92 research outputs found
Adversarial Sparse-View CBCT Artifact Reduction
We present an effective post-processing method to reduce the artifacts from
sparsely reconstructed cone-beam CT (CBCT) images. The proposed method is based
on the state-of-the-art, image-to-image generative models with a perceptual
loss as regulation. Unlike the traditional CT artifact-reduction approaches,
our method is trained in an adversarial fashion that yields more perceptually
realistic outputs while preserving the anatomical structures. To address the
streak artifacts that are inherently local and appear across various scales, we
further propose a novel discriminator architecture based on feature pyramid
networks and a differentially modulated focus map to induce the adversarial
training. Our experimental results show that the proposed method can greatly
correct the cone-beam artifacts from clinical CBCT images reconstructed using
1/3 projections, and outperforms strong baseline methods both quantitatively
and qualitatively
Dilated Convolutional Neural Networks for Cardiovascular MR Segmentation in Congenital Heart Disease
We propose an automatic method using dilated convolutional neural networks
(CNNs) for segmentation of the myocardium and blood pool in cardiovascular MR
(CMR) of patients with congenital heart disease (CHD).
Ten training and ten test CMR scans cropped to an ROI around the heart were
provided in the MICCAI 2016 HVSMR challenge. A dilated CNN with a receptive
field of 131x131 voxels was trained for myocardium and blood pool segmentation
in axial, sagittal and coronal image slices. Performance was evaluated within
the HVSMR challenge.
Automatic segmentation of the test scans resulted in Dice indices of
0.800.06 and 0.930.02, average distances to boundaries of
0.960.31 and 0.890.24 mm, and Hausdorff distances of 6.133.76
and 7.073.01 mm for the myocardium and blood pool, respectively.
Segmentation took 41.514.7 s per scan.
In conclusion, dilated CNNs trained on a small set of CMR images of CHD
patients showing large anatomical variability provide accurate myocardium and
blood pool segmentations
Adversarial training and dilated convolutions for brain MRI segmentation
Convolutional neural networks (CNNs) have been applied to various automatic
image segmentation tasks in medical image analysis, including brain MRI
segmentation. Generative adversarial networks have recently gained popularity
because of their power in generating images that are difficult to distinguish
from real images.
In this study we use an adversarial training approach to improve CNN-based
brain MRI segmentation. To this end, we include an additional loss function
that motivates the network to generate segmentations that are difficult to
distinguish from manual segmentations. During training, this loss function is
optimised together with the conventional average per-voxel cross entropy loss.
The results show improved segmentation performance using this adversarial
training procedure for segmentation of two different sets of images and using
two different network architectures, both visually and in terms of Dice
coefficients.Comment: MICCAI 2017 Workshop on Deep Learning in Medical Image Analysi
Unsupervised learning for cross-domain medical image synthesis using deformation invariant cycle consistency networks
Recently, the cycle-consistent generative adversarial networks (CycleGAN) has
been widely used for synthesis of multi-domain medical images. The
domain-specific nonlinear deformations captured by CycleGAN make the
synthesized images difficult to be used for some applications, for example,
generating pseudo-CT for PET-MR attenuation correction. This paper presents a
deformation-invariant CycleGAN (DicycleGAN) method using deformable
convolutional layers and new cycle-consistency losses. Its robustness dealing
with data that suffer from domain-specific nonlinear deformations has been
evaluated through comparison experiments performed on a multi-sequence brain MR
dataset and a multi-modality abdominal dataset. Our method has displayed its
ability to generate synthesized data that is aligned with the source while
maintaining a proper quality of signal compared to CycleGAN-generated data. The
proposed model also obtained comparable performance with CycleGAN when data
from the source and target domains are alignable through simple affine
transformations
Iterative Segmentation from Limited Training Data: Applications to Congenital Heart Disease
We propose a new iterative segmentation model which can be accurately learned
from a small dataset. A common approach is to train a model to directly segment
an image, requiring a large collection of manually annotated images to capture
the anatomical variability in a cohort. In contrast, we develop a segmentation
model that recursively evolves a segmentation in several steps, and implement
it as a recurrent neural network. We learn model parameters by optimizing the
interme- diate steps of the evolution in addition to the final segmentation. To
this end, we train our segmentation propagation model by presenting incom-
plete and/or inaccurate input segmentations paired with a recommended next
step. Our work aims to alleviate challenges in segmenting heart structures from
cardiac MRI for patients with congenital heart disease (CHD), which encompasses
a range of morphological deformations and topological changes. We demonstrate
the advantages of this approach on a dataset of 20 images from CHD patients,
learning a model that accurately segments individual heart chambers and great
vessels. Com- pared to direct segmentation, the iterative method yields more
accurate segmentation for patients with the most severe CHD malformations.Comment: Presented at the Deep Learning in Medical Image Analysis Workshop,
MICCAI 201
Neural Style Transfer Improves 3D Cardiovascular MR Image Segmentation on Inconsistent Data
Three-dimensional medical image segmentation is one of the most important
problems in medical image analysis and plays a key role in downstream diagnosis
and treatment. Recent years, deep neural networks have made groundbreaking
success in medical image segmentation problem. However, due to the high
variance in instrumental parameters, experimental protocols, and subject
appearances, the generalization of deep learning models is often hindered by
the inconsistency in medical images generated by different machines and
hospitals. In this work, we present StyleSegor, an efficient and easy-to-use
strategy to alleviate this inconsistency issue. Specifically, neural style
transfer algorithm is applied to unlabeled data in order to minimize the
differences in image properties including brightness, contrast, texture, etc.
between the labeled and unlabeled data. We also apply probabilistic adjustment
on the network output and integrate multiple predictions through ensemble
learning. On a publicly available whole heart segmentation benchmarking dataset
from MICCAI HVSMR 2016 challenge, we have demonstrated an elevated dice
accuracy surpassing current state-of-the-art method and notably, an improvement
of the total score by 29.91\%. StyleSegor is thus corroborated to be an
accurate tool for 3D whole heart segmentation especially on highly inconsistent
data, and is available at https://github.com/horsepurve/StyleSegor.Comment: 22nd International Conference on Medical Image Computing and Computer
Assisted Intervention (MICCAI 2019) early accep
RS-Net: Regression-Segmentation 3D CNN for Synthesis of Full Resolution Missing Brain MRI in the Presence of Tumours
Accurate synthesis of a full 3D MR image containing tumours from available
MRI (e.g. to replace an image that is currently unavailable or corrupted) would
provide a clinician as well as downstream inference methods with important
complementary information for disease analysis. In this paper, we present an
end-to-end 3D convolution neural network that takes a set of acquired MR image
sequences (e.g. T1, T2, T1ce) as input and concurrently performs (1) regression
of the missing full resolution 3D MRI (e.g. FLAIR) and (2) segmentation of the
tumour into subtypes (e.g. enhancement, core). The hypothesis is that this
would focus the network to perform accurate synthesis in the area of the
tumour. Experiments on the BraTS 2015 and 2017 datasets [1] show that: (1) the
proposed method gives better performance than state-of-the-art methods in terms
of established global evaluation metrics (e.g. PSNR), (2) replacing real MR
volumes with the synthesized MRI does not lead to significant degradation in
tumour and sub-structure segmentation accuracy. The system further provides
uncertainty estimates based on Monte Carlo (MC) dropout [11] for the
synthesized volume at each voxel, permitting quantification of the system's
confidence in the output at each location.Comment: Accepted at Workshop on Simulation and Synthesis in Medical Imaging -
SASHIMI2018 held in conjunction with the 21st International Conference on
Medical Image Computing and Computer Assisted Intervention (MICCAI 2018
Empirical Bayesian Mixture Models for Medical Image Translation
Automatically generating one medical imaging modality from another is known
as medical image translation, and has numerous interesting applications. This
paper presents an interpretable generative modelling approach to medical image
translation. By allowing a common model for group-wise normalisation and
segmentation of brain scans to handle missing data, the model allows for
predicting entirely missing modalities from one, or a few, MR contrasts.
Furthermore, the model can be trained on a fairly small number of subjects. The
proposed model is validated on three clinically relevant scenarios. Results
appear promising and show that a principled, probabilistic model of the
relationship between multi-channel signal intensities can be used to infer
missing modalities -- both MR contrasts and CT images.Comment: Accepted to the Simulation and Synthesis in Medical Imaging (SASHIMI)
workshop at MICCAI 201
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