328 research outputs found
4D Multi-atlas Label Fusion using Longitudinal Images
Longitudinal reproducibility is an essential concern in automated medical
image segmentation, yet has proven to be an elusive objective as manual brain
structure tracings have shown more than 10% variability. To improve
reproducibility, lon-gitudinal segmentation (4D) approaches have been
investigated to reconcile tem-poral variations with traditional 3D approaches.
In the past decade, multi-atlas la-bel fusion has become a state-of-the-art
segmentation technique for 3D image and many efforts have been made to adapt it
to a 4D longitudinal fashion. However, the previous methods were either limited
by using application specified energy function (e.g., surface fusion and multi
model fusion) or only considered tem-poral smoothness on two consecutive time
points (t and t+1) under sparsity as-sumption. Therefore, a 4D multi-atlas
label fusion theory for general label fusion purpose and simultaneously
considering temporal consistency on all time points is appealing. Herein, we
propose a novel longitudinal label fusion algorithm, called 4D joint label
fusion (4DJLF), to incorporate the temporal consistency modeling via non-local
patch-intensity covariance models. The advantages of 4DJLF include: (1) 4DJLF
is under the general label fusion framework by simul-taneously incorporating
the spatial and temporal covariance on all longitudinal time points. (2) The
proposed algorithm is a longitudinal generalization of a lead-ing joint label
fusion method (JLF) that has proven adaptable to a wide variety of
applications. (3) The spatial temporal consistency of atlases is modeled in a
prob-abilistic model inspired from both voting based and statistical fusion.
The pro-posed approach improves the consistency of the longitudinal
segmentation while retaining sensitivity compared with original JLF approach
using the same set of atlases. The method is available online in open-source
Improved Stability of Whole Brain Surface Parcellation with Multi-Atlas Segmentation
Whole brain segmentation and cortical surface parcellation are essential in
understanding the anatomical-functional relationships of the brain. 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 volume-surface
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 scan-rescan 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.Comment: SPIE Medical Imaging 201
Opportunities for Mining Radiology Archives for Pediatric Control Images
A large database of brain imaging data from healthy, normal controls is
useful to describe physiologic and pathologic structural changes at a
population scale. In particular, these data can provide information about
structural changes throughout development and aging. However, scarcity of
control data as well as technical challenges during imaging acquisition has
made it difficult to collect large amounts of data in a healthy pediatric
population. In this study, we search the medical record at Vanderbilt
University Medical Center for pediatric patients who received brain imaging,
either CT or MRI, according to 7 common complaints: headache, seizure, altered
level of consciousness, nausea and vomiting, dizziness, head injury, and gait
abnormalities in order to find the percent of studies that demonstrated
pathologic findings. Using a text-search based algorithm, we show that an
average of 59.3% of MRI studies and 37.3% of CT scans are classified as normal,
resulting in the production of thousands of normal images. These results
suggest there is a wealth of pediatric imaging control data which can be used
to create normative descriptions of development as well as to establish
biomarkers for disease.Comment: MASI Brief Repor
Reproducibility Evaluation of SLANT Whole Brain Segmentation Across Clinical Magnetic Resonance Imaging Protocols
Whole brain segmentation on structural magnetic resonance imaging (MRI) is
essential for understanding neuroanatomical-functional relationships.
Traditionally, multi-atlas segmentation has been regarded as the standard
method for whole brain segmentation. In past few years, deep convolutional
neural network (DCNN) segmentation methods have demonstrated their advantages
in both accuracy and computational efficiency. Recently, we proposed the
spatially localized atlas network tiles (SLANT) method, which is able to
segment a 3D MRI brain scan into 132 anatomical regions. Commonly, DCNN
segmentation methods yield inferior performance under external validations,
especially when the testing patterns were not presented in the training
cohorts. Recently, we obtained a clinically acquired, multi-sequence MRI brain
cohort with 1480 clinically acquired, de-identified brain MRI scans on 395
patients using seven different MRI protocols. Moreover, each subject has at
least two scans from different MRI protocols. Herein, we assess the SLANT
method's intra- and inter-protocol reproducibility. SLANT achieved less than
0.05 coefficient of variation (CV) for intra-protocol experiments and less than
0.15 CV for inter-protocol experiments. The results show that the SLANT method
achieved high intra- and inter- protocol reproducibility.Comment: To appear in SPIE Medical Imaging 201
Learning Implicit Brain MRI Manifolds with Deep Learning
An important task in image processing and neuroimaging is to extract
quantitative information from the acquired images in order to make observations
about the presence of disease or markers of development in populations. Having
a lowdimensional manifold of an image allows for easier statistical comparisons
between groups and the synthesis of group representatives. Previous studies
have sought to identify the best mapping of brain MRI to a low-dimensional
manifold, but have been limited by assumptions of explicit similarity measures.
In this work, we use deep learning techniques to investigate implicit manifolds
of normal brains and generate new, high-quality images. We explore implicit
manifolds by addressing the problems of image synthesis and image denoising as
important tools in manifold learning. First, we propose the unsupervised
synthesis of T1-weighted brain MRI using a Generative Adversarial Network (GAN)
by learning from 528 examples of 2D axial slices of brain MRI. Synthesized
images were first shown to be unique by performing a crosscorrelation with the
training set. Real and synthesized images were then assessed in a blinded
manner by two imaging experts providing an image quality score of 1-5. The
quality score of the synthetic image showed substantial overlap with that of
the real images. Moreover, we use an autoencoder with skip connections for
image denoising, showing that the proposed method results in higher PSNR than
FSL SUSAN after denoising. This work shows the power of artificial networks to
synthesize realistic imaging data, which can be used to improve image
processing techniques and provide a quantitative framework to structural
changes in the brain.Comment: SPIE Medical Imaging 201
Estimated Incidence of Ophthalmic Conditions Associated with Optic Nerve Disease in Middle Tennessee
Aims. The objective of this paper is to determine the incidence of ophthalmic
disease potentially leading to optic nerve disease in Middle Tennessee.
Methods. We use a retrospective population-based incidence study design
focusing on the population of middle Tennessee and its nearby suburbs (N=3 397
515). The electronic medical records for all patients evaluated or treated at a
large tertiary care hospital and clinics with an initial diagnosis of a disease
either affecting the optic nerve, or potentially associated with optic nerve
disease, between 2007 and 2014 were retrieved and analyzed. Results. 18 291
patients (10 808 F) with 18 779 incidence events were identified with an age
range of 0-101 years from the query of the Vanderbilt BioVU. Estimated
age-adjusted incidence per 100 000 population per year was 198.4/145.1
(glaucoma F/M), 14.4/11.4 (intrinsic optic nerve disease F/M), 10.6/5.8 (optic
nerve edema F/M), 6.1/6.6 (orbital inflammation F/M), and 23.7/6.7 (thyroid
disease F/M). Glaucoma incidence was strongly correlated with age with the
incidence sharply increasing after age 40. Optic nerve edema incidence peaked
in the 25-34 old females. African American population has increased likelihood
of glaucoma, orbital inflammation, and thyroid disease. Conclusions. Mapping
the incidence of pathologies of the optic nerve is essential to the
understanding of the relative likelihood of these conditions and impacts upon
public health. We find incidence of optic nerve diseases strongly varies by
gender, age, and race which have not been previously studied using a unified
framework or within a single metropolitan populatio
A Data Colocation Grid Framework for Big Data Medical Image Processing - Backend Design
When processing large medical imaging studies, adopting high performance grid
computing resources rapidly becomes important. We recently presented a "medical
image processing-as-a-service" grid framework that offers promise in utilizing
the Apache Hadoop ecosystem and HBase for data colocation by moving computation
close to medical image storage. However, the framework has not yet proven to be
easy to use in a heterogeneous hardware environment. Furthermore, the system
has not yet validated when considering variety of multi-level analysis in
medical imaging. Our target criteria are (1) improving the framework's
performance in a heterogeneous cluster, (2) performing population based summary
statistics on large datasets, and (3) introducing a table design scheme for
rapid NoSQL query. In this paper, we present a backend interface application
program interface design for Hadoop & HBase for Medical Image Processing. The
API includes: Upload, Retrieve, Remove, Load balancer and MapReduce templates.
A dataset summary statistic model is discussed and implemented by MapReduce
paradigm. We introduce a HBase table scheme for fast data query to better
utilize the MapReduce model. Briefly, 5153 T1 images were retrieved from a
university secure database and used to empirically access an in-house grid with
224 heterogeneous CPU cores. Three empirical experiments results are presented
and discussed: (1) load balancer wall-time improvement of 1.5-fold compared
with a framework with built-in data allocation strategy, (2) a summary
statistic model is empirically verified on grid framework and is compared with
the cluster when deployed with a standard Sun Grid Engine, which reduces 8-fold
of wall clock time and 14-fold of resource time, and (3) the proposed HBase
table scheme improves MapReduce computation with 7 fold reduction of wall time
compare with a na\"ive scheme when datasets are relative small.Comment: Accepted and awaiting publication at SPIE Medical Imaging,
International Society for Optics and Photonics, 201
Distributed deep learning for robust multi-site segmentation of CT imaging after traumatic brain injury
Machine learning models are becoming commonplace in the domain of medical
imaging, and with these methods comes an ever-increasing need for more data.
However, to preserve patient anonymity it is frequently impractical or
prohibited to transfer protected health information (PHI) between institutions.
Additionally, due to the nature of some studies, there may not be a large
public dataset available on which to train models. To address this conundrum,
we analyze the efficacy of transferring the model itself in lieu of data
between different sites. By doing so we accomplish two goals: 1) the model
gains access to training on a larger dataset that it could not normally obtain
and 2) the model better generalizes, having trained on data from separate
locations. In this paper, we implement multi-site learning with disparate
datasets from the National Institutes of Health (NIH) and Vanderbilt University
Medical Center (VUMC) without compromising PHI. Three neural networks are
trained to convergence on a computed tomography (CT) brain hematoma
segmentation task: one only with NIH data,one only with VUMC data, and one
multi-site model alternating between NIH and VUMC data. Resultant lesion masks
with the multi-site model attain an average Dice similarity coefficient of 0.64
and the automatically segmented hematoma volumes correlate to those done
manually with a Pearson correlation coefficient of 0.87,corresponding to an 8%
and 5% improvement, respectively, over the single-site model counterparts
Spatially Localized Atlas Network Tiles Enables 3D Whole Brain Segmentation from Limited Data
Whole brain segmentation on a structural magnetic resonance imaging (MRI) is
essential in non-invasive investigation for neuroanatomy. Historically,
multi-atlas segmentation (MAS) has been regarded as the de facto standard
method for whole brain segmentation. Recently, deep neural network approaches
have been applied to whole brain segmentation by learning random patches or 2D
slices. Yet, few previous efforts have been made on detailed whole brain
segmentation using 3D networks due to the following challenges: (1) fitting
entire whole brain volume into 3D networks is restricted by the current GPU
memory, and (2) the large number of targeting labels (e.g., > 100 labels) with
limited number of training 3D volumes (e.g., < 50 scans). In this paper, we
propose the spatially localized atlas network tiles (SLANT) method to
distribute multiple independent 3D fully convolutional networks to cover
overlapped sub-spaces in a standard atlas space. This strategy simplifies the
whole brain learning task to localized sub-tasks, which was enabled by combing
canonical registration and label fusion techniques with deep learning. To
address the second challenge, auxiliary labels on 5111 initially unlabeled
scans were created by MAS for pre-training. From empirical validation, the
state-of-the-art MAS method achieved mean Dice value of 0.76, 0.71, and 0.68,
while the proposed method achieved 0.78, 0.73, and 0.71 on three validation
cohorts. Moreover, the computational time reduced from > 30 hours using MAS to
~15 minutes using the proposed method. The source code is available online
https://github.com/MASILab/SLANTbrainSegComment: To appear in MICCAI201
Lung Cancer Detection using Co-learning from Chest CT Images and Clinical Demographics
Early detection of lung cancer is essential in reducing mortality. Recent
studies have demonstrated the clinical utility of low-dose computed tomography
(CT) to detect lung cancer among individuals selected based on very limited
clinical information. However, this strategy yields high false positive rates,
which can lead to unnecessary and potentially harmful procedures. To address
such challenges, we established a pipeline that co-learns from detailed
clinical demographics and 3D CT images. Toward this end, we leveraged data from
the Consortium for Molecular and Cellular Characterization of Screen-Detected
Lesions (MCL), which focuses on early detection of lung cancer. A 3D
attention-based deep convolutional neural net (DCNN) is proposed to identify
lung cancer from the chest CT scan without prior anatomical location of the
suspicious nodule. To improve upon the non-invasive discrimination between
benign and malignant, we applied a random forest classifier to a dataset
integrating clinical information to imaging data. The results show that the AUC
obtained from clinical demographics alone was 0.635 while the attention network
alone reached an accuracy of 0.687. In contrast when applying our proposed
pipeline integrating clinical and imaging variables, we reached an AUC of 0.787
on the testing dataset. The proposed network both efficiently captures
anatomical information for classification and also generates attention maps
that explain the features that drive performance.Comment: SPIE Medical Image, oral presentatio
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