Breast pectoral muscle segmentation in mammograms using a modified holistically-nested edge detection network

This paper presents a method for automatic breast pectoral muscle segmentation in mediolateral oblique mammograms using a Convolutional Neural Network (CNN) inspired by the Holistically-nested Edge Detection (HED) network. Most of the existing methods in the literature are based on hand-crafted models such as straight-line, curve-based techniques or a combination of both. Unfortunately, such models are insufficient when dealing with complex shape variations of the pectoral muscle boundary and when the boundary is unclear due to overlapping breast tissue. To compensate for these issues, we propose a neural network framework that incorporates multi-scale and multi-level learning, capable of learning complex hierarchical features to resolve spatial ambiguity in estimating the pectoral muscle boundary. For this purpose, we modified the HED network architecture to specifically find ‘contour-like’ objects in mammograms. The proposed framework produced a probability map that can be used to estimate the initial pectoral muscle boundary. Subsequently, we process these maps by extracting morphological properties to find the actual pectoral muscle boundary. Finally, we developed two different post-processing steps to find the actual pectoral muscle boundary. Quantitative evaluation results show that the proposed method is comparable with alternative state-of-the-art methods producing on average values of 94.8 ± 8.5% and 97.5 ± 6.3% for the Jaccard and Dice similarity metrics, respectively, across four different databases

Murine femur micro-computed tomography and biomechanical datasets for an ovariectomy-induced osteoporosis model

The development of new effective and safer therapies for osteoporosis, in addition to improved diagnostic and prevention strategies, represents a serious need in the scientific community. Micro-CT image-based analyses in association with biomechanical testing have become pivotal tools in identifying osteoporosis in animal models by assessment of bone microarchitecture and resistance, as well as bone strength. Here, we describe a dataset of micro-CT scans and reconstructions of 15 whole femurs and biomechanical tests on contralateral femurs from C57BL/6JOlaHsd ovariectomized (OVX), resembling human post-menopausal osteoporosis, and sham operated (sham) female mice. Data provided for each mouse include: the acquisition images (.tiff), the reconstructed images (.bmp) and an.xls file containing the maximum attenuations for each reconstructed image. Biomechanical data include an.xls file with the recorded load-displacement, a movie with the filmed test and an.xls file collecting all biomechanical results.This study was funded by Basque Country government under the ELKARTEK program No. kk-2018/00031/BC and No. kk-2019/00093/BC

La Dorada, Caldas : un lugar mágico lleno de paz y emociones mil ¡Que viva mi terruño que tanto amo! : recopilación de cuentos folclóricos

En el libro recupera cuentos folclóricos, versos, canciones, juegos y mitos producto de la tradición oral difundida en La Dorada Caldas y le da reconocimiento a los narradores de la cultura oral del poblado.In the book, he recovers folk tales, verses, songs, games and myths that are the product of the oral tradition spread in La Dorada Caldas and gives recognition to the narrators of the oral culture of the town.El fantasma -- Anécdota de la patasola -- Historia del mohán -- El pollito pio -- Nos ayudamos -- La emboscada -- Toño un amigo con diversidad -- El horripilante olvido en medio de un temblor -- Valoremos -- Mito de un arriero -- El juego de la candela -- Canción el capitán de un buque -- Versos -- Multiplicadores de la cultura oral.na66 página

TRY plant trait database – enhanced coverage and open access

Plant traits - the morphological, anatomical, physiological, biochemical and phenological characteristics of plants - determine how plants respond to environmental factors, affect other trophic levels, and influence ecosystem properties and their benefits and detriments to people. Plant trait data thus represent the basis for a vast area of research spanning from evolutionary biology, community and functional ecology, to biodiversity conservation, ecosystem and landscape management, restoration, biogeography and earth system modelling. Since its foundation in 2007, the TRY database of plant traits has grown continuously. It now provides unprecedented data coverage under an open access data policy and is the main plant trait database used by the research community worldwide. Increasingly, the TRY database also supports new frontiers of trait‐based plant research, including the identification of data gaps and the subsequent mobilization or measurement of new data. To support this development, in this article we evaluate the extent of the trait data compiled in TRY and analyse emerging patterns of data coverage and representativeness. Best species coverage is achieved for categorical traits - almost complete coverage for ‘plant growth form’. However, most traits relevant for ecology and vegetation modelling are characterized by continuous intraspecific variation and trait–environmental relationships. These traits have to be measured on individual plants in their respective environment. Despite unprecedented data coverage, we observe a humbling lack of completeness and representativeness of these continuous traits in many aspects. We, therefore, conclude that reducing data gaps and biases in the TRY database remains a key challenge and requires a coordinated approach to data mobilization and trait measurements. This can only be achieved in collaboration with other initiatives

Mis casos clínicos de especialidades odontológicas

Libro que muestra la atención de casos clínicos particulares referente a las diferentes especialidades odontológicasLibro que muestra la atención de casos clínicos particulares referente a las diferentes especialidades odontológicasUniversidad Autónoma de Campeche Universidad Autónoma del Estado de Hidalgo Universidad Autónoma del Estado de Méxic

Image analysis and deep learning to support endovascular repair of abdominal aortic aneurysms

An abdominal aortic aneurysm (AAA) is a focal dilation of the aorta that may lead to its rupture. The most common treatment for AAAs is endovascular aneurysm repair (EVAR). EVAR implies lifelong postoperative surveillance using Computed Tomography Angiography (CTA), due to the potential appearance of complications. This thesis sets the basis for intelligent CTA image analysis to support post-operative follow-up of AAAs, providing clinicians with valuable information to prognose the behavior of the aneurysm. First, novel pre-operative and post-operative AAA segmentation approaches are developed, based on Convolutional Neural Networks (CNN). Initially, 2D AAA detection and segmentation CNNs are proposed. Then, segmentation is extended to 3D to increase segmentation accuracy. Precise AAA segmentation is the basis for a good AAA follow-up. It allows to measure aneurysm volume, which is thought to be a better indicator for aneurysm rupture than the current AAA diameter measurements. Furthermore, it enables more complex analyses of AAA morphology and deformations. Subsequently, a methodology for post-operative CTA time-series registration and aneurysm biomechanical strain analysis is also proposed. From these strains, quantitative image-based descriptors are extracted and correlated with the long-term patient prognosis. The extracted descriptors are the basis for possible future imaging biomarkers to be used in clinical practice to assess patient prognosis and to enable informed decision making after EVAR. Finally, the technological developments in the thesis are applied to solve complex segmentation problems in other clinical domains, such as pectoral muscle segmentation from mammograms and pulmonary artery segmentation from CT scans. Validation of the 3D AAA segmentation approach proposed in this thesis is being carried out with the aim of integrating it in a commercial product.El aneurisma de aorta abdominal (AAA) es una dilatación focal de la aorta que puede provocar su ruptura. El tratamiento habitual es la reparación endovascular (EVAR), que conlleva un seguimiento postoperatorio de por vida en base a imágenes de angiografía por tomografía computarizada (CTA) para detectar posibles complicaciones. Esta tesis establece la base para el análisis inteligente de imágenes CTA para apoyar el seguimiento postoperatorio de los AAA, proporcionando a los profesionales médicos información valiosa para predecir el comportamiento del aneurisma. Primero, se han desarrollado algoritmos de segmentación de AAA a partir de CTA preoperatorias y postoperatorias, basados en redes neuronales convolucionales (CNN). Inicialmente, se han propuesto CNNs 2D para la detección y la segmentación de AAAs. Posteriormente, el algoritmo de segmentación se ha extendido a 3D para mejorar su precisión, ya que ésta es la base para un buen seguimiento. Permite medir el volumen del aneurisma, que se considera un mejor indicador de riesgo de ruptura del AAA que la aproximación actual en base a su diámetro. Además, permite realizar análisis más complejos de la morfología y las deformaciones del AAA. Una vez obtenida la segmentación, se ha propuesto una metodología para el registro de series de CTA postoperatorias y el subsiguiente análisis biomecánico de las deformaciones del aneurisma. Dichas deformaciones se han cuantificado mediante descriptores de imagen y se han correlacionado con el pronóstico del paciente a largo plazo. Los descriptores extraídos establecen la base para el desarrollo de futuros biomarcadores de imagen que puedan ser utilizados en la práctica clínica para evaluar el pronóstico del paciente y para dar soporte al médico en sus decisiones tras una intervención EVAR. Por último, la experiencia adquirida en la tesis ha permitido aplicar algunas de las tecnologías para la resolución de problemas de segmentación complejos en otros ámbitos médicos, como la segmentación del músculo pectoral en mamografías o la segmentación de la arteria pulmonar en CTA. Actualmente, se está llevando a cabo la validación del algoritmo de segmentación de AAA 3D propuesto en esta tesis, con el objetivo de integrarlo en un producto comercial

Image analysis and deep learning to support endovascular repair of abdominal aortic aneurysms

An abdominal aortic aneurysm (AAA) is a focal dilation of the aorta that may lead to its rupture. The most common treatment for AAAs is endovascular aneurysm repair (EVAR). EVAR implies lifelong postoperative surveillance using Computed Tomography Angiography (CTA), due to the potential appearance of complications. This thesis sets the basis for intelligent CTA image analysis to support post-operative follow-up of AAAs, providing clinicians with valuable information to prognose the behavior of the aneurysm. First, novel pre-operative and post-operative AAA segmentation approaches are developed, based on Convolutional Neural Networks (CNN). Initially, 2D AAA detection and segmentation CNNs are proposed. Then, segmentation is extended to 3D to increase segmentation accuracy. Precise AAA segmentation is the basis for a good AAA follow-up. It allows to measure aneurysm volume, which is thought to be a better indicator for aneurysm rupture than the current AAA diameter measurements. Furthermore, it enables more complex analyses of AAA morphology and deformations. Subsequently, a methodology for post-operative CTA time-series registration and aneurysm biomechanical strain analysis is also proposed. From these strains, quantitative image-based descriptors are extracted and correlated with the long-term patient prognosis. The extracted descriptors are the basis for possible future imaging biomarkers to be used in clinical practice to assess patient prognosis and to enable informed decision making after EVAR. Finally, the technological developments in the thesis are applied to solve complex segmentation problems in other clinical domains, such as pectoral muscle segmentation from mammograms and pulmonary artery segmentation from CT scans. Validation of the 3D AAA segmentation approach proposed in this thesis is being carried out with the aim of integrating it in a commercial product.El aneurisma de aorta abdominal (AAA) es una dilatación focal de la aorta que puede provocar su ruptura. El tratamiento habitual es la reparación endovascular (EVAR), que conlleva un seguimiento postoperatorio de por vida en base a imágenes de angiografía por tomografía computarizada (CTA) para detectar posibles complicaciones. Esta tesis establece la base para el análisis inteligente de imágenes CTA para apoyar el seguimiento postoperatorio de los AAA, proporcionando a los profesionales médicos información valiosa para predecir el comportamiento del aneurisma. Primero, se han desarrollado algoritmos de segmentación de AAA a partir de CTA preoperatorias y postoperatorias, basados en redes neuronales convolucionales (CNN). Inicialmente, se han propuesto CNNs 2D para la detección y la segmentación de AAAs. Posteriormente, el algoritmo de segmentación se ha extendido a 3D para mejorar su precisión, ya que ésta es la base para un buen seguimiento. Permite medir el volumen del aneurisma, que se considera un mejor indicador de riesgo de ruptura del AAA que la aproximación actual en base a su diámetro. Además, permite realizar análisis más complejos de la morfología y las deformaciones del AAA. Una vez obtenida la segmentación, se ha propuesto una metodología para el registro de series de CTA postoperatorias y el subsiguiente análisis biomecánico de las deformaciones del aneurisma. Dichas deformaciones se han cuantificado mediante descriptores de imagen y se han correlacionado con el pronóstico del paciente a largo plazo. Los descriptores extraídos establecen la base para el desarrollo de futuros biomarcadores de imagen que puedan ser utilizados en la práctica clínica para evaluar el pronóstico del paciente y para dar soporte al médico en sus decisiones tras una intervención EVAR. Por último, la experiencia adquirida en la tesis ha permitido aplicar algunas de las tecnologías para la resolución de problemas de segmentación complejos en otros ámbitos médicos, como la segmentación del músculo pectoral en mamografías o la segmentación de la arteria pulmonar en CTA. Actualmente, se está llevando a cabo la validación del algoritmo de segmentación de AAA 3D propuesto en esta tesis, con el objetivo de integrarlo en un producto comercial