72 research outputs found
Corticospinal Tract (CST) reconstruction based on fiber orientation distributions(FODs) tractography
The Corticospinal Tract (CST) is a part of pyramidal tract (PT), and it can
innervate the voluntary movement of skeletal muscle through spinal interneurons
(the 4th layer of the Rexed gray board layers), and anterior horn motorneurons
(which control trunk and proximal limb muscles). Spinal cord injury (SCI) is a
highly disabling disease often caused by traffic accidents. The recovery of CST
and the functional reconstruction of spinal anterior horn motor neurons play an
essential role in the treatment of SCI. However, the localization and
reconstruction of CST are still challenging issues; the accuracy of the
geometric reconstruction can directly affect the results of the surgery. The
main contribution of this paper is the reconstruction of the CST based on the
fiber orientation distributions (FODs) tractography. Differing from
tensor-based tractography in which the primary direction is a determined
orientation, the direction of FODs tractography is determined by the
probability. The spherical harmonics (SPHARM) can be used to approximate the
efficiency of FODs tractography. We manually delineate the three ROIs (the
posterior limb of the internal capsule, the cerebral peduncle, and the anterior
pontine area) by the ITK-SNAP software, and use the pipeline software to
reconstruct both the left and right sides of the CST fibers. Our results
demonstrate that FOD-based tractography can show more and correct anatomical
CST fiber bundles
Complex Image Generation SwinTransformer Network for Audio Denoising
Achieving high-performance audio denoising is still a challenging task in
real-world applications. Existing time-frequency methods often ignore the
quality of generated frequency domain images. This paper converts the audio
denoising problem into an image generation task. We first develop a complex
image generation SwinTransformer network to capture more information from the
complex Fourier domain. We then impose structure similarity and detailed loss
functions to generate high-quality images and develop an SDR loss to minimize
the difference between denoised and clean audios. Extensive experiments on two
benchmark datasets demonstrate that our proposed model is better than
state-of-the-art methods
Deep Feature Registration for Unsupervised Domain Adaptation
While unsupervised domain adaptation has been explored to leverage the
knowledge from a labeled source domain to an unlabeled target domain, existing
methods focus on the distribution alignment between two domains. However, how
to better align source and target features is not well addressed. In this
paper, we propose a deep feature registration (DFR) model to generate
registered features that maintain domain invariant features and simultaneously
minimize the domain-dissimilarity of registered features and target features
via histogram matching. We further employ a pseudo label refinement process,
which considers both probabilistic soft selection and center-based hard
selection to improve the quality of pseudo labels in the target domain.
Extensive experiments on multiple UDA benchmarks demonstrate the effectiveness
of our DFR model, resulting in new state-of-the-art performance
LaksNet: an end-to-end deep learning model for self-driving cars in Udacity simulator
The majority of road accidents occur because of human errors, including
distraction, recklessness, and drunken driving. One of the effective ways to
overcome this dangerous situation is by implementing self-driving technologies
in vehicles. In this paper, we focus on building an efficient deep-learning
model for self-driving cars. We propose a new and effective convolutional
neural network model called `LaksNet' consisting of four convolutional layers
and two fully connected layers. We conduct extensive experiments using our
LaksNet model with the training data generated from the Udacity simulator. Our
model outperforms many existing pre-trained ImageNet and NVIDIA models in terms
of the duration of the car for which it drives without going off the track on
the simulator
- …