Atlas Toolkit: Fast registration of 3D morphological datasets in the absence of landmarks

Image registration is a gateway technology for Developmental Systems Biology, enabling computational analysis of related datasets within a shared coordinate system. Many registration tools rely on landmarks to ensure that datasets are correctly aligned; yet suitable landmarks are not present in many datasets. Atlas Toolkit is a Fiji/ImageJ plugin collection offering elastic group-wise registration of 3D morphological datasets, guided by segmentation of the interesting morphology. We demonstrate the method by combinatorial mapping of cell signalling events in the developing eyes of chick embryos, and use the integrated datasets to predictively enumerate Gene Regulatory Network states

Roto-Translation Covariant Convolutional Networks for Medical Image Analysis

We propose a framework for rotation and translation covariant deep learning using $SE(2)$ group convolutions. The group product of the special Euclidean motion group $SE(2)$ describes how a concatenation of two roto-translations results in a net roto-translation. We encode this geometric structure into convolutional neural networks (CNNs) via $SE(2)$ group convolutional layers, which fit into the standard 2D CNN framework, and which allow to generically deal with rotated input samples without the need for data augmentation. We introduce three layers: a lifting layer which lifts a 2D (vector valued) image to an $SE(2)$-image, i.e., 3D (vector valued) data whose domain is $SE(2)$; a group convolution layer from and to an $SE(2)$-image; and a projection layer from an $SE(2)$-image to a 2D image. The lifting and group convolution layers are $SE(2)$ covariant (the output roto-translates with the input). The final projection layer, a maximum intensity projection over rotations, makes the full CNN rotation invariant. We show with three different problems in histopathology, retinal imaging, and electron microscopy that with the proposed group CNNs, state-of-the-art performance can be achieved, without the need for data augmentation by rotation and with increased performance compared to standard CNNs that do rely on augmentation.Comment: 8 pages, 2 figures, 1 table, accepted at MICCAI 201

CubeNet: Equivariance to 3D Rotation and Translation

3D Convolutional Neural Networks are sensitive to transformations applied to their input. This is a problem because a voxelized version of a 3D object, and its rotated clone, will look unrelated to each other after passing through to the last layer of a network. Instead, an idealized model would preserve a meaningful representation of the voxelized object, while explaining the pose-difference between the two inputs. An equivariant representation vector has two components: the invariant identity part, and a discernable encoding of the transformation. Models that can't explain pose-differences risk "diluting" the representation, in pursuit of optimizing a classification or regression loss function. We introduce a Group Convolutional Neural Network with linear equivariance to translations and right angle rotations in three dimensions. We call this network CubeNet, reflecting its cube-like symmetry. By construction, this network helps preserve a 3D shape's global and local signature, as it is transformed through successive layers. We apply this network to a variety of 3D inference problems, achieving state-of-the-art on the ModelNet10 classification challenge, and comparable performance on the ISBI 2012 Connectome Segmentation Benchmark. To the best of our knowledge, this is the first 3D rotation equivariant CNN for voxel representations.Comment: Preprin

Elastic Image Registration of 2-D Gels for Differential and Repeatability Studies

One of the main applications of electrophoretic 2-D gels is the analysis of differential responses between different conditions. For this reason, specific spots are present in one of the images, but not in the other. In some other occasions, the same experiment is repeated between 2 and 12 times in order to increase statistical significance. In both situations, one of the major difficulties of these analysis is that 2-D gels are affected by spatial distortions due to run-time differences and dye-front deformations, resulting in images that are significantly dissimilar not only because of their content, but also because of their geometry. In this technical brief, we show how to use free, state-of-the-art image registration and fusion algorithms developed by us for solving the problem of comparing differential expression profiles, or computing an "average" image from a series of virtually identical gels

Fast and Accurate Lung Tumor Spotting and Segmentation for Boundary Delineation on CT Slices In A Coarse-To-Fine Framework

Label noise and class imbalance are two of the critical challenges when training image-based deep neural networks, especially in the biomedical image processing domain. Our work focuses on how to address the two challenges effectively and accurately in the task of lesion segmentation from biomedical/medical images. To address the pixel-level label noise problem, we propose an advanced transfer training and learning approach with a detailed DICOM pre-processing method. To address the tumor/non-tumor class imbalance problem, we exploit a self-adaptive fully convolutional neural network with an automated weight distribution mechanism to spot the Radiomics lung tumor regions accurately. Furthermore, an improved conditional random field method is employed to obtain sophisticated lung tumor contour delineation and segmentation. Finally, our approach has been evaluated using several well-known evaluation metrics on the Lung Tumor segmentation dataset used in the 2018 IEEE VIP-CUP Challenge. Experimental results show that our weakly supervised learning algorithm outperforms other deep models and state-of-the-art approache

TrakEM2 Software for Neural Circuit Reconstruction

A key challenge in neuroscience is the expeditious reconstruction of neuronal circuits. For model systems such as Drosophila and C. elegans, the limiting step is no longer the acquisition of imagery but the extraction of the circuit from images. For this purpose, we designed a software application, TrakEM2, that addresses the systematic reconstruction of neuronal circuits from large electron microscopical and optical image volumes. We address the challenges of image volume composition from individual, deformed images; of the reconstruction of neuronal arbors and annotation of synapses with fast manual and semi-automatic methods; and the management of large collections of both images and annotations. The output is a neural circuit of 3d arbors and synapses, encoded in NeuroML and other formats, ready for analysis

Marker-free image registration of electron tomography tilt-series

<p>Abstract</p> <p>Background</p> <p>Tilt series are commonly used in electron tomography as a means of collecting three-dimensional information from two-dimensional projections. A common problem encountered is the projection alignment prior to 3D reconstruction. Current alignment techniques usually employ gold particles or image derived markers to correctly align the images. When these markers are not present, correlation between adjacent views is used to align them. However, sequential pairwise correlation is prone to bias and the resulting alignment is not always optimal.</p> <p>Results</p> <p>In this paper we introduce an algorithm to find regions of the tilt series which can be tracked within a subseries of the tilt series. These regions act as landmarks allowing the determination of the alignment parameters. We show our results with synthetic data as well as experimental cryo electron tomography.</p> <p>Conclusion</p> <p>Our algorithm is able to correctly align a single-tilt tomographic series without the help of fiducial markers thanks to the detection of thousands of small image patches that can be tracked over a short number of images in the series.</p

Fluidal pyroclasts reveal the intensity of peralkaline rhyolite pumice cone eruptions

This work is a contribution to the Natural Environment Research Council (NERC) funded RiftVolc project (NE/L013932/1, Rift volcanism: past, present and future) through which several of the authors are supported. In addition, Clarke was funded by a NERC doctoral training partnership grant (NE/L002558/1).Peralkaline rhyolites are medium to low viscosity, volatile-rich magmas typically associated with rift zones and extensional settings. The dynamics of peralkaline rhyolite eruptions remain elusive with no direct observations recorded, significantly hindering the assessment of hazard and risk. Here we describe uniquely-preserved, fluidal-shaped pyroclasts found within pumice cone deposits at Aluto, a peralkaline rhyolite caldera in the Main Ethiopian Rift. We use a combination of field-observations, geochemistry, X-ray computed microtomography (XCT) and thermal-modelling to investigate how these pyroclasts are formed. We find that they deform during flight and, depending on size, quench prior to deposition or continue to inflate then quench in-situ. These findings reveal important characteristics of the eruptions that gave rise to them: that despite the relatively low viscosity of these magmas, and similarities to basaltic scoria-cone deposits, moderate to intense, unstable, eruption columns are developed; meaning that such eruptions can generate extensive tephra-fall and pyroclastic density currents.Publisher PDFPeer reviewe

Regulation of Cancer Aggressive Features in Melanoma Cells by MicroRNAs

MicroRNAs (miRNAs) are small non-coding RNAs with regulatory roles, which are involved in a broad spectrum of physiological and pathological processes, including cancer. A common strategy for identification of miRNAs involved in cell transformation is to compare malignant cells to normal cells. Here we focus on identification of miRNAs that regulate the aggressive phenotype of melanoma cells. To avoid differences due to genetic background, a comparative high-throughput miRNA profiling was performed on two isogenic human melanoma cell lines that display major differences in their net proliferation, invasion and tube formation activities. This screening revealed two major cohorts of differentially expressed miRNAs. We speculated that miRNAs up-regulated in the more-aggressive cell line contribute oncogenic features, while the down-regulated miRNAs are tumor suppressive. This assumption was further tested experimentally on five candidate tumor suppressive miRNAs (miR-31, -34a, -184, -185 and -204) and on one candidate oncogenic miRNA (miR-17-5p), all of which have never been reported before in cutaneous melanoma. Remarkably, all candidate Suppressive-miRNAs inhibited net proliferation, invasion or tube formation, while miR-17-5p enhanced cell proliferation. miR-34a and miR-185 were further shown to inhibit the growth of melanoma xenografts when implanted in SCID-NOD mice. Finally, all six candidate miRNAs were detected in 15 different metastatic melanoma specimens, attesting for the physiological relevance of our findings. Collectively, these findings may prove instrumental for understanding mechanisms of disease and for development of novel therapeutic and staging technologies for melanoma