Generalizable Deepfake Detection with Phase-Based Motion Analysis.

We propose PhaseForensics, a DeepFake (DF) video detection method that uses a phase-based motion representation of facial temporal dynamics. Existing methods that rely on temporal information across video frames for DF detection have many advantages over the methods that only utilize the perframe features. However, these temporal DF detection methods still show limited cross-dataset generalization and robustness to common distortions due to factors such as error-prone motion estimation, inaccurate landmark tracking, or the susceptibility of the pixel intensity-based features to adversarial distortions and the cross-dataset domain shifts.

Artificial Intelligence for Automatic Analysis of Shunt Treatment in Presurgery and Postsurgery Computed Tomography Brain Scans of Patients With Idiopathic Normal Pressure Hydrocephalus.

Background And Objectives: Ventriculo-peritoneal shunt procedures can improve idiopathic normal pressure hydrocephalus (iNPH) symptoms. However, there are no automated methods that quantify the presurgery and postsurgery changes in the ventricular volume for computed tomography scans. Hence, the main goal of this research was to quantify longitudinal changes in the ventricular volume and its correlation with clinical improvement in iNPH symptoms.

A robust approach to 3D neuron shape representation for quantification and classification.

We consider the problem of finding an accurate representation of neuron shapes, extracting sub-cellular features, and classifying neurons based on neuron shapes. In neuroscience research, the skeleton representation is often used as a compact and abstract representation of neuron shapes. However, existing methods are limited to getting and analyzing "curve" skeletons which can only be applied for tubular shapes.

ReeBundle: A Method for Topological Modeling of White Matter Pathways Using Diffusion MRI.

Tractography can generate millions of complex curvilinear fibers (streamlines) in 3D that exhibit the geometry of white matter pathways in the brain. Common approaches to analyzing white matter connectivity are based on adjacency matrices that quantify connection strength but do not account for any topological information. A critical element in neurological and developmental disorders is the topological deterioration and irregularities in streamlines.

3D Neuron Morphology Analysis.

We consider the problem of finding an accurate representation of neuron shapes, extracting sub-cellular features, and classifying neurons based on neuron shapes. In neuroscience research, the skeleton representation is often used as a compact and abstract representation of neuron shapes. However, existing methods are limited to getting and analyzing"curve"skeletons which can only be applied for tubular shapes.

Segmentation, tracking, and sub-cellular feature extraction in 3D time-lapse images.

This paper presents a method for time-lapse 3D cell analysis. Specifically, we consider the problem of accurately localizing and quantitatively analyzing sub-cellular features, and for tracking individual cells from time-lapse 3D confocal cell image stacks. The heterogeneity of cells and the volume of multi-dimensional images presents a major challenge for fully automated analysis of morphogenesis and development of cells.

Erratum: Automatic classification and neurotransmitter prediction of synapses in electron microscopy - CORRIGENDUM.

[This corrects the article DOI: 10.1017/S2633903X2200006X.].

Automatic classification and neurotransmitter prediction of synapses in electron microscopy.

This paper presents a deep-learning-based workflow to detect synapses and predict their neurotransmitter type in the primitive chordate () electron microscopic (EM) images. Identifying synapses from EM images to build a full map of connections between neurons is a labor-intensive process and requires significant domain expertise. Automation of synapse classification would hasten the generation and analysis of connectomes.

Automated Segmentation and Connectivity Analysis for Normal Pressure Hydrocephalus.

. We propose an automated method of predicting Normal Pressure Hydrocephalus (NPH) from CT scans. A deep convolutional network segments regions of interest from the scans.

A Computational Geometry Approach for Modeling Neuronal Fiber Pathways.

We propose a novel and efficient algorithm to model high-level topological structures of neuronal fibers. Tractography constructs complex neuronal fibers in three dimensions that exhibit the geometry of white matter pathways in the brain. However, most tractography analysis methods are time consuming and intractable.

Corrigendum: Improving Patch-Based Convolutional Neural Networks for MRI Brain Tumor Segmentation by Leveraging Location Information.

[This corrects the article DOI: 10.3389/fnins.2019.

Improving Patch-Based Convolutional Neural Networks for MRI Brain Tumor Segmentation by Leveraging Location Information.

The manual brain tumor annotation process is time consuming and resource consuming, therefore, an automated and accurate brain tumor segmentation tool is greatly in demand. In this paper, we introduce a novel method to integrate location information with the state-of-the-art patch-based neural networks for brain tumor segmentation. This is motivated by the observation that lesions are not uniformly distributed across different brain parcellation regions and that a locality-sensitive segmentation is likely to obtain better segmentation accuracy.

Predicting Clinical Outcome of Stroke Patients with Tractographic Feature.

The volume of stroke lesion is the gold standard for predicting the clinical outcome of stroke patients. However, the presence of stroke lesion may cause neural disruptions to other brain regions, and these potentially damaged regions may affect the clinical outcome of stroke patients. In this paper, we introduce the tractographic feature to capture these potentially damaged regions and predict the modified Rankin Scale (mRS), which is a widely used outcome measure in stroke clinical trials.

Superpixel Embedding Network.

Superpixel segmentation is a fundamental computer vision technique that finds application in a multitude of high level computer vision tasks. Most state-of-the-art superpixel segmentation methods are unsupervised in nature and thus cannot fully utilize frequently occurring texture patterns or incorporate multiscale context. In this paper, we show that superpixel segmentation can be improved by leveraging the superior modeling power of deep convolutional autoencoders in a fully unsupervised manner.

Gamma-irradiation-induced micro-structural variations in flame-retardant polyurethane foam using synchrotron X-ray micro-tomography.

Flame-retardant polyurethane foams are potential packing materials for the transport casks of highly active nuclear materials for shock absorption and insulation purposes. Exposure of high doses of gamma radiation causes cross-linking and chain sectioning of macromolecules in this polymer foam, which leads to reorganization of their cellular microstructure and thereby variations in physico-mechanical properties. In this study, in-house-developed flame-retardant rigid polyurethane foam samples were exposed to gamma irradiation doses in the 0-20 kGy range and synchrotron radiation X-ray micro-computed tomography (SR-µCT) imaging was employed for the analysis of radiation-induced morphological variations in their cellular microstructure.

Hybrid LSTM and Encoder-Decoder Architecture for Detection of Image Forgeries.

With advanced image journaling tools, one can easily alter the semantic meaning of an image by exploiting certain manipulation techniques such as copy clone, object splicing, and removal, which mislead the viewers. In contrast, the identification of these manipulations becomes a very challenging task as manipulated regions are not visually apparent. This paper proposes a high-confidence manipulation localization architecture that utilizes resampling features, long short-term memory (LSTM) cells, and an encoder-decoder network to segment out manipulated regions from non-manipulated ones.

Healthcare Event and Activity Logging.

The health of patients in the intensive care unit (ICU) can change frequently and inexplicably. Crucial events and activities responsible for these changes often go unnoticed. This paper introduces healthcare event and action logging (HEAL) which automatically and unobtrusively monitors and reports on events and activities that occur in a medical ICU room.

Leiomyosarcoma of maxilla: a case report with review of literature.

One challenging feature of head and neck pathology is that a dizzying array of spindle cell lesions occurs here which ranges all the way from reactive, very aggressive forms to malignant lesions. Leiomyosarcoma is one such malignant tumour of mesenchymal origin exhibiting smooth muscle differentiation; presenting generally nonspecific signs and symptoms. Here we present a case of leiomyosarcoma in a 21 year old female patient associated with single reddish pink swelling present in the posterior right maxillary tuberosity region with moderate facial asymmetry.

CellECT: cell evolution capturing tool.

Background: Robust methods for the segmentation and analysis of cells in 3D time sequences (3D+t) are critical for quantitative cell biology. While many automated methods for segmentation perform very well, few generalize reliably to diverse datasets. Such automated methods could significantly benefit from at least minimal user guidance.

Characterizing spatial distributions of astrocytes in the mammalian retina.

Motivation: In addition to being involved in retinal vascular growth, astrocytes play an important role in diseases and injuries, such as glaucomatous neuro-degeneration and retinal detachment. Studying astrocytes, their morphological cell characteristics and their spatial relationships to the surrounding vasculature in the retina may elucidate their role in these conditions.

Results: Our results show that in normal healthy retinas, the distribution of observed astrocyte cells does not follow a uniform distribution.

A linear program formulation for the segmentation of Ciona membrane volumes.

We address the problem of cell segmentation in confocal microscopy membrane volumes of the ascidian Ciona used in the study of morphogenesis. The primary challenges are non-uniform and patchy membrane staining and faint spurious boundaries from other organelles (e.g.

Statistical analysis of dendritic spine distributions in rat hippocampal cultures.

Background: Dendritic spines serve as key computational structures in brain plasticity. Much remains to be learned about their spatial and temporal distribution among neurons. Our aim in this study was to perform exploratory analyses based on the population distributions of dendritic spines with regard to their morphological characteristics and period of growth in dissociated hippocampal neurons.

Robust segmentation based tracing using an adaptive wrapper for inducing priors.

Segmentation based tracing algorithms detect the extent and borders of an object in a given frame IZ by propagating results from frames I1 ≤ z < Z. Although application specific tracers have been forthcoming, techniques that automatically adapt across applications have been less explored. We approach this problem by learning a prior model on topological dynamics that encourages segmentation transitions across frames that are most likely for a given application.

Scalable tracing of electron micrographs by fusing top down and bottom up cues using hypergraph diffusion.

A novel framework for robust 3D tracing in electron micrographs is presented. The proposed framework is built using ideas from hypergraph diffusion, and achieves two main objectives. Firstly, the approach scales to trace hundreds of targets without noticeable increase in runtime complexity.

Biological imaging software tools.

Few technologies are more widespread in modern biological laboratories than imaging. Recent advances in optical technologies and instrumentation are providing hitherto unimagined capabilities. Almost all these advances have required the development of software to enable the acquisition, management, analysis and visualization of the imaging data.