Deep learning-based BMI inference from structural brain MRI reflects brain alterations following lifestyle intervention

Obesity is associated with negative effects on the brain. We exploit Artificial Intelligence (AI) tools to explore whether differences in clinical measurements following lifestyle interventions in overweight population could be reflected in brain morphology. In the DIRECT-PLUS clinical trial, participants with criterion for metabolic syndrome underwent an 18-month lifestyle intervention. Structural brain MRIs were acquired before and after the intervention. We utilized an ensemble learning framework to predict Body-Mass Index (BMI) scores, which correspond to adiposity-related clinical measurements from brain MRIs. We revealed that patient-specific reduction in BMI predictions was associated with actual weight loss and was significantly higher in active diet groups compared to a control group. Moreover, explainable AI (XAI) maps highlighted brain regions contributing to BMI predictions that were distinct from regions associated with age prediction. Our DIRECT-PLUS analysis results imply that predicted BMI and its reduction are unique neural biomarkers for obesity-related brain modifications and weight loss.

Differentiable Histogram Loss Functions for Intensity-based Image-to-Image Translation

We introduce the HueNet - a novel deep learning framework for a differentiable construction of intensity (1D) and joint (2D) histograms and present its applicability to paired and unpaired image-to-image translation problems. The key idea is an innovative technique for augmenting a generative neural network by histogram layers appended to the image generator. These histogram layers allow us to define two new histogram-based loss functions for constraining the structural appearance of the synthesized output image and its color distribution. Specifically, the color similarity loss is defined by the Earth Mover's Distance between the intensity histograms of the network output and a color reference image. The structural similarity loss is determined by the mutual information between the output and a content reference image based on their joint histogram. Although the HueNet can be applied to a variety of image-to-image translation problems, we chose to demonstrate its strength on the tasks of color transfer, exemplar-based image colorization, and edges → photo, where the colors of the output image are predefined. The code is available at https://github.com/mor-avi-aharon-bgu/HueNet.git.

Fine scale hippocampus morphology variation cross 552 healthy subjects from age 20 to 80

The cerebral cortex varies over the course of a person's life span: at birth, the surface is smooth, before becoming more bumpy (deeper sulci and thicker gyri) in middle age, and thinner in senior years. In this work, a similar phenomenon was observed on the hippocampus. It was previously believed the fine-scale morphology of the hippocampus could only be extracted only with high field scanners (7T, 9.4T); however, recent studies show that regular 3T MR scanners can be sufficient for this purpose. This finding opens the door for the study of fine hippocampal morphometry for a large amount of clinical data. In particular, a characteristic bumpy and subtle feature on the inferior aspect of the hippocampus, which we refer to as hippocampal dentation, presents a dramatic degree of variability between individuals from very smooth to highly dentated. In this report, we propose a combined method joining deep learning and sub-pixel level set evolution to efficiently obtain fine-scale hippocampal segmentation on 552 healthy subjects. Through non-linear dentation extraction and fitting, we reveal that the bumpiness of the inferior surface of the human hippocampus has a clear temporal trend. It is bumpiest between 40 and 50 years old. This observation should be aligned with neurodevelopmental and aging stages.

The Cell Tracking Challenge: 10 years of objective benchmarking

The Cell Tracking Challenge is an ongoing benchmarking initiative that has become a reference in cell segmentation and tracking algorithm development. Here, we present a significant number of improvements introduced in the challenge since our 2017 report. These include the creation of a new segmentation-only benchmark, the enrichment of the dataset repository with new datasets that increase its diversity and complexity, and the creation of a silver standard reference corpus based on the most competitive results, which will be of particular interest for data-hungry deep learning-based strategies. Furthermore, we present the up-to-date cell segmentation and tracking leaderboards, an in-depth analysis of the relationship between the performance of the state-of-the-art methods and the properties of the datasets and annotations, and two novel, insightful studies about the generalizability and the reusability of top-performing methods. These studies provide critical practical conclusions for both developers and users of traditional and machine learning-based cell segmentation and tracking algorithms.

Dual-Task ConvLSTM-UNet for Instance Segmentation of Weakly Annotated Microscopy Videos

Convolutional Neural Networks (CNNs) are considered state of the art segmentation methods for biomedical images in general and microscopy sequences of living cells, in particular. The success of the CNNs is attributed to their ability to capture the structural properties of the data, which enables accommodating complex spatial structures of the cells, low contrast, and unclear boundaries. However, in their standard form CNNs do not exploit the temporal information available in time-lapse sequences, which can be crucial to separating touching and partially overlapping cell instances. In this work, we exploit cell dynamics using a novel CNN architecture which allows multi-scale spatio-temporal feature extraction. Specifically, a novel recurrent neural network (RNN) architecture is proposed based on the integration of a Convolutional Long Short Term Memory (ConvLSTM) network with the U-Net. The proposed ConvLSTM-UNet network is constructed as a dual-task network to enable training with weakly annotated data, in the form of approximate cell centers, termed markers, when the complete cells' outlines are not available. We further use the fast marching method to facilitate the partitioning of clustered cells into individual connected components. Finally, we suggest an adaptation of the method for 3D microscopy sequences without drastically increasing the computational load. The method was evaluated on the Cell Segmentation Benchmark and was ranked among the top three methods on six submitted datasets. Exploiting the proposed built-in marker estimator we also present state-of-the-art cell detection results for an additional, publicly available, weekly annotated dataset. The source code is available at https://gitlab.com/shaked0/lstmUnet.

An atlas of classifiers-a machine learning paradigm for brain MRI segmentation

We present the Atlas of Classifiers (AoC)-a conceptually novel framework for brain MRI segmentation. The AoC is 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. It can, therefore, be considered as a light-weight learning machine, which despite its low capacity does not underfit the problem. The AoC construction is independent of the actual intensities 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. In this sense, it does not overfit the training data, as well. The proposed method has been applied to numerous publicly available datasets for the segmentation of brain MRI tissues and is shown to be robust to noise and outreach commonly used methods. Promising results were also obtained for multi-modal, cross-modality MRI segmentation. Finally, we show how AoC trained on brain MRIs of healthy subjects can be exploited for lesion segmentation of multiple sclerosis patients.

From a deep learning model back to the brain-Identifying regional predictors and their relation to aging

We present a Deep Learning framework for the prediction of chronological age from structural magnetic resonance imaging scans. Previous findings associate increased brain age with neurodegenerative diseases and higher mortality rates. However, the importance of brain age prediction goes beyond serving as biomarkers for neurological disorders. Specifically, utilizing convolutional neural network (CNN) analysis to identify brain regions contributing to the prediction can shed light on the complex multivariate process of brain aging. Previous work examined methods to attribute pixel/voxel-wise contributions to the prediction in a single image, resulting in "explanation maps" that were found noisy and unreliable. To address this problem, we developed an inference scheme for combining these maps across subjects, thus creating a population-based, rather than a subject-specific map. We applied this method to a CNN ensemble trained on predicting subjects' age from raw T1 brain images in a lifespan sample of 10,176 subjects. Evaluating the model on an untouched test set resulted in mean absolute error of 3.07 years and a correlation between chronological and predicted age of r = 0.98. Using the inference method, we revealed that cavities containing cerebrospinal fluid, previously found as general atrophy markers, had the highest contribution for age prediction. Comparing maps derived from different models within the ensemble allowed to assess differences and similarities in brain regions utilized by the model. We showed that this method substantially increased the replicability of explanation maps, converged with results from voxel-based morphometry age studies and highlighted brain regions whose volumetric variability correlated the most with the prediction error.

Subsampled brain MRI reconstruction by generative adversarial neural networks

A main challenge in magnetic resonance imaging (MRI) is speeding up scan time. Beyond improving patient experience and reducing operational costs, faster scans are essential for time-sensitive imaging, such as fetal, cardiac, or functional MRI, where temporal resolution is important and target movement is unavoidable, yet must be reduced. Current MRI acquisition methods speed up scan time at the expense of lower spatial resolution and costlier hardware. We introduce a practical, software-only framework, based on deep learning, for accelerating MRI acquisition, while maintaining anatomically meaningful imaging. This is accomplished by MRI subsampling followed by estimating the missing k-space samples via generative adversarial neural networks. A generator-discriminator interplay enables the introduction of an adversarial cost in addition to fidelity and image-quality losses used for optimizing the reconstruction. Promising reconstruction results are obtained from feasible sampling patterns of up to a fivefold acceleration of diverse brain MRIs, from a large publicly available dataset of healthy adult scans as well as multimodal acquisitions of multiple sclerosis patients and dynamic contrast-enhanced MRI (DCE-MRI) sequences of stroke and tumor patients. Clinical usability of the reconstructed MRI scans is assessed by performing either lesion or healthy tissue segmentation and comparing the results to those obtained by using the original, fully sampled images. Reconstruction quality and usability of the DCE-MRI sequences is demonstrated by calculating the pharmacokinetic (PK) parameters. The proposed MRI reconstruction approach is shown to outperform state-of-the-art methods for all datasets tested in terms of the peak signal-to-noise ratio (PSNR), the structural similarity index (SSIM), as well as either the mean squared error (MSE) with respect to the PK parameters, calculated for the fully sampled DCE-MRI sequences, or the segmentation compatibility, measured in terms of Dice scores and Hausdorff distance. The code is available on GitHub.

Fully unsupervised symmetry-based mitosis detection in time-lapse cell microscopy

MOTIVATION

Cell microscopy datasets have great diversity due to variability in cell types, imaging techniques and protocols. Existing methods are either tailored to specific datasets or are based on supervised learning, which requires comprehensive manual annotations. Using the latter approach, however, poses a significant difficulty due to the imbalance between the number of mitotic cells with respect to the entire cell population in a time-lapse microscopy sequence.

RESULTS

We present a fully unsupervised framework for both mitosis detection and mother-daughters association in fluorescence microscopy data. The proposed method accommodates the difficulty of the different cell appearances and dynamics. Addressing symmetric cell divisions, a key concept is utilizing daughters' similarity. Association is accomplished by defining cell neighborhood via a stochastic version of the Delaunay triangulation and optimization by dynamic programing. Our framework presents promising detection results for a variety of fluorescence microscopy datasets of different sources, including 2D and 3D sequences from the Cell Tracking Challenge.

AVAILABILITY AND IMPLEMENTATION

Code is available in github (github.com/topazgl/mitodix).

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Combining white matter diffusion and geometry for tract-specific alignment and variability analysis

We present a framework for along-tract analysis of white matter (WM) fiber bundles based on diffusion tensor imaging (DTI) and tractography. We introduce the novel concept of fiber-flux density for modeling fiber tracts' geometry, and combine it with diffusion-based measures to define vector descriptors called Fiber-Flux Diffusion Density (FFDD). The proposed model captures informative features of WM tracts at both the microscopic (diffusion-related) and macroscopic (geometry-related) scales, thus enabling improved sensitivity to subtle structural abnormalities that are not reflected by either diffusion or geometrical properties alone. A key step in this 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 tracts enable meaningful inter-subject comparisons and group-wise statistical analysis. Moreover, we show that the FMM alignment can be generalized in a straight forward manner to a single-shot co-alignment of multiple fiber bundles. The proposed alignment technique is shown to outperform a well-established, commonly used DTI registration algorithm. We demonstrate the FFDD framework on the Human Connectome Project (HCP) diffusion MRI dataset, as well as on two different datasets of contact sports players. We test our method using longitudinal scans of a basketball player diagnosed with a traumatic brain injury, showing compatibility with structural MRI findings. We further perform a group study comparing mid- and post-season scans of 13 active football players exposed to repetitive head trauma, to 17 non-player control (NPC) subjects. Results reveal statistically significant FFDD differences (p-values<0.05) between the groups, as well as increased abnormalities over time at spatially-consistent locations within several major fiber tracts of football players.

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

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).

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

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.

The Multimodal Brain Tumor Image Segmentation Benchmark (BRATS)

In this paper we report the set-up and results of the Multimodal Brain Tumor Image Segmentation Benchmark (BRATS) organized in conjunction with the MICCAI 2012 and 2013 conferences. Twenty state-of-the-art tumor segmentation algorithms were applied to a set of 65 multi-contrast MR scans of low- and high-grade glioma patients-manually annotated by up to four raters-and to 65 comparable scans generated using tumor image simulation software. Quantitative evaluations revealed considerable disagreement between the human raters in segmenting various tumor sub-regions (Dice scores in the range 74%-85%), illustrating the difficulty of this task. We found that different algorithms worked best for different sub-regions (reaching performance comparable to human inter-rater variability), but that no single algorithm ranked in the top for all sub-regions simultaneously. Fusing several good algorithms using a hierarchical majority vote yielded segmentations that consistently ranked above all individual algorithms, indicating remaining opportunities for further methodological improvements. The BRATS image data and manual annotations continue to be publicly available through an online evaluation system as an ongoing benchmarking resource.