31,886 research outputs found
Saliency-guided integration of multiple scans
we present a novel method..
Computerized Analysis of Magnetic Resonance Images to Study Cerebral Anatomy in Developing Neonates
The study of cerebral anatomy in developing neonates is of great importance for
the understanding of brain development during the early period of life. This
dissertation therefore focuses on three challenges in the modelling of cerebral
anatomy in neonates during brain development. The methods that have been
developed all use Magnetic Resonance Images (MRI) as source data.
To facilitate study of vascular development in the neonatal period, a set of image
analysis algorithms are developed to automatically extract and model cerebral
vessel trees. The whole process consists of cerebral vessel tracking from
automatically placed seed points, vessel tree generation, and vasculature
registration and matching. These algorithms have been tested on clinical Time-of-
Flight (TOF) MR angiographic datasets.
To facilitate study of the neonatal cortex a complete cerebral cortex segmentation
and reconstruction pipeline has been developed. Segmentation of the neonatal
cortex is not effectively done by existing algorithms designed for the adult brain
because the contrast between grey and white matter is reversed. This causes pixels
containing tissue mixtures to be incorrectly labelled by conventional methods. The
neonatal cortical segmentation method that has been developed is based on a novel
expectation-maximization (EM) method with explicit correction for mislabelled
partial volume voxels. Based on the resulting cortical segmentation, an implicit
surface evolution technique is adopted for the reconstruction of the cortex in
neonates. The performance of the method is investigated by performing a detailed
landmark study.
To facilitate study of cortical development, a cortical surface registration algorithm
for aligning the cortical surface is developed. The method first inflates extracted
cortical surfaces and then performs a non-rigid surface registration using free-form
deformations (FFDs) to remove residual alignment. Validation experiments using
data labelled by an expert observer demonstrate that the method can capture local
changes and follow the growth of specific sulcus
EchoFusion: Tracking and Reconstruction of Objects in 4D Freehand Ultrasound Imaging without External Trackers
Ultrasound (US) is the most widely used fetal imaging technique. However, US
images have limited capture range, and suffer from view dependent artefacts
such as acoustic shadows. Compounding of overlapping 3D US acquisitions into a
high-resolution volume can extend the field of view and remove image artefacts,
which is useful for retrospective analysis including population based studies.
However, such volume reconstructions require information about relative
transformations between probe positions from which the individual volumes were
acquired. In prenatal US scans, the fetus can move independently from the
mother, making external trackers such as electromagnetic or optical tracking
unable to track the motion between probe position and the moving fetus. We
provide a novel methodology for image-based tracking and volume reconstruction
by combining recent advances in deep learning and simultaneous localisation and
mapping (SLAM). Tracking semantics are established through the use of a
Residual 3D U-Net and the output is fed to the SLAM algorithm. As a proof of
concept, experiments are conducted on US volumes taken from a whole body fetal
phantom, and from the heads of real fetuses. For the fetal head segmentation,
we also introduce a novel weak annotation approach to minimise the required
manual effort for ground truth annotation. We evaluate our method
qualitatively, and quantitatively with respect to tissue discrimination
accuracy and tracking robustness.Comment: MICCAI Workshop on Perinatal, Preterm and Paediatric Image analysis
(PIPPI), 201
Automated detection of brain abnormalities in neonatal hypoxia ischemic injury from MR images.
We compared the efficacy of three automated brain injury detection methods, namely symmetry-integrated region growing (SIRG), hierarchical region splitting (HRS) and modified watershed segmentation (MWS) in human and animal magnetic resonance imaging (MRI) datasets for the detection of hypoxic ischemic injuries (HIIs). Diffusion weighted imaging (DWI, 1.5T) data from neonatal arterial ischemic stroke (AIS) patients, as well as T2-weighted imaging (T2WI, 11.7T, 4.7T) at seven different time-points (1, 4, 7, 10, 17, 24 and 31 days post HII) in rat-pup model of hypoxic ischemic injury were used to assess the temporal efficacy of our computational approaches. Sensitivity, specificity, and similarity were used as performance metrics based on manual ('gold standard') injury detection to quantify comparisons. When compared to the manual gold standard, automated injury location results from SIRG performed the best in 62% of the data, while 29% for HRS and 9% for MWS. Injury severity detection revealed that SIRG performed the best in 67% cases while 33% for HRS. Prior information is required by HRS and MWS, but not by SIRG. However, SIRG is sensitive to parameter-tuning, while HRS and MWS are not. Among these methods, SIRG performs the best in detecting lesion volumes; HRS is the most robust, while MWS lags behind in both respects
Stratified decision forests for accurate anatomical landmark localization in cardiac images
Accurate localization of anatomical landmarks is an important step in medical imaging, as it provides useful prior information for subsequent image analysis and acquisition methods. It is particularly useful for initialization of automatic image analysis tools (e.g. segmentation and registration) and detection of scan planes for automated image acquisition. Landmark localization has been commonly performed using learning based approaches, such as classifier and/or regressor models. However, trained models may not generalize well in heterogeneous datasets when the images contain large differences due to size, pose and shape variations of organs. To learn more data-adaptive and patient specific models, we propose a novel stratification based training model, and demonstrate its use in a decision forest. The proposed approach does not require any additional training information compared to the standard model training procedure and can be easily integrated into any decision tree framework. The proposed method is evaluated on 1080 3D highresolution and 90 multi-stack 2D cardiac cine MR images. The experiments show that the proposed method achieves state-of-theart landmark localization accuracy and outperforms standard regression and classification based approaches. Additionally, the proposed method is used in a multi-atlas segmentation to create a fully automatic segmentation pipeline, and the results show that it achieves state-of-the-art segmentation accuracy
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
Automatic 3D bi-ventricular segmentation of cardiac images by a shape-refined multi-task deep learning approach
Deep learning approaches have achieved state-of-the-art performance in
cardiac magnetic resonance (CMR) image segmentation. However, most approaches
have focused on learning image intensity features for segmentation, whereas the
incorporation of anatomical shape priors has received less attention. In this
paper, we combine a multi-task deep learning approach with atlas propagation to
develop a shape-constrained bi-ventricular segmentation pipeline for short-axis
CMR volumetric images. The pipeline first employs a fully convolutional network
(FCN) that learns segmentation and landmark localisation tasks simultaneously.
The architecture of the proposed FCN uses a 2.5D representation, thus combining
the computational advantage of 2D FCNs networks and the capability of
addressing 3D spatial consistency without compromising segmentation accuracy.
Moreover, the refinement step is designed to explicitly enforce a shape
constraint and improve segmentation quality. This step is effective for
overcoming image artefacts (e.g. due to different breath-hold positions and
large slice thickness), which preclude the creation of anatomically meaningful
3D cardiac shapes. The proposed pipeline is fully automated, due to network's
ability to infer landmarks, which are then used downstream in the pipeline to
initialise atlas propagation. We validate the pipeline on 1831 healthy subjects
and 649 subjects with pulmonary hypertension. Extensive numerical experiments
on the two datasets demonstrate that our proposed method is robust and capable
of producing accurate, high-resolution and anatomically smooth bi-ventricular
3D models, despite the artefacts in input CMR volumes
Multi-Atlas Segmentation using Partially Annotated Data: Methods and Annotation Strategies
Multi-atlas segmentation is a widely used tool in medical image analysis,
providing robust and accurate results by learning from annotated atlas
datasets. However, the availability of fully annotated atlas images for
training is limited due to the time required for the labelling task.
Segmentation methods requiring only a proportion of each atlas image to be
labelled could therefore reduce the workload on expert raters tasked with
annotating atlas images. To address this issue, we first re-examine the
labelling problem common in many existing approaches and formulate its solution
in terms of a Markov Random Field energy minimisation problem on a graph
connecting atlases and the target image. This provides a unifying framework for
multi-atlas segmentation. We then show how modifications in the graph
configuration of the proposed framework enable the use of partially annotated
atlas images and investigate different partial annotation strategies. The
proposed method was evaluated on two Magnetic Resonance Imaging (MRI) datasets
for hippocampal and cardiac segmentation. Experiments were performed aimed at
(1) recreating existing segmentation techniques with the proposed framework and
(2) demonstrating the potential of employing sparsely annotated atlas data for
multi-atlas segmentation
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