49 research outputs found
Automatic calcium scoring in low-dose chest CT using deep neural networks with dilated convolutions
Heavy smokers undergoing screening with low-dose chest CT are affected by
cardiovascular disease as much as by lung cancer. Low-dose chest CT scans
acquired in screening enable quantification of atherosclerotic calcifications
and thus enable identification of subjects at increased cardiovascular risk.
This paper presents a method for automatic detection of coronary artery,
thoracic aorta and cardiac valve calcifications in low-dose chest CT using two
consecutive convolutional neural networks. The first network identifies and
labels potential calcifications according to their anatomical location and the
second network identifies true calcifications among the detected candidates.
This method was trained and evaluated on a set of 1744 CT scans from the
National Lung Screening Trial. To determine whether any reconstruction or only
images reconstructed with soft tissue filters can be used for calcification
detection, we evaluated the method on soft and medium/sharp filter
reconstructions separately. On soft filter reconstructions, the method achieved
F1 scores of 0.89, 0.89, 0.67, and 0.55 for coronary artery, thoracic aorta,
aortic valve and mitral valve calcifications, respectively. On sharp filter
reconstructions, the F1 scores were 0.84, 0.81, 0.64, and 0.66, respectively.
Linearly weighted kappa coefficients for risk category assignment based on per
subject coronary artery calcium were 0.91 and 0.90 for soft and sharp filter
reconstructions, respectively. These results demonstrate that the presented
method enables reliable automatic cardiovascular risk assessment in all
low-dose chest CT scans acquired for lung cancer screening
Deep learning analysis of the myocardium in coronary CT angiography for identification of patients with functionally significant coronary artery stenosis
In patients with coronary artery stenoses of intermediate severity, the
functional significance needs to be determined. Fractional flow reserve (FFR)
measurement, performed during invasive coronary angiography (ICA), is most
often used in clinical practice. To reduce the number of ICA procedures, we
present a method for automatic identification of patients with functionally
significant coronary artery stenoses, employing deep learning analysis of the
left ventricle (LV) myocardium in rest coronary CT angiography (CCTA). The
study includes consecutively acquired CCTA scans of 166 patients with FFR
measurements. To identify patients with a functionally significant coronary
artery stenosis, analysis is performed in several stages. First, the LV
myocardium is segmented using a multiscale convolutional neural network (CNN).
To characterize the segmented LV myocardium, it is subsequently encoded using
unsupervised convolutional autoencoder (CAE). Thereafter, patients are
classified according to the presence of functionally significant stenosis using
an SVM classifier based on the extracted and clustered encodings. Quantitative
evaluation of LV myocardium segmentation in 20 images resulted in an average
Dice coefficient of 0.91 and an average mean absolute distance between the
segmented and reference LV boundaries of 0.7 mm. Classification of patients was
evaluated in the remaining 126 CCTA scans in 50 10-fold cross-validation
experiments and resulted in an area under the receiver operating characteristic
curve of 0.74 +- 0.02. At sensitivity levels 0.60, 0.70 and 0.80, the
corresponding specificity was 0.77, 0.71 and 0.59, respectively. The results
demonstrate that automatic analysis of the LV myocardium in a single CCTA scan
acquired at rest, without assessment of the anatomy of the coronary arteries,
can be used to identify patients with functionally significant coronary artery
stenosis.Comment: This paper was submitted in April 2017 and accepted in November 2017
for publication in Medical Image Analysis. Please cite as: Zreik et al.,
Medical Image Analysis, 2018, vol. 44, pp. 72-8
CNN-based Lung CT Registration with Multiple Anatomical Constraints
Deep-learning-based registration methods emerged as a fast alternative to
conventional registration methods. However, these methods often still cannot
achieve the same performance as conventional registration methods because they
are either limited to small deformation or they fail to handle a superposition
of large and small deformations without producing implausible deformation
fields with foldings inside.
In this paper, we identify important strategies of conventional registration
methods for lung registration and successfully developed the deep-learning
counterpart. We employ a Gaussian-pyramid-based multilevel framework that can
solve the image registration optimization in a coarse-to-fine fashion.
Furthermore, we prevent foldings of the deformation field and restrict the
determinant of the Jacobian to physiologically meaningful values by combining a
volume change penalty with a curvature regularizer in the loss function.
Keypoint correspondences are integrated to focus on the alignment of smaller
structures.
We perform an extensive evaluation to assess the accuracy, the robustness,
the plausibility of the estimated deformation fields, and the transferability
of our registration approach. We show that it achieves state-of-the-art results
on the COPDGene dataset compared to conventional registration method with much
shorter execution time. In our experiments on the DIRLab exhale to inhale lung
registration, we demonstrate substantial improvements (TRE below mm) over
other deep learning methods. Our algorithm is publicly available at
https://grand-challenge.org/algorithms/deep-learning-based-ct-lung-registration/
Lumbar spine segmentation in MR images: a dataset and a public benchmark
This paper presents a large publicly available multi-center lumbar spine
magnetic resonance imaging (MRI) dataset with reference segmentations of
vertebrae, intervertebral discs (IVDs), and spinal canal. The dataset includes
447 sagittal T1 and T2 MRI series from 218 patients with a history of low back
pain. It was collected from four different hospitals and was divided into a
training (179 patients) and validation (39 patients) set. An iterative data
annotation approach was used by training a segmentation algorithm on a small
part of the dataset, enabling semi-automatic segmentation of the remaining
images. The algorithm provided an initial segmentation, which was subsequently
reviewed, manually corrected, and added to the training data. We provide
reference performance values for this baseline algorithm and nnU-Net, which
performed comparably. We set up a continuous segmentation challenge to allow
for a fair comparison of different segmentation algorithms. This study may
encourage wider collaboration in the field of spine segmentation, and improve
the diagnostic value of lumbar spine MRI
Learn2Reg: comprehensive multi-task medical image registration challenge, dataset and evaluation in the era of deep learning
Image registration is a fundamental medical image analysis task, and a wide
variety of approaches have been proposed. However, only a few studies have
comprehensively compared medical image registration approaches on a wide range
of clinically relevant tasks. This limits the development of registration
methods, the adoption of research advances into practice, and a fair benchmark
across competing approaches. The Learn2Reg challenge addresses these
limitations by providing a multi-task medical image registration data set for
comprehensive characterisation of deformable registration algorithms. A
continuous evaluation will be possible at
https://learn2reg.grand-challenge.org. Learn2Reg covers a wide range of
anatomies (brain, abdomen, and thorax), modalities (ultrasound, CT, MR),
availability of annotations, as well as intra- and inter-patient registration
evaluation. We established an easily accessible framework for training and
validation of 3D registration methods, which enabled the compilation of results
of over 65 individual method submissions from more than 20 unique teams. We
used a complementary set of metrics, including robustness, accuracy,
plausibility, and runtime, enabling unique insight into the current
state-of-the-art of medical image registration. This paper describes datasets,
tasks, evaluation methods and results of the challenge, as well as results of
further analysis of transferability to new datasets, the importance of label
supervision, and resulting bias. While no single approach worked best across
all tasks, many methodological aspects could be identified that push the
performance of medical image registration to new state-of-the-art performance.
Furthermore, we demystified the common belief that conventional registration
methods have to be much slower than deep-learning-based methods