Adversarial training and dilated convolutions for brain MRI segmentation

Convolutional neural networks (CNNs) have been applied to various automatic image segmentation tasks in medical image analysis, including brain MRI segmentation. Generative adversarial networks have recently gained popularity because of their power in generating images that are difficult to distinguish from real images. In this study we use an adversarial training approach to improve CNN-based brain MRI segmentation. To this end, we include an additional loss function that motivates the network to generate segmentations that are difficult to distinguish from manual segmentations. During training, this loss function is optimised together with the conventional average per-voxel cross entropy loss. The results show improved segmentation performance using this adversarial training procedure for segmentation of two different sets of images and using two different network architectures, both visually and in terms of Dice coefficients.Comment: MICCAI 2017 Workshop on Deep Learning in Medical Image Analysi

Isointense infant brain MRI segmentation with a dilated convolutional neural network

Quantitative analysis of brain MRI at the age of 6 months is difficult because of the limited contrast between white matter and gray matter. In this study, we use a dilated triplanar convolutional neural network in combination with a non-dilated 3D convolutional neural network for the segmentation of white matter, gray matter and cerebrospinal fluid in infant brain MR images, as provided by the MICCAI grand challenge on 6-month infant brain MRI segmentation.Comment: MICCAI grand challenge on 6-month infant brain MRI segmentatio

The European Innovation Partnership: opportunities for innovation in organic farming and agroecology

The European Innovation Partnership for Agricultural Productivity and Sustainability, or EIP-AGRI, is a new policy instrument for more stakeholder and demand-driven research & innovation in agriculture. It contains several elements that are supportive of organic farming and agroecological innovation. The organic sector, with its history of strong collaboration across disciplines and between researchers and producers, should take advantage of the opportunities the EIP-AGRI offers. This dossier is there to help the organic sector and the agroecological community understand the implementation of the EIP-AGRI. After a general introduction to the EIP-AGRI in the first chapter, the concepts behind the new approach to innovation are explained. The third chapter addresses the EIP-AGRI activities at EU level. An important part of the work will have to be done, however, by the rural development programmes at the national or regional levels. This is explained in the fourth chapter. Whilst each Member State will take its own approach, the EIP-AGRI is all about learning from each other. Therefore, the fifth chapter describes a number of interesting initiatives in the Member States. The dossier ends with an overview of the wide range of innovations with which the organic sector can contribute to the EIP-AGRI

Organic in Europe: Prospects and Developments

In 2016/2017 the European organic food and farming sector continued to excel both in terms of organic production and market growth as well as the latest developments in European Union (EU) food and farming policy. Data for 2016 shows the European organic food market recording significant growth – increasing by 11.4 percent (EU: 12.0 percent). At the same time, the organic sector faces a number of challenges, notably that the growth rates in organic production continue to lag behind the dynamic growth seen within the organic food market. In the public policy arena at the EU level, there are some opportunities for the organic sector to capitalise on the growing awareness and interest in tackling sustainability concerns in the agri-food sector amongst policymakers, but there are also challenges. These prospects and developments for organic in Europe are explored in this chapter

Quantifying the contribution of free-living nematodes to nitrogen mineralization

Soil fauna are estimated to contribute to approximately 30 % of nitrogen mineralization (Verhoef ∧ Brussaard, 1990). Soil nematodes are important contributors to this process through their key trophic positions as microbial grazers. Quantification of this contribution has mostly relied on theoretical food web analyses (Hunt et al., 1987) or laboratory incubations with simplified and artificially constructed ecosystems (Ferris et al., 1998). Incubations are often performed on homogenized soil, though soil biota is known to be responsive to physical disturbance. Furthermore, sterilization typically relies on methods disruptive of soil structure (e.g. autoclaving, freezing). The aim of this experiment was to quantify the contribution of nematodes to nitrogen mineralization during incubation. Intact cores with a representative pore structure and entire nematode populations instead of single species were used. Gamma irradiation was selected as a sterilization method to remove only soil fauna, leaving the microflora and soil structure largely intact (McNamara et al., 2003)

Organic in Europe: Recent Developments

In 2017, the European organic food and farming sector continued to excel both in terms of organic production and market growth. Data for 2017 (for full data see page 216) shows the European organic food market recording significant growth – increasing by more than ten percent to 37.3 billion. At the same time, the organic sector faces a number of challenges, notably that the growth rates in organic area, in spite of recent stronger growth, continues to lag behind the dynamic growth seen within the organic food market (Figure 68). A major milestone in 2018 was the publication of the new European Union rules on organic production and labelling of organic products in May, and in June 2018, the European Commission launched its proposal for the Common Agricultural Policy for the period 2021 to 2027

Exploring the similarity of medical imaging classification problems

Supervised learning is ubiquitous in medical image analysis. In this paper we consider the problem of meta-learning -- predicting which methods will perform well in an unseen classification problem, given previous experience with other classification problems. We investigate the first step of such an approach: how to quantify the similarity of different classification problems. We characterize datasets sampled from six classification problems by performance ranks of simple classifiers, and define the similarity by the inverse of Euclidean distance in this meta-feature space. We visualize the similarities in a 2D space, where meaningful clusters start to emerge, and show that the proposed representation can be used to classify datasets according to their origin with 89.3\% accuracy. These findings, together with the observations of recent trends in machine learning, suggest that meta-learning could be a valuable tool for the medical imaging community

Boosting organic seed and Plant breeding across Europe 2017‐2021

Boosting organic seed and Plant breeding across Europe 2017‐202

Boosting organic seed and Plant breeding across Europe 2017‐2021

Boosting organic seed and Plant breeding across Europe 2017‐202