2,774 research outputs found
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
Shallow vs deep learning architectures for white matter lesion segmentation in the early stages of multiple sclerosis
In this work, we present a comparison of a shallow and a deep learning
architecture for the automated segmentation of white matter lesions in MR
images of multiple sclerosis patients. In particular, we train and test both
methods on early stage disease patients, to verify their performance in
challenging conditions, more similar to a clinical setting than what is
typically provided in multiple sclerosis segmentation challenges. Furthermore,
we evaluate a prototype naive combination of the two methods, which refines the
final segmentation. All methods were trained on 32 patients, and the evaluation
was performed on a pure test set of 73 cases. Results show low lesion-wise
false positives (30%) for the deep learning architecture, whereas the shallow
architecture yields the best Dice coefficient (63%) and volume difference
(19%). Combining both shallow and deep architectures further improves the
lesion-wise metrics (69% and 26% lesion-wise true and false positive rate,
respectively).Comment: Accepted to the MICCAI 2018 Brain Lesion (BrainLes) worksho
Simple Methods for Scanner Drift Normalization Validated for Automatic Segmentation of Knee Magnetic Resonance Imaging:with data from the Osteoarthritis Initiative
Scanner drift is a well-known magnetic resonance imaging (MRI) artifact
characterized by gradual signal degradation and scan intensity changes over
time. In addition, hardware and software updates may imply abrupt changes in
signal. The combined effects are particularly challenging for automatic image
analysis methods used in longitudinal studies. The implication is increased
measurement variation and a risk of bias in the estimations (e.g. in the volume
change for a structure). We proposed two quite different approaches for scanner
drift normalization and demonstrated the performance for segmentation of knee
MRI using the fully automatic KneeIQ framework. The validation included a total
of 1975 scans from both high-field and low-field MRI. The results demonstrated
that the pre-processing method denoted Atlas Affine Normalization significantly
removed scanner drift effects and ensured that the cartilage volume change
quantifications became consistent with manual expert scores
Mapping the human cortical surface by combining quantitative T(1) with retinotopy
We combined quantitative relaxation rate (R1= 1/T1) mapping-to measure local myelination-with fMRI-based retinotopy. Gray-white and pial surfaces were reconstructed and used to sample R1 at different cortical depths. Like myelination, R1 decreased from deeper to superficial layers. R1 decreased passing from V1 and MT, to immediately surrounding areas, then to the angular gyrus. High R1 was correlated across the cortex with convex local curvature so the data was first "de-curved". By overlaying R1 and retinotopic maps, we found that many visual area borders were associated with significant R1 increases including V1, V3A, MT, V6, V6A, V8/VO1, FST, and VIP. Surprisingly, retinotopic MT occupied only the posterior portion of an oval-shaped lateral occipital R1 maximum. R1 maps were reproducible within individuals and comparable between subjects without intensity normalization, enabling multi-center studies of development, aging, and disease progression, and structure/function mapping in other modalities
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