towards
the development and adoption of novel multimodal medical image retrieval
systems, to ultimately become part of the workflows of medical practitioners,
teachers, and researchers in healthcare.A proliferação de modalidades de imagem médica digital, em hospitais,
clínicas e outros centros de diagnóstico, levou à criação de enormes
repositórios de dados, frequentemente não explorados na sua totalidade.
Além disso, os últimos anos revelam, claramente, uma tendência para o
crescimento da produção de dados. Portanto, torna-se importante estudar
novas maneiras de indexar, processar e recuperar imagens médicas, por
parte da comunidade alargada de radiologistas, cientistas e engenheiros. A
recuperação de imagens baseada em conteúdo, que envolve uma grande
variedade de métodos, permite a exploração da informação visual num
arquivo de imagem médica, o que traz benefícios para os médicos e
investigadores. Contudo, a integração destas soluções nos fluxos de trabalho
é ainda rara e a eficácia dos mais recentes sistemas de recuperação de
imagem médica pode ser melhorada.
A presente tese propõe soluções e métodos para recuperação de informação
multimodal, no contexto de repositórios de imagem médica. As contribuições
principais são as seguintes: um motor de pesquisa para estudos de imagem
médica com suporte a pesquisas multimodais num arquivo extensível; uma
estrutura para a anotação automática de imagens; e uma avaliação e
proposta de técnicas de representation learning para deteção automática de
conceitos em imagens médicas, exibindo maior potencial do que as técnicas
de extração de features visuais outrora pertinentes em tarefas semelhantes.
Estas contribuições procuram reduzir as dificuldades técnicas e científicas
para o desenvolvimento e adoção de sistemas modernos de recuperação de
imagem médica multimodal, de modo a que estes façam finalmente parte
das ferramentas típicas dos profissionais, professores e investigadores da área
da saúde.Programa Doutoral em Informátic
Applications of Artificial Intelligence in Medicine Practice
This book focuses on a variety of interdisciplinary perspectives concerning the theory and application of artificial intelligence (AI) in medicine, medically oriented human biology, and healthcare. The list of topics includes the application of AI in biomedicine and clinical medicine, machine learning-based decision support, robotic surgery, data analytics and mining, laboratory information systems, and usage of AI in medical education. Special attention is given to the practical aspect of a study. Hence, the inclusion of a clinical assessment of the usefulness and potential impact of the submitted work is strongly highlighted
Fast upper airway magnetic resonance imaging for assessment of speech production and sleep apnea
The human upper airway is involved in various functions, including speech, swallowing, and respiration. Magnetic resonance imaging (MRI) can visualize the motion of the upper airway and has been used in scientific studies to understand the dynamics of vocal tract shaping during speech and for assessment of upper airway abnormalities related to obstructive sleep apnea and swallowing disorders. Acceleration technologies in MRI are crucial in improving spatiotemporal resolution or spatial coverage. Recent trends in technical aspects of upper airway MRI are to develop state-of-the-art image acquisition methods for improved dynamic imaging of the upper airway and develop automatic image analysis methods for efficient and accurate quantification of upper airway parameters of interest. This review covers the fast upper airway magnetic resonance (MR) acquisition and reconstruction, MR experimental issues, image analysis techniques, and applications, mainly with respect to studies of speech production and sleep apnea