Crop segmentation is an important task in Precision Agriculture, where the use of aerial robots with an on-board camera has contributed to the development of new solution alternatives. We address the problem of fig plant segmentation in top-view RGB (Red-Green-Blue) images of a crop grown under open-field difficult circumstances of complex lighting conditions and non-ideal crop maintenance practices defined by local farmers. We present a Convolutional Neural Network (CNN) with an encoder-decoder architecture that classifies each pixel as crop or non-crop using only raw colour images as input. Our approach achieves a mean accuracy of 93.85% despite the complexity of the background and a highly variable visual appearance of the leaves. We make available our CNN code to the research community, as well as the aerial image data set and a hand-made ground truth segmentation with pixel precision to facilitate the comparison among different algorithms.

Rotavirus genome replication and assembly take place in cytoplasmic electron dense inclusions termed viroplasms (VPs). Previous conventional optical microscopy studies observing the intracellular distribution of rotavirus proteins and their organization in VPs have lacked molecular-scale spatial resolution, due to inherent spatial resolution constraints. In this work we employed super-resolution microscopy to reveal the nanometric-scale organization of VPs formed during rotavirus infection, and quantitatively describe the structural organization of seven viral proteins within and around the VPs. The observed viral components are spatially organized as five concentric layers, in which NSP5 localizes at the center of the VPs, surrounded by a layer of NSP2 and NSP4 proteins, followed by an intermediate zone comprised of the VP1, VP2, VP6. In the outermost zone, we observed a ring of VP4 and finally a layer of VP7. These findings show that rotavirus VPs are highly organized organelles.

Tracing tubular structures from biomedical images is important for a wide range of applications. Particularly, the spermatozoon is an essential cell whose flagella has a tubular form. Its main function is to fertilize the egg and the flagellum is fundamental to achieve this task which depends importantly on the dynamics of intracellular calcium ([Ca2+]i). Measuring [Ca2+]i along the flagellum in 3D is not a simple matter since it requires: (i) sophisticated fluorescence imaging techniques dealing with low intensity and signal to noise ratio (SNR) and (ii) tracing the flagellum’s centerline. Most of the algorithms proposed to trace tubular structures have been developed for multi-branch structures not being adequate for single tubular structures with low SNR. Taking into account the prior knowledge that the flagellum is constituted by a single tubular structure, we propose an automatic method to trace and track multiple single tubular structures from 3D+t images. First, an algorithm based on one-class classification allows enhancement of the flagellum. This enhanced 3D image permits guiding an iterative centerline algorithm towards the flagellum’s centerline. Each sperm is assigned an ID to keep track of it in 3D+t. Our algorithm was quantitatively evaluated using as ground truth 564 semi-manual traces (six 3D+t image stacks) comparing them to those obtained from state-of-the-art tubular structure centerline extraction algorithms. The qualitative and quantitative results show that our algorithm is extracting similar traces as compared with ground truth and it is more robust and accurate to trace the flagellum’s centerline than multi-branch algorithms.

This paper presents a new version of the camera-space-manipulation method (CSM). The set of nonlinear view parameters of the classic CSM is replaced with a linear model. Simulations and experiments show a similar precision error for the two methods. However, the new approach is simpler to implement and is faster.

Viruses employ a variety of strategies to hijack cellular activities through the orchestrated recruitment of macromolecules to specific virus-induced cellular micro-environments. Adenoviruses (Ad) and other DNA viruses induce extensive reorganization of the cell nucleus and formation of nuclear Replication Compartments (RCs), where the viral genome is replicated and expressed. In this work an automatic algorithm designed for detection and segmentation of RCs using ellipses is presented. Unlike algorithms available in the literature, this approach is deterministic, automatic, and can adjust multiple RCs using ellipses. The proposed algorithm is non iterative, computationally efficient and is invariant to affine transformations. The method was validated over both synthetic images and more than 400 real images of Ad-infected cells at various timepoints of the viral replication cycle obtaining relevant information about the biogenesis of adenoviral RCs. As proof of concept the algorithm was then used to quantitatively compare RCs in cells infected with the adenovirus wild type or an adenovirus mutant that is null for expression of a viral protein that is known to affect activities associated with RCs that result in deficient viral progeny production.