18,572 research outputs found
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
An Unsupervised Learning Model for Deformable Medical Image Registration
We present a fast learning-based algorithm for deformable, pairwise 3D
medical image registration. Current registration methods optimize an objective
function independently for each pair of images, which can be time-consuming for
large data. We define registration as a parametric function, and optimize its
parameters given a set of images from a collection of interest. Given a new
pair of scans, we can quickly compute a registration field by directly
evaluating the function using the learned parameters. We model this function
using a convolutional neural network (CNN), and use a spatial transform layer
to reconstruct one image from another while imposing smoothness constraints on
the registration field. The proposed method does not require supervised
information such as ground truth registration fields or anatomical landmarks.
We demonstrate registration accuracy comparable to state-of-the-art 3D image
registration, while operating orders of magnitude faster in practice. Our
method promises to significantly speed up medical image analysis and processing
pipelines, while facilitating novel directions in learning-based registration
and its applications. Our code is available at
https://github.com/balakg/voxelmorph .Comment: 9 pages, in CVPR 201
A Survey on Deep Learning in Medical Image Analysis
Deep learning algorithms, in particular convolutional networks, have rapidly
become a methodology of choice for analyzing medical images. This paper reviews
the major deep learning concepts pertinent to medical image analysis and
summarizes over 300 contributions to the field, most of which appeared in the
last year. We survey the use of deep learning for image classification, object
detection, segmentation, registration, and other tasks and provide concise
overviews of studies per application area. Open challenges and directions for
future research are discussed.Comment: Revised survey includes expanded discussion section and reworked
introductory section on common deep architectures. Added missed papers from
before Feb 1st 201
MedGAN: Medical Image Translation using GANs
Image-to-image translation is considered a new frontier in the field of
medical image analysis, with numerous potential applications. However, a large
portion of recent approaches offers individualized solutions based on
specialized task-specific architectures or require refinement through
non-end-to-end training. In this paper, we propose a new framework, named
MedGAN, for medical image-to-image translation which operates on the image
level in an end-to-end manner. MedGAN builds upon recent advances in the field
of generative adversarial networks (GANs) by merging the adversarial framework
with a new combination of non-adversarial losses. We utilize a discriminator
network as a trainable feature extractor which penalizes the discrepancy
between the translated medical images and the desired modalities. Moreover,
style-transfer losses are utilized to match the textures and fine-structures of
the desired target images to the translated images. Additionally, we present a
new generator architecture, titled CasNet, which enhances the sharpness of the
translated medical outputs through progressive refinement via encoder-decoder
pairs. Without any application-specific modifications, we apply MedGAN on three
different tasks: PET-CT translation, correction of MR motion artefacts and PET
image denoising. Perceptual analysis by radiologists and quantitative
evaluations illustrate that the MedGAN outperforms other existing translation
approaches.Comment: 16 pages, 8 figure
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