Tammy Riklin Raviv

Symmetry-based mitosis detection in time-lapse microscopy

Providing a general framework for mitosis detection is challenging. The variability of the visual traits and temporal features which classify the event of cell division is huge due to the numerous cell types, perturbations, imaging techniques and protocols used in microscopy imaging analysis studies. The commonly used machine learning techniques are based on the extraction of comprehensive sets of discriminative features from labeled examples and therefore do not apply to general cases as they are restricted to trained datasets. We present a robust mitotic event detection algorithm that accommodates the difficulty of the different cell appearances and dynamics. Addressing symmetrical cell divisions, we consider the anaphase stage, immediately after the DNA material divides, at which the two daughter cells are approximately identical. Having detected pairs of candidate daughter cells, based on their association to potential mother cells, we look for the respective symmetry axes. Mitotic event is detected based on the calculated measure of symmetry of each candidate pair of cells. Promising mitosis detection results for four different time-lapse microscopy datasets were obtained.

Accelerated Magnetic Resonance Imaging by Adversarial Neural Network

A main challenge in Magnetic Resonance Imaging (MRI) for clinical applications is speeding up scan time. Beyond the improvement of patient experience and the reduction of operational costs, faster scans are essential for time-sensitive imaging, where target movement is unavoidable, yet must be significantly lessened, e.g., fetal MRI, cardiac cine, and lungs imaging. Moreover, short scan time can enhance temporal resolution in dynamic scans, such as functional MRI or dynamic contrast enhanced MRI. Current imaging methods facilitate MRI acquisition at the price of lower spatial resolution and costly hardware solutions.

Co-segmentation of multiple images into multiple regions: Application to mouse brain MRI

Challenging biomedical segmentation problems can be addressed by combining top-down information based on the known anatomy along with bottom-up models of the image data. Anatomical priors can be provided by probabilistic atlases. Nevertheless, in many cases the available atlases are inadequate. We present a novel method for the co-segmentation of multiple images into multiple regions, where only a very few annotated examples exist. The underlying, unknown anatomy is learned throughout an interleaved process, in which the segmentation of a region is supported both by the segmentation of the neighboring regions which share common boundaries and by the segmentation of corresponding regions in the other jointly segmented images. The method is applied to a mouse brain MRI dataset for the segmentation of five anatomical structures. Experimental results demonstrate the segmentation accuracy with respect to the data complexity.

De-noising of Contrast-Enhanced MRI Sequences by an Ensemble of Expert Deep Neural Networks

Dynamic contrast-enhanced MRI (DCE-MRI) is an imaging protocol where MRI scans are acquired repetitively throughout the injection of a contrast agent. The analysis of dynamic scans is widely used for the detection and quantification of blood brain barrier (BBB) permeability. Extraction of the pharmacokinetic (PK) parameters from the DCE-MRI washout curves allows quantitative assessment of the BBB functionality. Nevertheless, curve fitting required for the analysis of DCE-MRI data is error-prone as the dynamic scans are subject to non-white, spatially-dependent and anisotropic noise that does not fit standard noise models. The two existing approaches i.e. curve smoothing and image de-noising can either produce smooth curves but cannot guaranty fidelity to the PK model or cannot accommodate the high variability in noise statistics in time and space.

Multinomial Level-Set Framework for Multi-region Image Segmentation

We present a simple and elegant level-set framework for multi-region image segmentation. The key idea is based on replacing the traditional regularized Heaviside function with the multinomial logistic regression function, commonly known as Softmax. Segmentation is addressed by solving an optimization problem which considers the image intensities likelihood, a regularizer, based on boundary smoothness, and a pairwise region interactive term, which is naturally derived from the proposed formulation. We demonstrate our method on challenging multi-modal segmentation of MRI scans (4D) of brain tumor patients. Promising results are obtained for image partition into the different healthy brain tissues and the malignant regions.

Fiber-Flux Diffusion Density for White Matter Tracts Analysis: Application to Mild Anomalies Localization in Contact Sports Players.

We present the concept of fiber-flux density for locally quantifying white matter (WM) fiber bundles. By combining scalar diffusivity measures (e.g., fractional anisotropy) with fiber-flux measurements, we define new local descriptors called Fiber-Flux Diffusion Density (FFDD) vectors. Applying each descriptor throughout fiber bundles allows along-tract coupling of a specific diffusion measure with geometrical properties, such as fiber orientation and coherence. A key step in the proposed framework is the construction of an FFDD dissimilarity measure for sub-voxel alignment of fiber bundles, based on the fast marching method (FMM). The obtained aligned WM tract-profiles enable meaningful inter-subject comparisons and group-wise statistical analysis. We demonstrate our method using two different datasets of contact sports players . Along-tract pairwise comparison as well as group-wise analysis, with respect to non-player healthy controls, reveal significant and spatially-consistent FFDD anomalies. Comparing our method with along-tract FA analysis shows improved sensitivity to subtle structural anomalies in football players over standard FA measurements.

Multidimensional co-segmentation of longitudinal brain MRI ensembles in the presence of a neurodegenerative process

Abstract MRI Segmentation of a pathological brain poses a significant challenge, as the available anatomical priors that provide top‐down information to aid segmentation are inadequate in the presence of abnormalities. This problem is further complicated for longitudinal data capturing impaired brain development or neurodegenerative conditions, since the dynamic of brain atrophies has to be considered as well. For these cases, the absence of compatible annotated training examples renders the commonly used multi‐atlas or machine‐learning approaches impractical. We present a novel segmentation approach that accounts for the lack of labeled data via multi‐region multi‐subject co‐segmentation (MMCoSeg) of longitudinal MRI sequences. The underlying, unknown anatomy is learned throughout an iterative process, in which the segmentation of a region is supported both by the segmentation of the neighboring regions, which share common boundaries, and by the segmentation of corresponding regions, in the other jointly segmented images. A 4D multi‐region atlas that models the spatio‐temporal deformations and can be adapted to different subjects undergoing similar degeneration processes is reconstructed concurrently. An inducible mouse model of p25 accumulation (the CK‐p25 mouse) that displays key pathological hallmarks of Alzheimer disease (AD) is used as a gold‐standard to test the proposed algorithm by providing a conditional control of rapid neurodegeneration. Applying the MMCoSeg to a cohort of CK‐p25 mice and littermate controls yields promising segmentation results that demonstrate high compatibility with expertise manual annotations. An extensive comparative analysis with respect to current well‐established, atlas‐based segmentation methods highlights the advantage of the proposed approach, which provides accurate segmentation of longitudinal brain MRIs in pathological conditions, where only very few annotated examples are available. Graphical abstract Figure. No Caption available.

A probabilistic approach to joint cell tracking and segmentation in high-throughput microscopy videos

HighlightsWe propose an unsupervised, automatic tracking and segmentation framework for high‐throughput microscopy image sequences.Cell segmentation and tracking are tied together via Bayesian inference of dynamic models.The Kalman inference problem is exploited to estimate the time‐wise cell shape uncertainty in addition to cell trajectory. The inferred cell properties are integrated with the observed image, using a fast marching algorithm, to obtain the image likelihood for cell segmentation and association.We present highly accurate results, surpassing the state of the art, for a variety of microscopy data sets with high dynamics, including long sequences (hundreds of frames). Graphical abstract Figure. No caption available. ABSTRACT We present a novel computational framework for the analysis of high‐throughput microscopy videos of living cells. The proposed framework is generally useful and can be applied to different datasets acquired in a variety of laboratory settings. This is accomplished by tying together two fundamental aspects of cell lineage construction, namely cell segmentation and tracking, via a Bayesian inference of dynamic models. In contrast to most existing approaches, which aim to be general, no assumption of cell shape is made. Spatial, temporal, and cross‐sectional variation of the analysed data are accommodated by two key contributions. First, time series analysis is exploited to estimate the temporal cell shape uncertainty in addition to cell trajectory. Second, a fast marching (FM) algorithm is used to integrate the inferred cell properties with the observed image measurements in order to obtain image likelihood for cell segmentation, and association. The proposed approach has been tested on eight different time‐lapse microscopy data sets, some of which are high‐throughput, demonstrating promising results for the detection, segmentation and association of planar cells. Our results surpass the state of the art for the Fluo‐C2DL‐MSC data set of the Cell Tracking Challenge (Maška et al., 2014).

Atlas of Classifiers for Brain MRI Segmentation

We present a conceptually novel framework for brain tissue segmentation based on an Atlas of Classifiers (AoC). The AoC allows a statistical summary of the annotated datasets taking into account both the imaging data and the corresponding labels. It is therefore more informative than the classical probabilistic atlas and more economical than the popular multi-atlas approaches, which require large memory consumption and high computational complexity for each segmentation. Specifically, we consider an AoC as a spatial map of voxel-wise multinomial logistic regression (LR) functions learned from the labeled data. Upon convergence, the resulting fixed LR weights (a few for each voxel) represent the training dataset, which might be huge. Segmentation of a new image is therefore immediate and only requires the calculation of the LR outputs based on the respective voxel-wise features. Moreover, the AoC construction is independent of the test images, providing the flexibility to train it on the available labeled data and use it for the segmentation of images from different datasets and modalities.