930 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
deep learning based segmentation of breast masses in dedicated breast ct imaging radiomic feature stability between radiologists and artificial intelligence
Abstract A deep learning (DL) network for 2D-based breast mass segmentation in unenhanced dedicated breast CT images was developed and validated, and its robustness in radiomic feature stability and diagnostic performance compared to manual annotations of multiple radiologists was investigated. 93 mass-like lesions were extensively augmented and used to train the network (n = 58 masses), which was then tested (n = 35 masses) against manual ground truth of a qualified breast radiologist with experience in breast CT imaging using the Conformity coefficient (with a value equal to 1 indicating a perfect performance). Stability and diagnostic power of 672 radiomic descriptors were investigated between the computerized segmentation, and 4 radiologists' annotations for the 35 test set cases. Feature stability and diagnostic performance in the discrimination between benign and malignant cases were quantified using intraclass correlation (ICC) and multivariate analysis of variance (MANOVA), performed for each segmentation case (4 radiologists and DL algorithm). DL-based segmentation resulted in a Conformity of 0.85 ± 0.06 against the annotated ground truth. For the stability analysis, although modest agreement was found among the four annotations performed by radiologists (Conformity 0.78 ± 0.03), over 90% of all radiomic features were found to be stable (ICC>0.75) across multiple segmentations. All MANOVA analyses were statistically significant (p ≤ 0.05), with all dimensions equal to 1, and Wilks' lambda ≤0.35. In conclusion, DL-based mass segmentation in dedicated breast CT images can achieve high segmentation performance, and demonstrated to provide stable radiomic descriptors with comparable discriminative power in the classification of benign and malignant tumors to expert radiologist annotation
Computer-aided detection and diagnosis of breast cancer in 2D and 3D medical imaging through multifractal analysis
This Thesis describes the research work performed in the scope of a doctoral research program
and presents its conclusions and contributions. The research activities were carried on in the
industry with Siemens S.A. Healthcare Sector, in integration with a research team.
Siemens S.A. Healthcare Sector is one of the world biggest suppliers of products, services and
complete solutions in the medical sector. The company offers a wide selection of diagnostic
and therapeutic equipment and information systems. Siemens products for medical imaging and
in vivo diagnostics include: ultrasound, computer tomography, mammography, digital breast tomosynthesis,
magnetic resonance, equipment to angiography and coronary angiography, nuclear
imaging, and many others.
Siemens has a vast experience in Healthcare and at the beginning of this project it was strategically
interested in solutions to improve the detection of Breast Cancer, to increase its competitiveness
in the sector.
The company owns several patents related with self-similarity analysis, which formed the background
of this Thesis. Furthermore, Siemens intended to explore commercially the computer-
aided automatic detection and diagnosis eld for portfolio integration. Therefore, with the
high knowledge acquired by University of Beira Interior in this area together with this Thesis,
will allow Siemens to apply the most recent scienti c progress in the detection of the breast
cancer, and it is foreseeable that together we can develop a new technology with high potential.
The project resulted in the submission of two invention disclosures for evaluation in Siemens
A.G., two articles published in peer-reviewed journals indexed in ISI Science Citation Index,
two other articles submitted in peer-reviewed journals, and several international conference
papers. This work on computer-aided-diagnosis in breast led to innovative software and novel
processes of research and development, for which the project received the Siemens Innovation
Award in 2012.
It was very rewarding to carry on such technological and innovative project in a socially sensitive
area as Breast Cancer.No cancro da mama a deteção precoce e o diagnóstico correto são de extrema importância na
prescrição terapêutica e caz e e ciente, que potencie o aumento da taxa de sobrevivência Ã
doença. A teoria multifractal foi inicialmente introduzida no contexto da análise de sinal e a
sua utilidade foi demonstrada na descrição de comportamentos siológicos de bio-sinais e até
na deteção e predição de patologias. Nesta Tese, três métodos multifractais foram estendidos
para imagens bi-dimensionais (2D) e comparados na deteção de microcalci cações em mamogramas.
Um destes métodos foi também adaptado para a classi cação de massas da mama, em
cortes transversais 2D obtidos por ressonância magnética (RM) de mama, em grupos de massas
provavelmente benignas e com suspeição de malignidade. Um novo método de análise multifractal
usando a lacunaridade tri-dimensional (3D) foi proposto para classi cação de massas da
mama em imagens volumétricas 3D de RM de mama. A análise multifractal revelou diferenças
na complexidade subjacente às localizações das microcalci cações em relação aos tecidos normais,
permitindo uma boa exatidão da sua deteção em mamogramas. Adicionalmente, foram
extraÃdas por análise multifractal caracterÃsticas dos tecidos que permitiram identi car os casos
tipicamente recomendados para biópsia em imagens 2D de RM de mama. A análise multifractal
3D foi e caz na classi cação de lesões mamárias benignas e malignas em imagens 3D de RM de
mama. Este método foi mais exato para esta classi cação do que o método 2D ou o método
padrão de análise de contraste cinético tumoral. Em conclusão, a análise multifractal fornece
informação útil para deteção auxiliada por computador em mamogra a e diagnóstico auxiliado
por computador em imagens 2D e 3D de RM de mama, tendo o potencial de complementar a
interpretação dos radiologistas
Texture analysis and Its applications in biomedical imaging: a survey
Texture analysis describes a variety of image analysis techniques that quantify the variation in intensity
and pattern. This paper provides an overview of several texture analysis approaches addressing the rationale supporting them, their advantages, drawbacks, and applications.
This survey’s emphasis is in collecting and categorising over five decades of active research on texture analysis.Brief descriptions of different approaches are presented along with application examples. From a broad range of texture analysis applications, this survey’s final focus is on biomedical image analysis. An up-to-date list of biological tissues and organs in which disorders produce texture changes that may be used to spot disease onset and progression is provided. Finally, the role of texture analysis methods as biomarkers of disease is summarised.Manuscript received February 3, 2021; revised June 23, 2021; accepted September 21, 2021. Date of publication September 27, 2021;
date of current version January 24, 2022. This work was supported in
part by the Portuguese Foundation for Science and Technology (FCT)
under Grants PTDC/EMD-EMD/28039/2017, UIDB/04950/2020, PestUID/NEU/04539/2019, and CENTRO-01-0145-FEDER-000016 and by
FEDER-COMPETE under Grant POCI-01-0145-FEDER-028039. (Corresponding author: Rui Bernardes.)info:eu-repo/semantics/publishedVersio
Segmentação de massas em ultrasons peitorais usando técnicas de multiresolução
A imagem de ultrasons é uma ferramenta de diagnóstico importante e cada vez mais aplicada na deteção do cancro da mama. No entanto, este tipo de exame é, intrinsecamente, degradado por ruÃdo e pelo baixo contraste, resultando em di culdades na deteção de massas ou nódulos e, acima de tudo, na avaliação do seu tamanho e forma. Neste sentido, as técnicas de diagnóstico assistido por computador surgem como um factor de suporte importante para a análise
deste tipo de imagem.
No presente trabalho, uma abordagem bifaseada para um método de segmentação de ultrasons mamários, totalmente automático, é apresentada. A primeira etapa procura realizar uma segmentação inicial da imagem, que permita a localização primária da Região de Interesse (ROI).
A segunda parte foca-se na área de nida na etapa anterior, tendo como objectivo a melhoria da resolução espacial da segmentação.
Na primeira etapa de segmentação, diversas técnicas de classi cação binária são aplicadas para realizar a segmentação da imagem, utilizando caracterÃsticas multiresolução para o descriptor de pixel - ltragem FIR passa-banda e difusão não linear e curvatura scale-space de alta escala.
Estas técnicas de processamento de imagem são aplicadas para a redução da in uência dos
componentes de ruÃdo inerentes aos ultrasons e, simultaneamente, recolher informação estrutural e estatÃstica adequada para a segmentação das massas. Os dados são classi cados usando Support Vector Machines e Análise Discriminante.
Na segunda fase, as máscaras obtidas a partir da segmentação inicial são dilatadas, produzindo uma área restrita que contém a ROI. Considerando apenas os pixéis pertencentes a esta região, uma nova segmentação é executada, através do algoritmo AdaBoost, usando a difusão não linear e curvaturas de menor escala. Um algoritmo de contornos activos é, também, aplicado para melhorar os resultados da segmentação, sendo as máscaras da segmentação inicial utilizadas
como contornos iniciais.
Os resultados nais con rmam a metodologia proposta como sendo uma solução promissora para a segmentação de massas em imagens de ultrasons da mama, revelando, em termos globais, bons resultados de acurácia - 97,58% (AdaBoost) e 97,70% (Contornos Activos) -, sensibilidade - 76,46% (AdaBoost) e 75,40% (Contornos Activos) - e de precisão - 87,26% (AdaBoost) e 87,51%
(Contornos Activos).Breast ultrasound imaging is an important and increasingly applied diagnostic tool for breast
cancer detection. However, this type of exam is intrinsically degraded by noise, resulting in a
dif cult detection of masses or nodules, and, most importantly, the evaluation of their size and
shape. Computer-aided diagnosis arises as a major help factor, for the analysis of this type of
medical imaging.
In this work, a two-stage approach towards a fully automated BUS segmentation method is
presented. The rst stage attempts an initial segmentation of the BUS image, used to track
the ROI. The second part focuses on the area surrounding the ROI de ned in the rst stage,
improving the spatial resolution of the segmentation.
In the rst segmentation stage, several binary class cation techniques are applied to perform
image segmentation, using multi-resolution features to construct the pixel descriptor - FIR bandpass ltering and high scale non-linear diffusion and scale-space curvature. These processing
techniques were chosen to reduce the in uence of noise components that are inherent to ultrasound images and, simultaneously, select structural and statistical information suitable for the
segmentation of masses. The data is classi ed using Support Vector Machines and Discriminant
Analysis.
In the second stage, the masks obtained from the initial segmentation are dilated, yielding a
restricted area containing the ROI. Considering only the pixels inside this region, a new segmentation task is performed. The images are classifed using an AdaBoost classi er, using lower
scale non-linear diffusion and scale-space curvature measures. Active contours are also used
to improve the segmentation results, being the initial segmentation masks are used as initial
contours.
Final results con rm the proposed methods as a promising solution for mass segmentation in BUS
images, achieving good overall accuracy - 97.58% for (AdaBoost) and 97.70% (Active Contours)
-, recall - 76.46% (AdaBoost) and 75.40% (Active Contours) - and precision - 87.26% (AdaBoost)
and 87.51% (Active Contours) - results~
Analyzing the breast tissue in mammograms using deep learning
La densitat mamogrà fica de la mama (MBD) reflecteix la quantitat d'à rea fibroglandular del teixit mamari que apareix blanca i brillant a les mamografies, comunament coneguda com a densitat percentual de la mama (PD%). El MBD és un factor de risc per al cà ncer de mama i un factor de risc per emmascarar tumors. Tot i això, l'estimació precisa de la DMO amb avaluació visual continua sent un repte a causa del contrast feble i de les variacions significatives en els teixits grassos de fons en les mamografies. A més, la interpretació correcta de les imatges de mamografia requereix experts mèdics altament capacitats: És difÃcil, laboriós, car i propens a errors. No obstant això, el teixit mamari dens pot dificultar la identificació del cà ncer de mama i associar-se amb un risc més gran de cà ncer de mama. Per exemple, s'ha informat que les dones amb una alta densitat mamà ria en comparació amb les dones amb una densitat mamà ria baixa tenen un risc de quatre a sis vegades més gran de desenvolupar la malaltia.
La clau principal de la computació de densitat de mama i la classificació de densitat de mama és detectar correctament els teixits densos a les imatges mamogrà fiques. S'han proposat molts mètodes per estimar la densitat mamà ria; no obstant això, la majoria no estan automatitzats. A més, s'han vist greument afectats per la baixa relació senyal-soroll i la variabilitat de la densitat en aparença i textura.
Seria més útil tenir un sistema de diagnòstic assistit per ordinador (CAD) per ajudar el metge a analitzar-lo i diagnosticar-lo automà ticament. El desenvolupament actual de mètodes daprenentatge profund ens motiva a millorar els sistemes actuals danà lisi de densitat mamà ria.
L'enfocament principal de la present tesi és desenvolupar un sistema per automatitzar l'anà lisi de densitat de la mama ( tal com; Segmentació de densitat de mama (BDS), percentatge de densitat de mama (BDP) i classificació de densitat de mama (BDC) ), utilitzant tècniques d'aprenentatge profund i aplicant-la a les mamografies temporals després del tractament per analitzar els canvis de densitat de mama per trobar un pacient perillós i sospitós.La densidad mamográfica de la mama (MBD) refleja la cantidad de área fibroglandular del tejido mamario que aparece blanca y brillante en las mamografÃas, comúnmente conocida como densidad porcentual de la mama (PD%). El MBD es un factor de riesgo para el cáncer de mama y un factor de riesgo para enmascarar tumores. Sin embargo, la estimación precisa de la DMO con evaluación visual sigue siendo un reto debido al contraste débil y a las variaciones significativas en los tejidos grasos de fondo en las mamografÃas. Además, la interpretación correcta de las imágenes de mamografÃa requiere de expertos médicos altamente capacitados: Es difÃcil, laborioso, caro y propenso a errores. Sin embargo, el tejido mamario denso puede dificultar la identificación del cáncer de mama y asociarse con un mayor riesgo de cáncer de mama. Por ejemplo, se ha informado que las mujeres con una alta densidad mamaria en comparación con las mujeres con una densidad mamaria baja tienen un riesgo de cuatro a seis veces mayor de desarrollar la enfermedad.
La clave principal de la computación de densidad de mama y la clasificación de densidad de mama es detectar correctamente los tejidos densos en las imágenes mamográficas. Se han propuesto muchos métodos para la estimación de la densidad mamaria; sin embargo, la mayorÃa de ellos no están automatizados. Además, se han visto gravemente afectados por la baja relación señal-ruido y la variabilidad de la densidad en apariencia y textura.
SerÃa más útil disponer de un sistema de diagnóstico asistido por ordenador (CAD) para ayudar al médico a analizarlo y diagnosticarlo automáticamente. El desarrollo actual de métodos de aprendizaje profundo nos motiva a mejorar los sistemas actuales de análisis de densidad mamaria.
El enfoque principal de la presente tesis es desarrollar un sistema para automatizar el análisis de densidad de la mama ( tal como; Segmentación de densidad de mama (BDS), porcentaje de densidad de mama (BDP) y clasificación de densidad de mama (BDC)), utilizando técnicas de aprendizaje profundo y aplicándola en las mamografÃas temporales después del tratamiento para analizar los cambios de densidad de mama para encontrar un paciente peligroso y sospechoso.Mammographic breast density (MBD) reflects the amount of fibroglandular breast tissue area that appears white and bright on mammograms, commonly referred to as breast percent density (PD%). MBD is a risk factor for breast cancer and a risk factor for masking tumors. However, accurate MBD estimation with visual assessment is still a challenge due to faint contrast and significant variations in background fatty tissues in mammograms. In addition, correctly interpreting mammogram images requires highly trained medical experts: it is difficult, time-consuming, expensive, and error-prone. Nevertheless, dense breast tissue can make it harder to identify breast cancer and be associated with an increased risk of breast cancer. For example, it has been reported that women with a high breast density compared to women with a low breast density have a four- to six-fold increased risk of developing the disease.
The primary key of breast density computing and breast density classification is to detect the dense tissues in the mammographic images correctly. Many methods have been proposed for breast density estimation; however, most are not automated. Besides, they have been badly affected by low signal-to-noise ratio and variability of density in appearance and texture. It would be more helpful to have a computer-aided diagnosis (CAD) system to assist the doctor analyze and diagnosing it automatically. Current development in deep learning methods motivates us to improve current breast density analysis systems.
The main focus of the present thesis is to develop a system for automating the breast density analysis ( such as; breast density segmentation(BDS), breast density percentage (BDP), and breast density classification ( BDC)), using deep learning techniques and applying it on the temporal mammograms after treatment for analyzing the breast density changes to find a risky and suspicious patient
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
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