Expert-centered Evaluation of Deep Learning Algorithms for Brain Tumor Segmentation

Purpose To present results from a literature survey on practices in deep learning segmentation algorithm evaluation and perform a study on expert quality perception of brain tumor segmentation. Materials and Methods A total of 180 articles reporting on brain tumor segmentation algorithms were surveyed for the reported quality evaluation. Additionally, ratings of segmentation quality on a four-point scale were collected from medical professionals for 60 brain tumor segmentation cases. Results Of the surveyed articles, Dice score, sensitivity, and Hausdorff distance were the most popular metrics to report segmentation performance. Notably, only 2.8% of the articles included clinical experts\u27 evaluation of segmentation quality. The experimental results revealed a low interrater agreement (Krippendorff α, 0.34) in experts\u27 segmentation quality perception. Furthermore, the correlations between the ratings and commonly used quantitative quality metrics were low (Kendall tau between Dice score and mean rating, 0.23; Kendall tau between Hausdorff distance and mean rating, 0.51), with large variability among the experts. Conclusion The results demonstrate that quality ratings are prone to variability due to the ambiguity of tumor boundaries and individual perceptual differences, and existing metrics do not capture the clinical perception of segmentation quality

Model averaging in ecology: a review of Bayesian, information-theoretic and tactical approaches for predictive inference

In ecology, the true causal structure for a given problem is often not known, and several plausible models and thus model predictions exist. It has been claimed that using weighted averages of these models can reduce prediction error, as well as better reflect model selection uncertainty. These claims, however, are often demonstrated by isolated examples. Analysts must better understand under which conditions model averaging can improve predictions and their uncertainty estimates. Moreover, a large range of different model averaging methods exists, raising the question of how they differ in their behaviour and performance. Here, we review the mathematical foundations of model averaging along with the diversity of approaches available. We explain that the error in model‐averaged predictions depends on each model's predictive bias and variance, as well as the covariance in predictions between models, and uncertainty about model weights. We show that model averaging is particularly useful if the predictive error of contributing model predictions is dominated by variance, and if the covariance between models is low. For noisy data, which predominate in ecology, these conditions will often be met. Many different methods to derive averaging weights exist, from Bayesian over information‐theoretical to cross‐validation optimized and resampling approaches. A general recommendation is difficult, because the performance of methods is often context dependent. Importantly, estimating weights creates some additional uncertainty. As a result, estimated model weights may not always outperform arbitrary fixed weights, such as equal weights for all models. When averaging a set of models with many inadequate models, however, estimating model weights will typically be superior to equal weights. We also investigate the quality of the confidence intervals calculated for model‐averaged predictions, showing that they differ greatly in behaviour and seldom manage to achieve nominal coverage. Our overall recommendations stress the importance of non‐parametric methods such as cross‐validation for a reliable uncertainty quantification of model‐averaged predictions

25th annual computational neuroscience meeting: CNS-2016

The same neuron may play different functional roles in the neural circuits to which it belongs. For example, neurons in the Tritonia pedal ganglia may participate in variable phases of the swim motor rhythms [1]. While such neuronal functional variability is likely to play a major role the delivery of the functionality of neural systems, it is difficult to study it in most nervous systems. We work on the pyloric rhythm network of the crustacean stomatogastric ganglion (STG) [2]. Typically network models of the STG treat neurons of the same functional type as a single model neuron (e.g. PD neurons), assuming the same conductance parameters for these neurons and implying their synchronous firing [3, 4]. However, simultaneous recording of PD neurons shows differences between the timings of spikes of these neurons. This may indicate functional variability of these neurons. Here we modelled separately the two PD neurons of the STG in a multi-neuron model of the pyloric network. Our neuron models comply with known correlations between conductance parameters of ionic currents. Our results reproduce the experimental finding of increasing spike time distance between spikes originating from the two model PD neurons during their synchronised burst phase. The PD neuron with the larger calcium conductance generates its spikes before the other PD neuron. Larger potassium conductance values in the follower neuron imply longer delays between spikes, see Fig. 17.Neuromodulators change the conductance parameters of neurons and maintain the ratios of these parameters [5]. Our results show that such changes may shift the individual contribution of two PD neurons to the PD-phase of the pyloric rhythm altering their functionality within this rhythm. Our work paves the way towards an accessible experimental and computational framework for the analysis of the mechanisms and impact of functional variability of neurons within the neural circuits to which they belong

The global distribution of plant species richness in a human-dominated world

Plant species richness is essential for ecosystem functioning, resilience and ecosystem services, yet is globally threatened by anthropogenic land use, including management and modification of the natural environment. At broad scales, land-use effects are often simply modelled by habitat loss, assuming that transformed land becomes completely inhospitable for naturally occurring species. Further, estimates of species losses are flawed by the common assumption of a universal slope of the species–area curve, typically ranging from 0.15 to 0.35. My PhD dissertation consists of a global species–area analysis, a meta-analysis about land-use effects on plant species richness and an approach to integrate these land-use effects in a countryside species–area model. Overall, my PhD research contributes to a deeper understanding of species–area relationships and how patterns of species richness at macroscales are driven by land use. It proposes a model to predict species richness patterns of vascular plants that overcomes limitations of previous models.</p

The global distribution of plant species richness in a human-dominated world

Plant species richness is essential for ecosystem functioning, resilience and ecosystem services, yet is globally threatened by anthropogenic land use, including management and modification of the natural environment. At broad scales, land-use effects are often simply modelled by habitat loss, assuming that transformed land becomes completely inhospitable for naturally occurring species. Further, estimates of species losses are flawed by the common assumption of a universal slope of the species–area curve, typically ranging from 0.15 to 0.35. My PhD dissertation consists of a global species–area analysis, a meta-analysis about land-use effects on plant species richness and an approach to integrate these land-use effects in a countryside species–area model. Overall, my PhD research contributes to a deeper understanding of species–area relationships and how patterns of species richness at macroscales are driven by land use. It proposes a model to predict species richness patterns of vascular plants that overcomes limitations of previous models

The global distribution of plant species richness in a human-dominated world

Plant species richness is essential for ecosystem functioning, resilience and ecosystem services, yet is globally threatened by anthropogenic land use, including management and modification of the natural environment. At broad scales, land-use effects are often simply modelled by habitat loss, assuming that transformed land becomes completely inhospitable for naturally occurring species. Further, estimates of species losses are flawed by the common assumption of a universal slope of the species–area curve, typically ranging from 0.15 to 0.35. My PhD dissertation consists of a global species–area analysis, a meta-analysis about land-use effects on plant species richness and an approach to integrate these land-use effects in a countryside species–area model. Overall, my PhD research contributes to a deeper understanding of species–area relationships and how patterns of species richness at macroscales are driven by land use. It proposes a model to predict species richness patterns of vascular plants that overcomes limitations of previous models

Why do forest products become less available? A pan-tropical comparison of drivers of forest-resource degradation

Forest products provide an important source of income and wellbeing for rural smallholder communities across the tropics. Although tropical forest products frequently become over-exploited, only few studies explicitly address the dynamics of degradation in response to socio-economic drivers. Our study addresses this gap by analyzing the factors driving changes in tropical forest products in the perception of rural smallholder communities. Using the poverty and environment network global dataset, we studied recently perceived trends of forest product availability considering firewood, charcoal, timber, food, medicine, forage and other forest products. We looked at a pan-tropical sample of 233 villages with forest access. Our results show that 90% of the villages experienced declining availability of forest resources over the last five years according to the informants. Timber and fuelwood together with forest foods were featured as the most strongly affected, though with marked differences across continents. In contrast, availability of at least one main forest product was perceived to increase in only 39% of the villages. Furthermore, the growing local use of forest resources is seen as the main culprit for the decline. In villages with both growing forest resource use and immigration—vividly illustrating demographic pressures—the strongest forest resources degradation was observed. Conversely, villages with little or no population growth and a decreased use of forest resources were most likely to see significant forest-resource increases. Further, villages are less likely to perceive resource declines when local communities own a significant share of forest area. Our results thus suggest that perceived resource declines have only exceptionally triggered adaptations in local resource-use and management patterns that would effectively deal with scarcity. Hence, at the margin this supports neo-Malthusian over neo-Boserupian explanations of local resource-use dynamics

Synthesis in land change science: methodological patterns, challenges, and guidelines

Global and regional economic and environmental changes are increasingly influencing local land-use, livelihoods, and ecosystems. At the same time, cumulative local land changes are driving global and regional changes in biodiversity and the environment. To understand the causes and consequences of these changes, land change science (LCS) draws on a wide array synthetic and meta-study techniques to generate global and regional knowledge from local case studies of land change. Here, we review the characteristics and applications of synthesis methods in LCS and assess the current state of synthetic research based on a meta-analysis of synthesis studies from 1995 to 2012. Publication of synthesis research is accelerating, with a clear trend toward increasingly sophisticated and quantitative methods, including meta-analysis. Detailed trends in synthesis objectives, methods, and land change phenomena and world regions most commonly studied are presented. Significant challenges to successful synthesis research in LCS are also identified, including issues of interpretability and comparability across case-studies and the limits of and biases in the geographic coverage of case studies. Nevertheless, synthesis methods based on local case studies will remain essential for generating systematic global and regional understanding of local land change for the foreseeable future, and multiple opportunities exist to accelerate and enhance the reliability of synthetic LCS research in the future. Demand for global and regional knowledge generation will continue to grow to support adaptation and mitigation policies consistent with both the local realities and regional and global environmental and economic contexts of land change. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1007/s10113-014-0626-8) contains supplementary material, which is available to authorized users

Central synaptopathy is the most conserved feature of motor circuit pathology across spinal muscular atrophy mouse models

Spinal muscular atrophy (SMA) is a neurodegenerative disease caused by reduced survival motor neuron (SMN) protein. Recently, SMN dysfunction has been linked to individual aspects of motor circuit pathology in a severe SMA mouse model. To determine whether these disease mechanisms are conserved, we directly compared the motor circuit pathology of three SMA mouse models. The severe SMND7 model exhibits vast motor circuit defects, including degeneration of motor neurons, spinal excitatory synapses, and neuromuscular junctions (NMJs). In contrast, the Taiwanese model shows very mild motor neuron pathology, but early central synaptic loss. In the intermediate Smn(2B)(/-) model, strong pathology of central excitatory synapses and NMJs precedes the late onset of p53-dependent motor neuron death. These pathological events correlate with SMN-dependent splicing dysregulation of specific mRNAs. Our study provides a knowledge base for properly tailoring future studies and identifies central excitatory synaptopathy as a key feature of motor circuit pathology in SMA