4,242 research outputs found
UNet++: A Nested U-Net Architecture for Medical Image Segmentation
In this paper, we present UNet++, a new, more powerful architecture for
medical image segmentation. Our architecture is essentially a deeply-supervised
encoder-decoder network where the encoder and decoder sub-networks are
connected through a series of nested, dense skip pathways. The re-designed skip
pathways aim at reducing the semantic gap between the feature maps of the
encoder and decoder sub-networks. We argue that the optimizer would deal with
an easier learning task when the feature maps from the decoder and encoder
networks are semantically similar. We have evaluated UNet++ in comparison with
U-Net and wide U-Net architectures across multiple medical image segmentation
tasks: nodule segmentation in the low-dose CT scans of chest, nuclei
segmentation in the microscopy images, liver segmentation in abdominal CT
scans, and polyp segmentation in colonoscopy videos. Our experiments
demonstrate that UNet++ with deep supervision achieves an average IoU gain of
3.9 and 3.4 points over U-Net and wide U-Net, respectively.Comment: 8 pages, 3 figures, 3 tables, accepted by 4th Deep Learning in
Medical Image Analysis (DLMIA) Worksho
Segmentation of Intracranial Arterial Calcification with Deeply Supervised Residual Dropout Networks
Intracranial carotid artery calcification (ICAC) is a major risk factor for
stroke, and might contribute to dementia and cognitive decline. Reliance on
time-consuming manual annotation of ICAC hampers much demanded further research
into the relationship between ICAC and neurological diseases. Automation of
ICAC segmentation is therefore highly desirable, but difficult due to the
proximity of the lesions to bony structures with a similar attenuation
coefficient. In this paper, we propose a method for automatic segmentation of
ICAC; the first to our knowledge. Our method is based on a 3D fully
convolutional neural network that we extend with two regularization techniques.
Firstly, we use deep supervision (hidden layers supervision) to encourage
discriminative features in the hidden layers. Secondly, we augment the network
with skip connections, as in the recently developed ResNet, and dropout layers,
inserted in a way that skip connections circumvent them. We investigate the
effect of skip connections and dropout. In addition, we propose a simple
problem-specific modification of the network objective function that restricts
the focus to the most important image regions and simplifies the optimization.
We train and validate our model using 882 CT scans and test on 1,000. Our
regularization techniques and objective improve the average Dice score by 7.1%,
yielding an average Dice of 76.2% and 97.7% correlation between predicted ICAC
volumes and manual annotations.Comment: Accepted for MICCAI 201
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