112 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
Airway Label Prediction in Video Bronchoscopy: Capturing Temporal Dependencies Utilizing Anatomical Knowledge
Purpose: Navigation guidance is a key requirement for a multitude of lung
interventions using video bronchoscopy. State-of-the-art solutions focus on
lung biopsies using electromagnetic tracking and intraoperative image
registration w.r.t. preoperative CT scans for guidance. The requirement of
patient-specific CT scans hampers the utilisation of navigation guidance for
other applications such as intensive care units.
Methods: This paper addresses navigation guidance solely incorporating
bronchosopy video data. In contrast to state-of-the-art approaches we entirely
omit the use of electromagnetic tracking and patient-specific CT scans.
Guidance is enabled by means of topological bronchoscope localization w.r.t. an
interpatient airway model. Particularly, we take maximally advantage of
anatomical constraints of airway trees being sequentially traversed. This is
realized by incorporating sequences of CNN-based airway likelihoods into a
Hidden Markov Model.
Results: Our approach is evaluated based on multiple experiments inside a
lung phantom model. With the consideration of temporal context and use of
anatomical knowledge for regularization, we are able to improve the accuracy up
to to 0.98 compared to 0.81 (weighted F1: 0.98 compared to 0.81) for a
classification based on individual frames.
Conclusion: We combine CNN-based single image classification of airway
segments with anatomical constraints and temporal HMM-based inference for the
first time. Our approach renders vision-only guidance for bronchoscopy
interventions in the absence of electromagnetic tracking and patient-specific
CT scans possible.Comment: Submitted to International Journal of Computer Assisted Radiology and
Surger
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