BACKGROUND AND PURPOSE:The role of DCE-MR imaging in the study of bone marrow perfusion is only partially developed, though potential applications for routine use in the clinical setting are beginning to be described. We hypothesize that DCE-MR imaging can be used to discriminate between hypervascular and hypovascular metastases based on measured perfusion variables.

Patients with pathologic processes of the breast commonly present in the Emergency Department (ED). Familiarity with the imaging and management of the most common entities is essential for the radiologist. Additionally, it is important to understand the limitations of ED imaging and management in the acute setting and to recognize when referrals to a specialty breast center are necessary. The goal of this article is to review the clinical presentations, pathophysiology, imaging, and management of emergency breast cases and common breast pathology seen in the ED.

BackgroundAccurate and automated phenotyping of leaf images is necessary for high throughput studies of leaf form like genome-wide association analysis and other forms of quantitative trait locus mapping. Dissected leaves (also referred to as compound) that are subdivided into individual units are an attractive system to study diversification of form. However, there are only few software tools for their automated analysis. Thus, high-throughput image processing algorithms are needed that can partition these leaves in their phenotypically relevant units and calculate morphological features based on these units.ResultsWe have developed MowJoe, an image processing algorithm that dissects a dissected leaf into leaflets, petiolule, rachis and petioles. It employs image skeletonization to convert leaves into graphs, and thereafter applies algorithms operating on graph structures. This partitioning of a leaf allows the derivation of morphological features such as leaf size, or eccentricity of leaflets. Furthermore, MowJoe automatically places landmarks onto the terminal leaflet that can be used for further leaf shape analysis. It generates specific output files that can directly be imported into downstream shape analysis tools. We applied the algorithm to two accessions of Cardamine hirsuta and show that our features are able to robustly discriminate between these accessions.ConclusionMowJoe is a tool for the semi-automated, quantitative high throughput shape analysis of dissected leaf images. It provides the statistical power for the detection of the genetic basis of quantitative morphological variations.Electronic supplementary materialThe online version of this article (10.1186/s13007-018-0290-y) contains supplementary material, which is available to authorized users.

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