19,317 research outputs found
Learning Deep Similarity Metric for 3D MR-TRUS Registration
Purpose: The fusion of transrectal ultrasound (TRUS) and magnetic resonance
(MR) images for guiding targeted prostate biopsy has significantly improved the
biopsy yield of aggressive cancers. A key component of MR-TRUS fusion is image
registration. However, it is very challenging to obtain a robust automatic
MR-TRUS registration due to the large appearance difference between the two
imaging modalities. The work presented in this paper aims to tackle this
problem by addressing two challenges: (i) the definition of a suitable
similarity metric and (ii) the determination of a suitable optimization
strategy.
Methods: This work proposes the use of a deep convolutional neural network to
learn a similarity metric for MR-TRUS registration. We also use a composite
optimization strategy that explores the solution space in order to search for a
suitable initialization for the second-order optimization of the learned
metric. Further, a multi-pass approach is used in order to smooth the metric
for optimization.
Results: The learned similarity metric outperforms the classical mutual
information and also the state-of-the-art MIND feature based methods. The
results indicate that the overall registration framework has a large capture
range. The proposed deep similarity metric based approach obtained a mean TRE
of 3.86mm (with an initial TRE of 16mm) for this challenging problem.
Conclusion: A similarity metric that is learned using a deep neural network
can be used to assess the quality of any given image registration and can be
used in conjunction with the aforementioned optimization framework to perform
automatic registration that is robust to poor initialization.Comment: To appear on IJCAR
Face Recognition from Sequential Sparse 3D Data via Deep Registration
Previous works have shown that face recognition with high accurate 3D data is
more reliable and insensitive to pose and illumination variations. Recently,
low-cost and portable 3D acquisition techniques like ToF(Time of Flight) and
DoE based structured light systems enable us to access 3D data easily, e.g.,
via a mobile phone. However, such devices only provide sparse(limited speckles
in structured light system) and noisy 3D data which can not support face
recognition directly. In this paper, we aim at achieving high-performance face
recognition for devices equipped with such modules which is very meaningful in
practice as such devices will be very popular. We propose a framework to
perform face recognition by fusing a sequence of low-quality 3D data. As 3D
data are sparse and noisy which can not be well handled by conventional methods
like the ICP algorithm, we design a PointNet-like Deep Registration
Network(DRNet) which works with ordered 3D point coordinates while preserving
the ability of mining local structures via convolution. Meanwhile we develop a
novel loss function to optimize our DRNet based on the quaternion expression
which obviously outperforms other widely used functions. For face recognition,
we design a deep convolutional network which takes the fused 3D depth-map as
input based on AMSoftmax model. Experiments show that our DRNet can achieve
rotation error 0.95{\deg} and translation error 0.28mm for registration. The
face recognition on fused data also achieves rank-1 accuracy 99.2% , FAR-0.001
97.5% on Bosphorus dataset which is comparable with state-of-the-art
high-quality data based recognition performance.Comment: To be appeared in ICB201
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