37 research outputs found
Computationally efficient cardiac views projection using 3D Convolutional Neural Networks
4D Flow is an MRI sequence which allows acquisition of 3D images of the
heart. The data is typically acquired volumetrically, so it must be reformatted
to generate cardiac long axis and short axis views for diagnostic
interpretation. These views may be generated by placing 6 landmarks: the left
and right ventricle apex, and the aortic, mitral, pulmonary, and tricuspid
valves. In this paper, we propose an automatic method to localize landmarks in
order to compute the cardiac views. Our approach consists of first calculating
a bounding box that tightly crops the heart, followed by a landmark
localization step within this bounded region. Both steps are based on a 3D
extension of the recently introduced ENet. We demonstrate that the long and
short axis projections computed with our automated method are of equivalent
quality to projections created with landmarks placed by an experienced cardiac
radiologist, based on a blinded test administered to a different cardiac
radiologist
ScarGAN: Chained Generative Adversarial Networks to Simulate Pathological Tissue on Cardiovascular MR Scans
Medical images with specific pathologies are scarce, but a large amount of
data is usually required for a deep convolutional neural network (DCNN) to
achieve good accuracy. We consider the problem of segmenting the left
ventricular (LV) myocardium on late gadolinium enhancement (LGE) cardiovascular
magnetic resonance (CMR) scans of which only some of the scans have scar
tissue. We propose ScarGAN to simulate scar tissue on healthy myocardium using
chained generative adversarial networks (GAN). Our novel approach factorizes
the simulation process into 3 steps: 1) a mask generator to simulate the shape
of the scar tissue; 2) a domain-specific heuristic to produce the initial
simulated scar tissue from the simulated shape; 3) a refining generator to add
details to the simulated scar tissue. Unlike other approaches that generate
samples from scratch, we simulate scar tissue on normal scans resulting in
highly realistic samples. We show that experienced radiologists are unable to
distinguish between real and simulated scar tissue. Training a U-Net with
additional scans with scar tissue simulated by ScarGAN increases the percentage
of scar pixels correctly included in LV myocardium prediction from 75.9% to
80.5%.Comment: 12 pages, 5 figures. To appear in MICCAI DLMIA 201
Debris Disks in the Scorpius-Centaurus OB Association Resolved by ALMA
We present a CO(2-1) and 1240 um continuum survey of 23 debris disks with
spectral types B9-G1, observed at an angular resolution of 0.5-1 arcsec with
the Atacama Large Millimeter/Submillimeter Array (ALMA). The sample was
selected for large infrared excess and age ~10 Myr, to characterize the
prevalence of molecular gas emission in young debris disks. We identify three
CO-rich debris disks, plus two additional tentative (3-sigma) CO detections.
Twenty disks were detected in the continuum at the >3-sigma level. For the 12
disks in the sample that are spatially resolved by our observations, we perform
an independent analysis of the interferometric continuum visibilities to
constrain the basic dust disk geometry, as well as a simultaneous analysis of
the visibilities and broad-band spectral energy distribution to constrain the
characteristic grain size and disk mass. The gas-rich debris disks exhibit
preferentially larger outer radii in their dust disks, and a higher prevalence
of characteristic grain sizes smaller than the blowout size. The gas-rich disks
do not exhibit preferentially larger dust masses, contrary to expectations for
a scenario in which a higher cometary destruction rate would be expected to
result in a larger mass of both CO and dust. The three debris disks in our
sample with strong CO detections are all around A stars: the conditions in
disks around intermediate-mass stars appear to be the most conducive to the
survival or formation of CO.Comment: 16 pages, 6 figures, accepted for publication in Ap
Radial Surface Density Profiles of Gas and Dust in the Debris Disk around 49 Ceti
We present ~0.4 resolution images of CO(3-2) and associated continuum
emission from the gas-bearing debris disk around the nearby A star 49 Ceti,
observed with the Atacama Large Millimeter/Submillimeter Array (ALMA). We
analyze the ALMA visibilities in tandem with the broad-band spectral energy
distribution to measure the radial surface density profiles of dust and gas
emission from the system. The dust surface density decreases with radius
between ~100 and 310 au, with a marginally significant enhancement of surface
density at a radius of ~110 au. The SED requires an inner disk of small grains
in addition to the outer disk of larger grains resolved by ALMA. The gas disk
exhibits a surface density profile that increases with radius, contrary to most
previous spatially resolved observations of circumstellar gas disks. While ~80%
of the CO flux is well described by an axisymmetric power-law disk in Keplerian
rotation about the central star, residuals at ~20% of the peak flux exhibit a
departure from axisymmetry suggestive of spiral arms or a warp in the gas disk.
The radial extent of the gas disk (~220 au) is smaller than that of the dust
disk (~300 au), consistent with recent observations of other gas-bearing debris
disks. While there are so far only three broad debris disks with well
characterized radial dust profiles at millimeter wavelengths, 49 Ceti's disk
shows a markedly different structure from two radially resolved gas-poor debris
disks, implying that the physical processes generating and sculpting the gas
and dust are fundamentally different.Comment: 20 pages, 8 figures, accepted for publication in ApJ March 31, 2017
(submitted Nov 2016
Debris Disks in the Scorpius–Centaurus OB Association Resolved by ALMA
We present a CO(2-1) and 1240 μm continuum survey of 23 debris disks with spectral types B9-G1, observed at an angular resolution of 0.”5–1'' with the Atacama Large Millimeter/Submillimeter Array (ALMA). The sample was selected for large infrared excess and age ~10 Myr, to characterize the prevalence of molecular gas emission in young debris disks. We identify three CO-rich debris disks, plus two additional tentative (3σ) CO detections. Twenty disks were detected in the continuum at the >3σ level. For the 12 disks in the sample that are spatially resolved by our observations, we perform an independent analysis of the interferometric continuum visibilities to constrain the basic dust disk geometry, as well as a simultaneous analysis of the visibilities and broadband spectral energy distribution to constrain the characteristic grain size and disk mass. The gas-rich debris disks exhibit preferentially larger outer radii in their dust disks, and a higher prevalence of characteristic grain sizes smaller than the blowout size. The gas-rich disks do not exhibit preferentially larger dust masses, contrary to expectations for a scenario in which a higher cometary destruction rate would be expected to result in a larger mass of both CO and dust. The three debris disks in our sample with strong CO detections are all around A stars: the conditions in disks around intermediate-mass stars appear to be the most conducive to the survival or formation of CO
Automated cardiovascular magnetic resonance image analysis with fully convolutional networks
Background: Cardiovascular magnetic resonance (CMR) imaging is a standard imaging modality for assessing cardiovascular diseases (CVDs), the leading cause of death globally. CMR enables accurate quantification of the cardiac chamber volume, ejection fraction and myocardial mass, providing information for diagnosis and monitoring of CVDs. However, for years, clinicians have been relying on manual approaches for CMR image analysis, which is time consuming and prone to subjective errors. It is a major clinical challenge to automatically derive quantitative and clinically relevant information from CMR images.
Methods: Deep neural networks have shown a great potential in image pattern recognition and segmentation for a variety of tasks. Here we demonstrate an automated analysis method for CMR images, which is based on a fully convolutional network (FCN). The network is trained and evaluated on a large-scale dataset from the UK Biobank, consisting of 4,875 subjects with 93,500 pixelwise annotated images. The performance of the method has been evaluated using a number of technical metrics, including the Dice metric, mean contour distance and Hausdorff distance, as well as clinically relevant measures, including left ventricle (LV) end-diastolic volume (LVEDV) and end-systolic volume (LVESV), LV mass (LVM); right ventricle (RV) end-diastolic volume (RVEDV) and end-systolic volume (RVESV).
Results: By combining FCN with a large-scale annotated dataset, the proposed automated method achieves a high performance in segmenting the LV and RV on short-axis CMR images and the left atrium (LA) and right atrium (RA) on long-axis CMR images. On a short-axis image test set of 600 subjects, it achieves an average Dice metric of 0.94 for the LV cavity, 0.88 for the LV myocardium and 0.90 for the RV cavity. The mean absolute difference between automated measurement and manual measurement was 6.1 mL for LVEDV, 5.3 mL for LVESV, 6.9 gram for LVM, 8.5 mL for RVEDV and 7.2 mL for RVESV. On long-axis image test sets, the average Dice metric was 0.93 for the LA cavity (2-chamber view), 0.95 for the LA cavity (4-chamber view) and 0.96 for the RA cavity (4-chamber view). The performance is comparable to human inter-observer variability.
Conclusions: We show that an automated method achieves a performance on par with human experts in analysing CMR images and deriving clinically relevant measures
Debris Disks: Probing Planet Formation
Debris disks are the dust disks found around ~20% of nearby main sequence
stars in far-IR surveys. They can be considered as descendants of
protoplanetary disks or components of planetary systems, providing valuable
information on circumstellar disk evolution and the outcome of planet
formation. The debris disk population can be explained by the steady
collisional erosion of planetesimal belts; population models constrain where
(10-100au) and in what quantity (>1Mearth) planetesimals (>10km in size)
typically form in protoplanetary disks. Gas is now seen long into the debris
disk phase. Some of this is secondary implying planetesimals have a Solar
System comet-like composition, but some systems may retain primordial gas.
Ongoing planet formation processes are invoked for some debris disks, such as
the continued growth of dwarf planets in an unstirred disk, or the growth of
terrestrial planets through giant impacts. Planets imprint structure on debris
disks in many ways; images of gaps, clumps, warps, eccentricities and other
disk asymmetries, are readily explained by planets at >>5au. Hot dust in the
region planets are commonly found (<5au) is seen for a growing number of stars.
This dust usually originates in an outer belt (e.g., from exocomets), although
an asteroid belt or recent collision is sometimes inferred.Comment: Invited review, accepted for publication in the 'Handbook of
Exoplanets', eds. H.J. Deeg and J.A. Belmonte, Springer (2018
Multi-task learning for left atrial segmentation on GE-MRI
Segmentation of the left atrium (LA) is crucial for assessing its anatomy in both pre-operative atrial fibrillation (AF) ablation planning and post-operative follow-up studies. In this paper, we present a fully automated framework for left atrial segmentation in gadolinium-enhanced magnetic resonance images (GE-MRI) based on deep learning. We propose a fully convolutional neural network and explore the benefits of multi-task learning for performing both atrial segmentation and pre/post ablation classification. Our results show that, by sharing features between related tasks, the network can gain additional anatomical information and achieve more accurate atrial segmentation, leading to a mean Dice score of 0.901 on a test set of 20 3D MRI images. Code of our proposed algorithm is available at https://github.com/cherise215/atria_segmentation_2018/