74 research outputs found
Attention Gated Networks: Learning to Leverage Salient Regions in Medical Images
We propose a novel attention gate (AG) model for medical image analysis that
automatically learns to focus on target structures of varying shapes and sizes.
Models trained with AGs implicitly learn to suppress irrelevant regions in an
input image while highlighting salient features useful for a specific task.
This enables us to eliminate the necessity of using explicit external
tissue/organ localisation modules when using convolutional neural networks
(CNNs). AGs can be easily integrated into standard CNN models such as VGG or
U-Net architectures with minimal computational overhead while increasing the
model sensitivity and prediction accuracy. The proposed AG models are evaluated
on a variety of tasks, including medical image classification and segmentation.
For classification, we demonstrate the use case of AGs in scan plane detection
for fetal ultrasound screening. We show that the proposed attention mechanism
can provide efficient object localisation while improving the overall
prediction performance by reducing false positives. For segmentation, the
proposed architecture is evaluated on two large 3D CT abdominal datasets with
manual annotations for multiple organs. Experimental results show that AG
models consistently improve the prediction performance of the base
architectures across different datasets and training sizes while preserving
computational efficiency. Moreover, AGs guide the model activations to be
focused around salient regions, which provides better insights into how model
predictions are made. The source code for the proposed AG models is publicly
available.Comment: Accepted for Medical Image Analysis (Special Issue on Medical Imaging
with Deep Learning). arXiv admin note: substantial text overlap with
arXiv:1804.03999, arXiv:1804.0533
Fully Automated Segmentation of the Left Ventricle in Magnetic Resonance Images
Automatic and robust segmentation of the left ventricle (LV) in magnetic
resonance images (MRI) has remained challenging for many decades. With the
great success of deep learning in object detection and classification, the
research focus of LV segmentation has changed to convolutional neural network
(CNN) in recent years. However, LV segmentation is a pixel-level classification
problem and its categories are intractable compared to object detection and
classification. Although lots of CNN based methods have been proposed for LV
segmentation, no robust and reproducible results are achieved yet. In this
paper, we try to reproduce the CNN based LV segmentation methods with their
disclosed codes and trained CNN models. Not surprisingly, the reproduced
results are significantly worse than their claimed accuracies. We also proposed
a fully automated LV segmentation method based on slope difference distribution
(SDD) threshold selection to compare with the reproduced CNN methods. The
proposed method achieved 95.44% DICE score on the test set of automated cardiac
diagnosis challenge (ACDC) while the two compared CNN methods achieved 90.28%
and 87.13% DICE scores. Our achieved accuracy is also higher than the best
accuracy reported in the published literatures. The MATLAB codes of our
proposed method are freely available on line
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