1,207 research outputs found
Integrating semi-supervised label propagation and random forests for multi-atlas based hippocampus segmentation
A novel multi-atlas based image segmentation method is proposed by
integrating a semi-supervised label propagation method and a supervised random
forests method in a pattern recognition based label fusion framework. The
semi-supervised label propagation method takes into consideration local and
global image appearance of images to be segmented and segments the images by
propagating reliable segmentation results obtained by the supervised random
forests method. Particularly, the random forests method is used to train a
regression model based on image patches of atlas images for each voxel of the
images to be segmented. The regression model is used to obtain reliable
segmentation results to guide the label propagation for the segmentation. The
proposed method has been compared with state-of-the-art multi-atlas based image
segmentation methods for segmenting the hippocampus in MR images. The
experiment results have demonstrated that our method obtained superior
segmentation performance.Comment: Accepted paper in IEEE International Symposium on Biomedical Imaging
(ISBI), 201
Attention Gated Networks: Learning to Leverage Salient Regions in Medical Images
We propose a novel attention gate (AG) model for medical image analysis that
automatically learns to focus on target structures of varying shapes and sizes.
Models trained with AGs implicitly learn to suppress irrelevant regions in an
input image while highlighting salient features useful for a specific task.
This enables us to eliminate the necessity of using explicit external
tissue/organ localisation modules when using convolutional neural networks
(CNNs). AGs can be easily integrated into standard CNN models such as VGG or
U-Net architectures with minimal computational overhead while increasing the
model sensitivity and prediction accuracy. The proposed AG models are evaluated
on a variety of tasks, including medical image classification and segmentation.
For classification, we demonstrate the use case of AGs in scan plane detection
for fetal ultrasound screening. We show that the proposed attention mechanism
can provide efficient object localisation while improving the overall
prediction performance by reducing false positives. For segmentation, the
proposed architecture is evaluated on two large 3D CT abdominal datasets with
manual annotations for multiple organs. Experimental results show that AG
models consistently improve the prediction performance of the base
architectures across different datasets and training sizes while preserving
computational efficiency. Moreover, AGs guide the model activations to be
focused around salient regions, which provides better insights into how model
predictions are made. The source code for the proposed AG models is publicly
available.Comment: Accepted for Medical Image Analysis (Special Issue on Medical Imaging
with Deep Learning). arXiv admin note: substantial text overlap with
arXiv:1804.03999, arXiv:1804.0533
Fully Automatic Segmentation of Lumbar Vertebrae from CT Images using Cascaded 3D Fully Convolutional Networks
We present a method to address the challenging problem of segmentation of
lumbar vertebrae from CT images acquired with varying fields of view. Our
method is based on cascaded 3D Fully Convolutional Networks (FCNs) consisting
of a localization FCN and a segmentation FCN. More specifically, in the first
step we train a regression 3D FCN (we call it "LocalizationNet") to find the
bounding box of the lumbar region. After that, a 3D U-net like FCN (we call it
"SegmentationNet") is then developed, which after training, can perform a
pixel-wise multi-class segmentation to map a cropped lumber region volumetric
data to its volume-wise labels. Evaluated on publicly available datasets, our
method achieved an average Dice coefficient of 95.77 0.81% and an average
symmetric surface distance of 0.37 0.06 mm.Comment: 5 pages and 5 figure
Probabilistic Atlas Based Segmentation Using Affine Moment Descriptors and Graph-Cuts
We show a procedure for constructing a probabilistic atlas based on affine moment descriptors. It uses a normalization procedure over the labeled atlas. The proposed linear registration is defined by closed-form expressions involving only geometric moments. This procedure applies both to atlas construction as atlas-based segmentation. We model the likelihood term for each voxel and each label using parametric or nonparametric distributions and the prior term is determined by applying the vote-rule. The probabilistic atlas is built with the variability of our linear registration. We have two segmentation strategy: a) it applies the proposed affine registration to bring the target image into the coordinate frame of the atlas or b) the probabilistic atlas is non-rigidly aligning with the target image, where the probabilistic atlas is previously aligned to the target image with our affine registration. Finally, we adopt a graph cut - Bayesian framework for implementing the atlas-based segmentation
Affine Registration of label maps in Label Space
Two key aspects of coupled multi-object shape\ud
analysis and atlas generation are the choice of representation\ud
and subsequent registration methods used to align the sample\ud
set. For example, a typical brain image can be labeled into\ud
three structures: grey matter, white matter and cerebrospinal\ud
fluid. Many manipulations such as interpolation, transformation,\ud
smoothing, or registration need to be performed on these images\ud
before they can be used in further analysis. Current techniques\ud
for such analysis tend to trade off performance between the two\ud
tasks, performing well for one task but developing problems when\ud
used for the other.\ud
This article proposes to use a representation that is both\ud
flexible and well suited for both tasks. We propose to map object\ud
labels to vertices of a regular simplex, e.g. the unit interval for\ud
two labels, a triangle for three labels, a tetrahedron for four\ud
labels, etc. This representation, which is routinely used in fuzzy\ud
classification, is ideally suited for representing and registering\ud
multiple shapes. On closer examination, this representation\ud
reveals several desirable properties: algebraic operations may\ud
be done directly, label uncertainty is expressed as a weighted\ud
mixture of labels (probabilistic interpretation), interpolation is\ud
unbiased toward any label or the background, and registration\ud
may be performed directly.\ud
We demonstrate these properties by using label space in a gradient\ud
descent based registration scheme to obtain a probabilistic\ud
atlas. While straightforward, this iterative method is very slow,\ud
could get stuck in local minima, and depends heavily on the initial\ud
conditions. To address these issues, two fast methods are proposed\ud
which serve as coarse registration schemes following which the\ud
iterative descent method can be used to refine the results. Further,\ud
we derive an analytical formulation for direct computation of the\ud
"group mean" from the parameters of pairwise registration of all\ud
the images in the sample set. We show results on richly labeled\ud
2D and 3D data sets
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