Avila Bugge (crabfire3)

To investigate a deep learning approach that enables three-dimensional (3D) segmentation of an arbitrary structure of interest given a user provided two-dimensional (2D) contour for context. Such an approach could decrease delineation times and improve contouring consistency, particularly for anatomical structures for which no automatic segmentation tools exist. A series of deep learning segmentation models using a Recurrent Residual U-Net with attention gates was trained with a successively expanding training set. Contextual information was provided to the models, using a previously contoured slice as an input, in addition to the slice to be contoured. In total, 6 models were developed, and 19 different anatomical structures were used for training and testing. Each of the models was evaluated for all 19 structures, even if they were excluded from the training set, in order to assess the model's ability to segment unseen structures of interest. Each model's performance was evaluated using the Dice similartouring to facilitate semi-automatic segmentation of CT images for any given structure. Such an approach can enable faster de-novo contouring in clinical practice. Training a contextual deep learning model on a diverse set of structures increases the segmentation performance for the structures in the training set, but importantly enables the model to generalize and make predictions even for unseen structures that were not represented in the training set. This shows that user-provided context can be incorporated into deep learning contouring to facilitate semi-automatic segmentation of CT images for any given structure. Such an approach can enable faster de-novo contouring in clinical practice.Mutualisms are ubiquitous in nature and are thought to play important roles in the maintenance of biodiversity. For biodiversity to be maintained, however, species must coexist in the face of competitive exclusion. Chesson's coexistence theory provides a mechanistic framework for evaluating coexistence, yet mutualisms are conspicuously absent from coexistence theory and there are no comparable frameworks for evaluating how mutualisms affect the coexistence of competiting species. To address this conceptual gap, I develop theory predicting how multitrophic mutualisms mediate the coexistence of species competing for mutualistic commodities and other limiting resources using the niche and fitness difference concepts of coexistence theory. I demonstrate that failing to account for mutualisms can lead to erroneous conclusions. For example, species might appear to coexist on resources alone, when the simultaneous incorporation of mutualisms actually drives competitive exclusion, or competitive exclusion might occur under resource competition, when in fact, the incorporation of mutualisms generates coexistence. Existing coexistence theory cannot therefore be applied to mutualisms without explicitly considering the underlying biology of the interactions. By discussing how the metrics derived from coexistence theory can be quantified empirically, I show how this theory can be operationalized to evaluate the coexistence consequences of mutualism in natural communities.The cheerleader effect occurs when the same face is rated to be more attractive when it is seen in a group compared to when seen alone. We investigated whether this phenomenon also occurs for trustworthiness judgements, and examined how these effects are influenced by the characteristics of the individual being evaluated and those of the group they are seen in. Across three experiments, we reliably replicated the cheerleader effect. Most faces became more attractive in a group. Yet, the size of the cheerleader effect that each face experienced was not related to its own attractiveness, nor to the attractiveness of the group or the group's digitally averaged face. selleck We discuss the implications of our findings for the hierarchical encoding and contrast mechani