187 research outputs found
3D deep convolutional neural network-based ventilated lung segmentation using multi-nuclear hyperpolarized gas MRI
Hyperpolarized gas MRI enables visualization of regional lung ventilation with high spatial resolution. Segmentation of the ventilated lung is required to calculate clinically relevant biomarkers. Recent research in deep learning (DL) has shown promising results for numerous segmentation problems. In this work, we evaluate a 3D V-Net to segment ventilated lung regions on hyperpolarized gas MRI scans. The dataset consists of 743 helium-3 (3He) or xenon-129 (129Xe) volumetric scans and corresponding expert segmentations from 326 healthy subjects and patients with a wide range of pathologies. We evaluated segmentation performance for several DL experimental methods via overlap, distance and error metrics and compared them to conventional segmentation methods, namely, spatial fuzzy c-means (SFCM) and K-means clustering. We observed that training on combined 3He and 129Xe MRI scans outperformed other DL methods, achieving a mean ± SD Dice of 0.958 ± 0.022, average boundary Hausdorff distance of 2.22 ± 2.16 mm, Hausdorff 95th percentile of 8.53 ± 12.98 mm and relative error of 0.087 ± 0.049. Moreover, no difference in performance was observed between 129Xe and 3He scans in the testing set. Combined training on 129Xe and 3He yielded statistically significant improvements over the conventional methods (p < 0.0001). The DL approach evaluated provides accurate, robust and rapid segmentations of ventilated lung regions and successfully excludes non-lung regions such as the airways and noise artifacts and is expected to eliminate the need for, or significantly reduce, subsequent time-consuming manual editing
HeMIS: Hetero-Modal Image Segmentation
We introduce a deep learning image segmentation framework that is extremely
robust to missing imaging modalities. Instead of attempting to impute or
synthesize missing data, the proposed approach learns, for each modality, an
embedding of the input image into a single latent vector space for which
arithmetic operations (such as taking the mean) are well defined. Points in
that space, which are averaged over modalities available at inference time, can
then be further processed to yield the desired segmentation. As such, any
combinatorial subset of available modalities can be provided as input, without
having to learn a combinatorial number of imputation models. Evaluated on two
neurological MRI datasets (brain tumors and MS lesions), the approach yields
state-of-the-art segmentation results when provided with all modalities;
moreover, its performance degrades remarkably gracefully when modalities are
removed, significantly more so than alternative mean-filling or other synthesis
approaches.Comment: Accepted as an oral presentation at MICCAI 201
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
Automatic Segmentation of Muscle Tissue and Inter-muscular Fat in Thigh and Calf MRI Images
Magnetic resonance imaging (MRI) of thigh and calf muscles is one of the most
effective techniques for estimating fat infiltration into muscular dystrophies.
The infiltration of adipose tissue into the diseased muscle region varies in
its severity across, and within, patients. In order to efficiently quantify the
infiltration of fat, accurate segmentation of muscle and fat is needed. An
estimation of the amount of infiltrated fat is typically done visually by
experts. Several algorithmic solutions have been proposed for automatic
segmentation. While these methods may work well in mild cases, they struggle in
moderate and severe cases due to the high variability in the intensity of
infiltration, and the tissue's heterogeneous nature. To address these
challenges, we propose a deep-learning approach, producing robust results with
high Dice Similarity Coefficient (DSC) of 0.964, 0.917 and 0.933 for
muscle-region, healthy muscle and inter-muscular adipose tissue (IMAT)
segmentation, respectively.Comment: 9 pages, 4 figures, 2 tables, MICCAI 2019, the 22nd International
Conference on Medical Image Computing and Computer Assisted Interventio
Modelling the Distribution of 3D Brain MRI using a 2D Slice VAE
Probabilistic modelling has been an essential tool in medical image analysis,
especially for analyzing brain Magnetic Resonance Images (MRI). Recent deep
learning techniques for estimating high-dimensional distributions, in
particular Variational Autoencoders (VAEs), opened up new avenues for
probabilistic modeling. Modelling of volumetric data has remained a challenge,
however, because constraints on available computation and training data make it
difficult effectively leverage VAEs, which are well-developed for 2D images. We
propose a method to model 3D MR brain volumes distribution by combining a 2D
slice VAE with a Gaussian model that captures the relationships between slices.
We do so by estimating the sample mean and covariance in the latent space of
the 2D model over the slice direction. This combined model lets us sample new
coherent stacks of latent variables to decode into slices of a volume. We also
introduce a novel evaluation method for generated volumes that quantifies how
well their segmentations match those of true brain anatomy. We demonstrate that
our proposed model is competitive in generating high quality volumes at high
resolutions according to both traditional metrics and our proposed evaluation.Comment: accepted for publication at MICCAI 2020. Code available
https://github.com/voanna/slices-to-3d-brain-vae
Surface agnostic metrics for cortical volume segmentation and regression
The cerebral cortex performs higher-order brain functions and is thus implicated in a range of cognitive disorders. Current analysis of cortical variation is typically performed by fitting surface mesh models to inner and outer cortical boundaries and investigating metrics such as surface area and cortical curvature or thickness. These, however, take a long time to run, and are sensitive to motion and image and surface resolution, which can prohibit their use in clinical settings. In this paper, we instead propose a machine learning solution, training a novel architecture to predict cortical thickness and curvature metrics from T2 MRI images, while additionally returning metrics of prediction uncertainty. Our proposed model is tested on a clinical cohort (Down Syndrome) for which surface-based modelling often fails. Results suggest that deep convolutional neural networks are a viable option to predict cortical metrics across a range of brain development stages and pathologies
Brain Tumor Segmentation from Multi-Spectral MR Image Data Using Random Forest Classifier
The development of brain tumor segmentation techniques based on multi-spectral MR image data has relevant impact on the clinical practice via better diagnosis, radiotherapy planning and follow-up studies. This task is also very challenging due to the great variety of tumor appearances, the presence of several noise effects, and the differences in scanner sensitivity. This paper proposes an automatic procedure trained to distinguish gliomas from normal brain tissues in multi-spectral
MRI data. The procedure is based on a random forest (RF) classifier, which uses 80 computed features beside the four observed ones, including morphological ones, gradients, and Gabor wavelet features. The intermediary segmentation outcome provided by the RF is fed to a twofold post-processing, which regularizes the shape of detected tumors and enhances the segmentation accuracy. The performance of the procedure was evaluated using the 274 records of the BraTS 2015 train data set. The
achieved overall Dice scores between 85-86% represent highly accurate segmentation
Automatic Tissue Segmentation with Deep Learning in Patients with Congenital or Acquired Distortion of Brain Anatomy
Brains with complex distortion of cerebral anatomy present several challenges
to automatic tissue segmentation methods of T1-weighted MR images. First, the
very high variability in the morphology of the tissues can be incompatible with
the prior knowledge embedded within the algorithms. Second, the availability of
MR images of distorted brains is very scarce, so the methods in the literature
have not addressed such cases so far. In this work, we present the first
evaluation of state-of-the-art automatic tissue segmentation pipelines on
T1-weighted images of brains with different severity of congenital or acquired
brain distortion. We compare traditional pipelines and a deep learning model,
i.e. a 3D U-Net trained on normal-appearing brains. Unsurprisingly, traditional
pipelines completely fail to segment the tissues with strong anatomical
distortion. Surprisingly, the 3D U-Net provides useful segmentations that can
be a valuable starting point for manual refinement by
experts/neuroradiologists
3D U-Net Based Brain Tumor Segmentation and Survival Days Prediction
Past few years have witnessed the prevalence of deep learning in many
application scenarios, among which is medical image processing. Diagnosis and
treatment of brain tumors requires an accurate and reliable segmentation of
brain tumors as a prerequisite. However, such work conventionally requires
brain surgeons significant amount of time. Computer vision techniques could
provide surgeons a relief from the tedious marking procedure. In this paper, a
3D U-net based deep learning model has been trained with the help of brain-wise
normalization and patching strategies for the brain tumor segmentation task in
the BraTS 2019 competition. Dice coefficients for enhancing tumor, tumor core,
and the whole tumor are 0.737, 0.807 and 0.894 respectively on the validation
dataset. These three values on the test dataset are 0.778, 0.798 and 0.852.
Furthermore, numerical features including ratio of tumor size to brain size and
the area of tumor surface as well as age of subjects are extracted from
predicted tumor labels and have been used for the overall survival days
prediction task. The accuracy could be 0.448 on the validation dataset, and
0.551 on the final test dataset.Comment: Third place award of the 2019 MICCAI BraTS challenge survival task
[BraTS 2019](https://www.med.upenn.edu/cbica/brats2019.html
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