25,078 research outputs found
Anaesthesia Fluid Detection in 3D Contrast Enhanced Ultrasound Image
Ultrasound medical image has disadvantage on displaying anaesthesia fluid due to its low intensity. Using contrast agent to enhance brightness of fluid area makes it possible to extract fluid area from acquired 3D image. This paper proposes an easy to implement approach to detect anaesthesia fluid. The approach will slice 3D image into arrays of 2D image, remove low intensities area from image, reconstruct fluid area to its original size, and combine 2D fluid area images into 3D visualization. The purpose of this paper is to help anaesthetist to confirm whether the operation is success and for further studying on how anaesthesia fluid spread
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
End-to-end detection-segmentation network with ROI convolution
We propose an end-to-end neural network that improves the segmentation
accuracy of fully convolutional networks by incorporating a localization unit.
This network performs object localization first, which is then used as a cue to
guide the training of the segmentation network. We test the proposed method on
a segmentation task of small objects on a clinical dataset of ultrasound
images. We show that by jointly learning for detection and segmentation, the
proposed network is able to improve the segmentation accuracy compared to only
learning for segmentation. Code is publicly available at
https://github.com/vincentzhang/roi-fcn.Comment: ISBI 201
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