1,074 research outputs found
Fabric Image Representation Encoding Networks for Large-scale 3D Medical Image Analysis
Deep neural networks are parameterised by weights that encode feature
representations, whose performance is dictated through generalisation by using
large-scale feature-rich datasets. The lack of large-scale labelled 3D medical
imaging datasets restrict constructing such generalised networks. In this work,
a novel 3D segmentation network, Fabric Image Representation Networks
(FIRENet), is proposed to extract and encode generalisable feature
representations from multiple medical image datasets in a large-scale manner.
FIRENet learns image specific feature representations by way of 3D fabric
network architecture that contains exponential number of sub-architectures to
handle various protocols and coverage of anatomical regions and structures. The
fabric network uses Atrous Spatial Pyramid Pooling (ASPP) extended to 3D to
extract local and image-level features at a fine selection of scales. The
fabric is constructed with weighted edges allowing the learnt features to
dynamically adapt to the training data at an architecture level. Conditional
padding modules, which are integrated into the network to reinsert voxels
discarded by feature pooling, allow the network to inherently process
different-size images at their original resolutions. FIRENet was trained for
feature learning via automated semantic segmentation of pelvic structures and
obtained a state-of-the-art median DSC score of 0.867. FIRENet was also
simultaneously trained on MR (Magnatic Resonance) images acquired from 3D
examinations of musculoskeletal elements in the (hip, knee, shoulder) joints
and a public OAI knee dataset to perform automated segmentation of bone across
anatomy. Transfer learning was used to show that the features learnt through
the pelvic segmentation helped achieve improved mean DSC scores of 0.962,
0.963, 0.945 and 0.986 for automated segmentation of bone across datasets.Comment: 12 pages, 10 figure
U-Net and its variants for medical image segmentation: theory and applications
U-net is an image segmentation technique developed primarily for medical
image analysis that can precisely segment images using a scarce amount of
training data. These traits provide U-net with a very high utility within the
medical imaging community and have resulted in extensive adoption of U-net as
the primary tool for segmentation tasks in medical imaging. The success of
U-net is evident in its widespread use in all major image modalities from CT
scans and MRI to X-rays and microscopy. Furthermore, while U-net is largely a
segmentation tool, there have been instances of the use of U-net in other
applications. As the potential of U-net is still increasing, in this review we
look at the various developments that have been made in the U-net architecture
and provide observations on recent trends. We examine the various innovations
that have been made in deep learning and discuss how these tools facilitate
U-net. Furthermore, we look at image modalities and application areas where
U-net has been applied.Comment: 42 pages, in IEEE Acces
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