Yuankai Huo

Multi-atlas learner fusion: An efficient segmentation approach for large-scale data.

We propose multi-atlas learner fusion (MLF), a framework for rapidly and accurately replicating the highly accurate, yet computationally expensive, multi-atlas segmentation framework based on fusing local learners. In the largest whole-brain multi-atlas study yet reported, multi-atlas segmentations are estimated for a training set of 3464 MR brain images. Using these multi-atlas estimates we (1) estimate a low-dimensional representation for selecting locally appropriate example images, and (2) build AdaBoost learners that map a weak initial segmentation to the multi-atlas segmentation result. Thus, to segment a new target image we project the image into the low-dimensional space, construct a weak initial segmentation, and fuse the trained, locally selected, learners. The MLF framework cuts the runtime on a modern computer from 36 h down to 3-8 min - a 270× speedup - by completely bypassing the need for deformable atlas-target registrations. Additionally, we (1) describe a technique for optimizing the weak initial segmentation and the AdaBoost learning parameters, (2) quantify the ability to replicate the multi-atlas result with mean accuracies approaching the multi-atlas intra-subject reproducibility on a testing set of 380 images, (3) demonstrate significant increases in the reproducibility of intra-subject segmentations when compared to a state-of-the-art multi-atlas framework on a separate reproducibility dataset, (4) show that under the MLF framework the large-scale data model significantly improve the segmentation over the small-scale model under the MLF framework, and (5) indicate that the MLF framework has comparable performance as state-of-the-art multi-atlas segmentation algorithms without using non-local information.

Simultaneous total intracranial volume and posterior fossa volume estimation using multi-atlas label fusion

Total intracranial volume (TICV) is an essential covariate in brain volumetric analyses. The prevalent brain imaging software packages provide automatic TICV estimates. FreeSurfer and FSL estimate TICV using a scaling factor while SPM12 accumulates probabilities of brain tissues. None of the three provide explicit skull/CSF boundary (SCB) since it is challenging to distinguish these dark structures in a T1‐weighted image. However, explicit SCB not only leads to a natural way of obtaining TICV (i.e., counting voxels inside the skull) but also allows sub‐definition of TICV, for example, the posterior fossa volume (PFV). In this article, they proposed to use multi‐atlas label fusion to obtain TICV and PFV simultaneously. The main contributions are: (1) TICV and PFV are simultaneously obtained with explicit SCB from a single T1‐weighted image. (2) TICV and PFV labels are added to the widely used BrainCOLOR atlases. (3) Detailed mathematical derivation of non‐local spatial STAPLE (NLSS) label fusion is presented. As the skull is clearly distinguished in CT images, we use a semi‐manual procedure to obtain atlases with TICV and PFV labels using 20 subjects who both have a MR and CT scan. The proposed method provides simultaneous TICV and PFV estimation while achieving more accurate TICV estimation compared with FreeSurfer, FSL, SPM12, and the previously proposed STAPLE based approach. The newly developed TICV and PFV labels for the OASIS BrainCOLOR atlases provide acceptable performance, which enables simultaneous TICV and PFV estimation during whole brain segmentation. The NLSS method and the new atlases have been made freely available. Hum Brain Mapp 38:599–616, 2017.

Combining Multi-atlas Segmentation with Brain Surface Estimation.

Whole brain segmentation (with comprehensive cortical and subcortical labels) and cortical surface reconstruction are two essential techniques for investigating the human brain. The two tasks are typically conducted independently, however, which leads to spatial inconsistencies and hinders further integrated cortical analyses. To obtain self-consistent whole brain segmentations and surfaces, FreeSurfer segregates the subcortical and cortical segmentations before and after the cortical surface reconstruction. However, this “segmentation to surface to parcellation” strategy has shown limitation in various situations. In this work, we propose a novel “multi-atlas segmentation to surface” method called Multi-atlas CRUISE (MaCRUISE), which achieves self-consistent whole brain segmentations and cortical surfaces by combining multi-atlas segmentation with the cortical reconstruction method CRUISE. To our knowledge, this is the first work that achieves the reliability of state-of-the-art multi-atlas segmentation and labeling methods together with accurate and consistent cortical surface reconstruction. Compared with previous methods, MaCRUISE has three features: (1) MaCRUISE obtains 132 cortical/subcortical labels simultaneously from a single multi-atlas segmentation before reconstructing volume consistent surfaces; (2) Fuzzy tissue memberships are combined with multi-atlas segmentations to address partial volume effects; (3) MaCRUISE reconstructs topologically consistent cortical surfaces by using the sulci locations from multi-atlas segmentation. Two data sets, one consisting of five subjects with expertly traced landmarks and the other consisting of 100 volumes from elderly subjects are used for validation. Compared with CRUISE, MaCRUISE achieves self-consistent whole brain segmentation and cortical reconstruction without compromising on surface accuracy. MaCRUISE is comparably accurate to FreeSurfer while achieving greater robustness across an elderly population.

Theoretical and empirical comparison of big data image processing with Apache Hadoop and Sun Grid Engine

The field of big data is generally concerned with the scale of processing at which traditional computational paradigms break down. In medical imaging, traditional large scale processing uses a cluster computer that combines a group of workstation nodes into a functional unit that is controlled by a job scheduler. Typically, a shared-storage network file system (NFS) is used to host imaging data. However, data transfer from storage to processing nodes can saturate network bandwidth when data is frequently uploaded/retrieved from the NFS, e.g., “short” processing times and/or “large” datasets. Recently, an alternative approach using Hadoop and HBase was presented for medical imaging to enable co-location of data storage and computation while minimizing data transfer. The benefits of using such a framework must be formally evaluated against a traditional approach to characterize the point at which simply “large scale” processing transitions into “big data” and necessitates alternative computational frameworks. The proposed Hadoop system was implemented on a production lab-cluster alongside a standard Sun Grid Engine (SGE). Theoretical models for wall-clock time and resource time for both approaches are introduced and validated. To provide real example data, three T1 image archives were retrieved from a university secure, shared web database and used to empirically assess computational performance under three configurations of cluster hardware (using 72, 109, or 209 CPU cores) with differing job lengths. Empirical results match the theoretical models. Based on these data, a comparative analysis is presented for when the Hadoop framework will be relevant and nonrelevant for medical imaging.

Mapping Lifetime Brain Volumetry with Covariate-Adjusted Restricted Cubic Spline Regression from Cross-Sectional Multi-site MRI

Understanding brain volumetry is essential to understand neurodevelopment and disease. Historically, age-related changes have been studied in detail for specific age ranges (e.g., early childhood, teen, young adults, elderly, etc.) or more sparsely sampled for wider considerations of lifetime aging. Recent advancements in data sharing and robust processing have made available considerable quantities of brain images from normal, healthy volunteers. However, existing analysis approaches have had difficulty addressing (1) complex volumetric developments on the large cohort across the life time (e.g., beyond cubic age trends), (2) accounting for confound effects, and (3) maintaining an analysis framework consistent with the general linear model (GLM) approach pervasive in neuroscience. To address these challenges, we propose to use covariate-adjusted restricted cubic spline (C-RCS) regression within a multi-site cross-sectional framework. This model allows for flexible consideration of non-linear age-associated patterns while accounting for traditional covariates and interaction effects. As a demonstration of this approach on lifetime brain aging, we derive normative volumetric trajectories and 95% confidence intervals from 5111 healthy patients from 64 sites while accounting for confounding sex, intracranial volume and field strength effects. The volumetric results are shown to be consistent with traditional studies that have explored more limited age ranges using single-site analyses. This work represents the first integration of C-RCS with neuroimaging and the derivation of structural covariance networks (SCNs) from a large study of multi-site, cross-sectional data.

Splenomegaly segmentation using global convolutional kernels and conditional generative adversarial networks.

Spleen volume estimation using automated image segmentation technique may be used to detect splenomegaly (abnormally enlarged spleen) on Magnetic Resonance Imaging (MRI) scans. In recent years, Deep Convolutional Neural Networks (DCNN) segmentation methods have demonstrated advantages for abdominal organ segmentation. However, variations in both size and shape of the spleen on MRI images may result in large false positive and false negative labeling when deploying DCNN based methods. In this paper, we propose the Splenomegaly Segmentation Network (SSNet) to address spatial variations when segmenting extraordinarily large spleens. SSNet was designed based on the framework of image-to-image conditional generative adversarial networks (cGAN). Specifically, the Global Convolutional Network (GCN) was used as the generator to reduce false negatives, while the Markovian discriminator (PatchGAN) was used to alleviate false positives. A cohort of clinically acquired 3D MRI scans (both T1 weighted and T2 weighted) from patients with splenomegaly were used to train and test the networks. The experimental results demonstrated that a mean Dice coefficient of 0.9260 and a median Dice coefficient of 0.9262 using SSNet on independently tested MRI volumes of patients with splenomegaly.

Lower gray matter integrity is associated with greater lap time variation in high-functioning older adults

BACKGROUND Lower integrity of cerebral gray matter is associated with higher gait variability. It is not known whether gray matter integrity is associated with higher lap time variation (LTV), a clinically accessible measure of gait variability, high levels of which have been associated with mortality. This study examines the cross-sectional association between gray matter mean diffusivity (MD) and LTV in community-dwelling older adults. METHODS Study participants consisted of 449 high-functioning adults aged 50 and older (56.8% female) in the Baltimore Longitudinal Study of Aging, free of overt neurological disease. The magnitude of MD in the gray matter, a measure of impaired tissue integrity, was assessed by diffusion tensor imaging in 16 regions of interest (ROIs) involved with executive function, sensorimotor function, and memory. LTV was assessed as variability in lap time based on individual trajectories over ten 40-m laps. Age, sex, height, and weight were covariates. The model additionally adjusted for mean lap time and health conditions that may affect LTV. RESULTS Higher levels of average MD across 16 ROIs were significantly associated with higher LTV after adjustment for covariates. Specifically, higher MD in the precuneus and the anterior and middle cingulate cortices was strongly associated with higher LTV, as compared to other ROIs. The association persisted after adjustment for mean lap time, hypertension, and diabetes. CONCLUSIONS Lower gray matter integrity in selected areas may underlie greater LTV in high-functioning community-dwelling older adults. Longitudinal studies are warranted to examine whether changes in gray matter integrity precede more variable gait.

Improved stability of whole brain surface parcellation with multi-atlas segmentation.

Whole brain segmentation and cortical surface parcellation are essential in understanding the brain’s anatomicalfunctional relationships. Multi-atlas segmentation has been regarded as one of the leading segmentation methods for the whole brain segmentation. In our recent work, the multi-atlas technique has been adapted to surface reconstruction using a method called Multi-atlas CRUISE (MaCRUISE). The MaCRUISE method not only performed consistent volumesurface analyses but also showed advantages on robustness compared with the FreeSurfer method. However, a detailed surface parcellation was not provided by MaCRUISE, which hindered the region of interest (ROI) based analyses on surfaces. Herein, the MaCRUISE surface parcellation (MaCRUISEsp) method is proposed to perform the surface parcellation upon the inner, central and outer surfaces that are reconstructed from MaCRUISE. MaCRUISEsp parcellates inner, central and outer surfaces with 98 cortical labels respectively using a volume segmentation based surface parcellation (VSBSP), following a topological correction step. To validate the performance of MaCRUISEsp, 21 scanrescan magnetic resonance imaging (MRI) T1 volume pairs from the Kirby21 dataset were used to perform a reproducibility analyses. MaCRUISEsp achieved 0.948 on median Dice Similarity Coefficient (DSC) for central surfaces. Meanwhile, FreeSurfer achieved 0.905 DSC for inner surfaces and 0.881 DSC for outer surfaces, while the proposed method achieved 0.929 DSC for inner surfaces and 0.835 DSC for outer surfaces. Qualitatively, the results are encouraging, but are not directly comparable as the two approaches use different definitions of cortical labels.

Fully convolutional neural networks improve abdominal organ segmentation.

Abdominal image segmentation is a challenging, yet important clinical problem. Variations in body size, position, and relative organ positions greatly complicate the segmentation process. Historically, multi-atlas methods have achieved leading results across imaging modalities and anatomical targets. However, deep learning is rapidly overtaking classical approaches for image segmentation. Recently, Zhou et al. showed that fully convolutional networks produce excellent results in abdominal organ segmentation of computed tomography (CT) scans. Yet, deep learning approaches have not been applied to whole abdomen magnetic resonance imaging (MRI) segmentation. Herein, we evaluate the applicability of an existing fully convolutional neural network (FCNN) designed for CT imaging to segment abdominal organs on T2 weighted (T2w) MRI’s with two examples. In the primary example, we compare a classical multi-atlas approach with FCNN on forty-five T2w MRI’s acquired from splenomegaly patients with five organs labeled (liver, spleen, left kidney, right kidney, and stomach). Thirty-six images were used for training while nine were used for testing. The FCNN resulted in a Dice similarity coefficient (DSC) of 0.930 in spleens, 0.730 in left kidneys, 0.780 in right kidneys, 0.913 in livers, and 0.556 in stomachs. The performance measures for livers, spleens, right kidneys, and stomachs were significantly better than multi-atlas (p < 0.05, Wilcoxon rank-sum test). In a secondary example, we compare the multi-atlas approach with FCNN on 138 distinct T2w MRI’s with manually labeled pancreases (one label). On the pancreas dataset, the FCNN resulted in a median DSC of 0.691 in pancreases versus 0.287 for multi-atlas. The results are highly promising given relatively limited training data and without specific training of the FCNN model and illustrate the potential of deep learning approaches to transcend imaging modalities. 1

Gray Matter Surface Based Spatial Statistics (GS-BSS) in Diffusion Microstructure

Tract-based spatial statistics (TBSS) has proven to be a popular technique for performing voxel-wise statistical analysis that aims to improve sensitivity and interpretability of analysis of multi-subject diffusion imaging studies in white matter. With the advent of advanced diffusion MRI models - e.g., the neurite orientation dispersion density imaging (NODDI), it is of interest to analyze microstructural changes within gray matter (GM). A recent study has proposed using NODDI in gray matter based spatial statistics (N-GBSS) to perform voxel-wise statistical analysis on GM microstructure. N-GBSS adapts TBSS by skeletonizing the GM and projecting diffusion metrics to a cortical ribbon. In this study, we propose an alternate approach, known as gray matter surface based spatial statistics (GS-BSS), to perform statistical analysis using gray matter surfaces by incorporating established methods of registration techniques of GM surface segmentation on structural images. Diffusion microstructure features from NODDI and GM surfaces are transferred to standard space. All the surfaces are then projected onto a common GM surface non-linearly using diffeomorphic spectral matching on cortical surfaces. Prior post-mortem studies have shown reduced dendritic length in prefrontal cortex region in schizophrenia and bipolar disorder population. To validate the results, statistical tests are compared between GS-BSS and N-GBSS to study the differences between healthy and psychosis population. Significant results confirming the microstructural changes are presented. GS-BSS results show higher sensitivity to group differences between healthy and psychosis population in previously known regions.