Automatic segmentation of cerebral infarcts in follow-up computed tomography images with convolutional neural networks

Background and purpose: Infarct volume is a valuable outcome measure in treatment trials of acute ischemic stroke and is strongly associated with functional outcome. Its manual volumetric assessment is, however, too demanding to be implemented in clinical practice. Objective: To assess the value of convolutional neural networks (CNNs) in the automatic segmentation of infarct volume in follow-up CT images in a large population of patients with acute ischemic stroke. Materials and methods: We included CT images of 1026 patients from a large pooling of patients with acute ischemic stroke. A reference standard for the infarct segmentation was generated by manual delineation. We introduce three CNN models for the segmentation of subtle, intermediate, and severe hypodense lesions. The fully automated infarct segmentation was defined as the combination of the results of these three CNNs. The results of the three-CNNs approach were compared with the results from a single CNN approach and with the reference standard segmentations. Results: The median infarct volume was 48 mL (IQR 15–125 mL). Comparison between the volumes of the three-CNNs approach and manually delineated infarct volumes showed excellent agreement, with an intraclass correlation coefficient (ICC) of 0.88. Even better agreement was found for severe and intermediate hypodense infarcts, with ICCs of 0.98 and 0.93, respectively. Although the number of patients used for training in the single CNN approach was much larger, the accuracy of the three-CNNs approach strongly outperformed the single CNN approach, which had an ICC of 0.34. Conclusion: Convolutional neural networks are valuable and accurate in the quantitative assessment of infarct volumes, for both subtle and severe hypodense infarcts in follow-up CT images. Our proposed three-CNNs approach strongly outperforms a more straightforward single CNN approach

Automatic segmentation of cerebral infarcts in follow-up computed tomography images with convolutional neural networks

Background and purpose: Infarct volume is a valuable outcome measure in treatment trials of acute ischemic stroke and is strongly associated with functional outcome. Its manual volumetric assessment is, however, too demanding to be implemented in clinical practice. Objective: To assess the value of convolutional neural networks (CNNs) in the automatic segmentation of infarct volume in follow-up CT images in a large population of patients with acute ischemic stroke. Materials and methods: We included CT images of 1026 patients from a large pooling of patients with acute ischemic stroke. A reference standard for the infarct segmentation was generated by manual delineation. We introduce three CNN models for the segmentati

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear un derstanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5–7 vast areas of the tropics remain understudied.8–11 In the American tropics, Amazonia stands out as the world’s most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepre sented in biodiversity databases.13–15 To worsen this situation, human-induced modifications16,17 may elim inate pieces of the Amazon’s biodiversity puzzle before we can use them to understand how ecological com munities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple or ganism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region’s vulnerability to environmental change. 15%–18% of the most ne glected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lostinfo:eu-repo/semantics/publishedVersio

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear understanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5,6,7 vast areas of the tropics remain understudied.8,9,10,11 In the American tropics, Amazonia stands out as the world's most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepresented in biodiversity databases.13,14,15 To worsen this situation, human-induced modifications16,17 may eliminate pieces of the Amazon's biodiversity puzzle before we can use them to understand how ecological communities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple organism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region's vulnerability to environmental change. 15%–18% of the most neglected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lost

Simulação de transtornos de pigmentação da pele humana

Our work presents a simulation model of human pigmentation disorders. Our model is formed by a set of differential equations that defines a reaction-diffusion system. Our system simulates some features of the human pigmentary system. Changes in this system can lead to imbalances in the distribution of melanin in the skin resulting in artifacts known as pigmented lesions. Our model aims to reproduce these changes and consequently synthesize human pigmented lesions. Our reaction-diffusion system was developed based on biological data regarding human skin, pigmentary system and melanocytes life cycle. The melanocytes are the main cells involved in this type of human skin disorders. The simulation of such disorders has many applications in dermatology, for example, to assist dermatologists in diagnosis and training related to pigmentation disorders. However, our study focuses on applications related to computer graphics. Thus, we also present a method to transfer the results of our simulation model for textures and images of human skin. In this context, our model contributes to the generation of more realistic skin textures and consequently for the generation of more realistic human models. Moreover, we also compared the results of our simulation with real pigmented lesions to evaluate the quality of the lesions generated by our model. To perform this comparison we measured some features of real and synthesized pigmented lesions and we compared the results of these measurements. Based on this comparison, we observed that synthesized lesions exhibit the same characteristics of real lesions. Still, for the purpose of visual comparisons, we also present images of real lesions along with images of synthesized lesions. In this visual comparison, we can note that the method used to produce lesions images from the results of our simulation generates images that are indistinguishable from real images.Nosso trabalho apresenta um modelo de simulação de transtornos de pigmentação humana. Nosso modelo é formado por um conjunto de equações diferenciais que definem um sistema reação-difusão. Nosso sistema simula algumas características do sistema pigmentar humano. Alterações nesse sistema podem levar a desequilíbrios na distribuição de melanina na pele resultando em artefatos conhecidos como lesões de pigmentação. Nosso modelo tem como objetivo reproduzir essas alterações e assim sintetizar lesões de pigmentação humanas. Nosso sistema reação-difusão foi elaborado tomando como base dados biológicos a respeito da pele humana, do sistema pigmentar e do ciclo de vida dos melanócitos, que são as principais células envolvidas nesse tipo de transtorno. A simulação desse tipo de transtorno apresenta diversas aplicações em dermatologia como, por exemplo, suporte para o treinamento de dermatologistas e auxílio no diagnóstico de transtornos de pigmentação. No entanto, nosso trabalho se concentra em aplicações relacionadas com computação gráfica. Assim, nós também apresentamos um método para transferir os resultados do nosso modelo de simulação para texturas e imagens de pele humana. Nesse contexto, o nosso modelo contribui para a geração de texturas de pele mais realistas e consequentemente para a geração de modelos de serem humanos mais realistas. Além disso, nós também comparamos os resultados da nossa simulação com lesões de pigmentações reais objetivando avaliar a qualidade das lesões geradas pelo nosso modelo. Para realizar essa comparação nós extraímos métricas das lesões sintetizadas e das lesões reais e comparamos os valores dessas métricas. Com base nessa comparação, nós observamos que as lesões sintetizadas apresentam as mesmas características das lesões reais. Ainda, para efeito de comparações visuais, nós também apresentamos imagens de lesões reais lado a lado com imagens sintetizadas e podemos observar que o método utilizado para produzir imagens de lesões a partir do resultado do nosso modelo de simulação produz resultados que são indistinguíveis das imagens reais

Simulação de transtornos de pigmentação da pele humana

Our work presents a simulation model of human pigmentation disorders. Our model is formed by a set of differential equations that defines a reaction-diffusion system. Our system simulates some features of the human pigmentary system. Changes in this system can lead to imbalances in the distribution of melanin in the skin resulting in artifacts known as pigmented lesions. Our model aims to reproduce these changes and consequently synthesize human pigmented lesions. Our reaction-diffusion system was developed based on biological data regarding human skin, pigmentary system and melanocytes life cycle. The melanocytes are the main cells involved in this type of human skin disorders. The simulation of such disorders has many applications in dermatology, for example, to assist dermatologists in diagnosis and training related to pigmentation disorders. However, our study focuses on applications related to computer graphics. Thus, we also present a method to transfer the results of our simulation model for textures and images of human skin. In this context, our model contributes to the generation of more realistic skin textures and consequently for the generation of more realistic human models. Moreover, we also compared the results of our simulation with real pigmented lesions to evaluate the quality of the lesions generated by our model. To perform this comparison we measured some features of real and synthesized pigmented lesions and we compared the results of these measurements. Based on this comparison, we observed that synthesized lesions exhibit the same characteristics of real lesions. Still, for the purpose of visual comparisons, we also present images of real lesions along with images of synthesized lesions. In this visual comparison, we can note that the method used to produce lesions images from the results of our simulation generates images that are indistinguishable from real images.Nosso trabalho apresenta um modelo de simulação de transtornos de pigmentação humana. Nosso modelo é formado por um conjunto de equações diferenciais que definem um sistema reação-difusão. Nosso sistema simula algumas características do sistema pigmentar humano. Alterações nesse sistema podem levar a desequilíbrios na distribuição de melanina na pele resultando em artefatos conhecidos como lesões de pigmentação. Nosso modelo tem como objetivo reproduzir essas alterações e assim sintetizar lesões de pigmentação humanas. Nosso sistema reação-difusão foi elaborado tomando como base dados biológicos a respeito da pele humana, do sistema pigmentar e do ciclo de vida dos melanócitos, que são as principais células envolvidas nesse tipo de transtorno. A simulação desse tipo de transtorno apresenta diversas aplicações em dermatologia como, por exemplo, suporte para o treinamento de dermatologistas e auxílio no diagnóstico de transtornos de pigmentação. No entanto, nosso trabalho se concentra em aplicações relacionadas com computação gráfica. Assim, nós também apresentamos um método para transferir os resultados do nosso modelo de simulação para texturas e imagens de pele humana. Nesse contexto, o nosso modelo contribui para a geração de texturas de pele mais realistas e consequentemente para a geração de modelos de serem humanos mais realistas. Além disso, nós também comparamos os resultados da nossa simulação com lesões de pigmentações reais objetivando avaliar a qualidade das lesões geradas pelo nosso modelo. Para realizar essa comparação nós extraímos métricas das lesões sintetizadas e das lesões reais e comparamos os valores dessas métricas. Com base nessa comparação, nós observamos que as lesões sintetizadas apresentam as mesmas características das lesões reais. Ainda, para efeito de comparações visuais, nós também apresentamos imagens de lesões reais lado a lado com imagens sintetizadas e podemos observar que o método utilizado para produzir imagens de lesões a partir do resultado do nosso modelo de simulação produz resultados que são indistinguíveis das imagens reais