17 research outputs found
Pulse Sequence Resilient Fast Brain Segmentation
Accurate automatic segmentation of brain anatomy from
-weighted~(-w) magnetic resonance images~(MRI) has been a
computationally intensive bottleneck in neuroimaging pipelines, with
state-of-the-art results obtained by unsupervised intensity modeling-based
methods and multi-atlas registration and label fusion. With the advent of
powerful supervised convolutional neural networks~(CNN)-based learning
algorithms, it is now possible to produce a high quality brain segmentation
within seconds. However, the very supervised nature of these methods makes it
difficult to generalize them on data different from what they have been trained
on. Modern neuroimaging studies are necessarily multi-center initiatives with a
wide variety of acquisition protocols. Despite stringent protocol harmonization
practices, it is not possible to standardize the whole gamut of MRI imaging
parameters across scanners, field strengths, receive coils etc., that affect
image contrast. In this paper we propose a CNN-based segmentation algorithm
that, in addition to being highly accurate and fast, is also resilient to
variation in the input -w acquisition. Our approach relies on building
approximate forward models of -w pulse sequences that produce a typical
test image. We use the forward models to augment the training data with test
data specific training examples. These augmented data can be used to update
and/or build a more robust segmentation model that is more attuned to the test
data imaging properties. Our method generates highly accurate, state-of-the-art
segmentation results~(overall Dice overlap=0.94), within seconds and is
consistent across a wide-range of protocols.Comment: Accepted at MICCAI 201
Contrast Adaptive Tissue Classification by Alternating Segmentation and Synthesis
Deep learning approaches to the segmentation of magnetic resonance images
have shown significant promise in automating the quantitative analysis of brain
images. However, a continuing challenge has been its sensitivity to the
variability of acquisition protocols. Attempting to segment images that have
different contrast properties from those within the training data generally
leads to significantly reduced performance. Furthermore, heterogeneous data
sets cannot be easily evaluated because the quantitative variation due to
acquisition differences often dwarfs the variation due to the biological
differences that one seeks to measure. In this work, we describe an approach
using alternating segmentation and synthesis steps that adapts the contrast
properties of the training data to the input image. This allows input images
that do not resemble the training data to be more consistently segmented. A
notable advantage of this approach is that only a single example of the
acquisition protocol is required to adapt to its contrast properties. We
demonstrate the efficacy of our approaching using brain images from a set of
human subjects scanned with two different T1-weighted volumetric protocols.Comment: 10 pages. MICCAI SASHIMI Workshop 202
PSACNN: Pulse Sequence Adaptive Fast Whole Brain Segmentation
With the advent of convolutional neural networks~(CNN), supervised learning
methods are increasingly being used for whole brain segmentation. However, a
large, manually annotated training dataset of labeled brain images required to
train such supervised methods is frequently difficult to obtain or create. In
addition, existing training datasets are generally acquired with a homogeneous
magnetic resonance imaging~(MRI) acquisition protocol. CNNs trained on such
datasets are unable to generalize on test data with different acquisition
protocols. Modern neuroimaging studies and clinical trials are necessarily
multi-center initiatives with a wide variety of acquisition protocols. Despite
stringent protocol harmonization practices, it is very difficult to standardize
the gamut of MRI imaging parameters across scanners, field strengths, receive
coils etc., that affect image contrast. In this paper we propose a CNN-based
segmentation algorithm that, in addition to being highly accurate and fast, is
also resilient to variation in the input acquisition. Our approach relies on
building approximate forward models of pulse sequences that produce a typical
test image. For a given pulse sequence, we use its forward model to generate
plausible, synthetic training examples that appear as if they were acquired in
a scanner with that pulse sequence. Sampling over a wide variety of pulse
sequences results in a wide variety of augmented training examples that help
build an image contrast invariant model. Our method trains a single CNN that
can segment input MRI images with acquisition parameters as disparate as
-weighted and -weighted contrasts with only -weighted training
data. The segmentations generated are highly accurate with state-of-the-art
results~(overall Dice overlap), with a fast run time~( 45
seconds), and consistent across a wide range of acquisition protocols.Comment: Typo in author name corrected. Greves -> Grev
Machine Learning for Alzheimer’s Disease and Related Dementias
Dementia denotes the condition that affects people suffering from cognitive and behavioral impairments
due to brain damage. Common causes of dementia include Alzheimer’s disease, vascular dementia, or
frontotemporal dementia, among others. The onset of these pathologies often occurs at least a decade
before any clinical symptoms are perceived. Several biomarkers have been developed to gain a better insight
into disease progression, both in the prodromal and the symptomatic phases. Those markers are commonly
derived from genetic information, biofluid, medical images, or clinical and cognitive assessments. Information is nowadays also captured using smart devices to further understand how patients are affected. In the
last two to three decades, the research community has made a great effort to capture and share for research a
large amount of data from many sources. As a result, many approaches using machine learning have been
proposed in the scientific literature. Those include dedicated tools for data harmonization, extraction of
biomarkers that act as disease progression proxy, classification tools, or creation of focused modeling tools
that mimic and help predict disease progression. To date, however, very few methods have been translated
to clinical care, and many challenges still need addressing