2 research outputs found
CathAI: Fully Automated Interpretation of Coronary Angiograms Using Neural Networks
Coronary heart disease (CHD) is the leading cause of adult death in the
United States and worldwide, and for which the coronary angiography procedure
is the primary gateway for diagnosis and clinical management decisions. The
standard-of-care for interpretation of coronary angiograms depends upon ad-hoc
visual assessment by the physician operator. However, ad-hoc visual
interpretation of angiograms is poorly reproducible, highly variable and bias
prone. Here we show for the first time that fully-automated angiogram
interpretation to estimate coronary artery stenosis is possible using a
sequence of deep neural network algorithms. The algorithmic pipeline we
developed--called CathAI--achieves state-of-the art performance across the
sequence of tasks required to accomplish automated interpretation of
unselected, real-world angiograms. CathAI (Algorithms 1-2) demonstrated
positive predictive value, sensitivity and F1 score of >=90% to identify the
projection angle overall and >=93% for left or right coronary artery angiogram
detection, the primary anatomic structures of interest. To predict obstructive
coronary artery stenosis (>=70% stenosis), CathAI (Algorithm 4) exhibited an
area under the receiver operating characteristic curve (AUC) of 0.862 (95% CI:
0.843-0.880). When externally validated in a healthcare system in another
country, CathAI AUC was 0.869 (95% CI: 0.830-0.907) to predict obstructive
coronary artery stenosis. Our results demonstrate that multiple purpose-built
neural networks can function in sequence to accomplish the complex series of
tasks required for automated analysis of real-world angiograms. Deployment of
CathAI may serve to increase standardization and reproducibility in coronary
stenosis assessment, while providing a robust foundation to accomplish future
tasks for algorithmic angiographic interpretation.Comment: 62 pages, 3 main figures, 2 main table
Weakly Supervised Vessel Segmentation in X-ray Angiograms by Self-Paced Learning from Noisy Labels with Suggestive Annotation
The segmentation of coronary arteries in X-ray angiograms by convolutional
neural networks (CNNs) is promising yet limited by the requirement of precisely
annotating all pixels in a large number of training images, which is extremely
labor-intensive especially for complex coronary trees. To alleviate the burden
on the annotator, we propose a novel weakly supervised training framework that
learns from noisy pseudo labels generated from automatic vessel enhancement,
rather than accurate labels obtained by fully manual annotation. A typical
self-paced learning scheme is used to make the training process robust against
label noise while challenged by the systematic biases in pseudo labels, thus
leading to the decreased performance of CNNs at test time. To solve this
problem, we propose an annotation-refining self-paced learning framework
(AR-SPL) to correct the potential errors using suggestive annotation. An
elaborate model-vesselness uncertainty estimation is also proposed to enable
the minimal annotation cost for suggestive annotation, based on not only the
CNNs in training but also the geometric features of coronary arteries derived
directly from raw data. Experiments show that our proposed framework achieves
1) comparable accuracy to fully supervised learning, which also significantly
outperforms other weakly supervised learning frameworks; 2) largely reduced
annotation cost, i.e., 75.18% of annotation time is saved, and only 3.46% of
image regions are required to be annotated; and 3) an efficient intervention
process, leading to superior performance with even fewer manual interactions