114 research outputs found
Enhancing Hierarchical Transformers for Whole Brain Segmentation with Intracranial Measurements Integration
Whole brain segmentation with magnetic resonance imaging (MRI) enables the
non-invasive measurement of brain regions, including total intracranial volume
(TICV) and posterior fossa volume (PFV). Enhancing the existing whole brain
segmentation methodology to incorporate intracranial measurements offers a
heightened level of comprehensiveness in the analysis of brain structures.
Despite its potential, the task of generalizing deep learning techniques for
intracranial measurements faces data availability constraints due to limited
manually annotated atlases encompassing whole brain and TICV/PFV labels. In
this paper, we enhancing the hierarchical transformer UNesT for whole brain
segmentation to achieve segmenting whole brain with 133 classes and TICV/PFV
simultaneously. To address the problem of data scarcity, the model is first
pretrained on 4859 T1-weighted (T1w) 3D volumes sourced from 8 different sites.
These volumes are processed through a multi-atlas segmentation pipeline for
label generation, while TICV/PFV labels are unavailable. Subsequently, the
model is finetuned with 45 T1w 3D volumes from Open Access Series Imaging
Studies (OASIS) where both 133 whole brain classes and TICV/PFV labels are
available. We evaluate our method with Dice similarity coefficients(DSC). We
show that our model is able to conduct precise TICV/PFV estimation while
maintaining the 132 brain regions performance at a comparable level. Code and
trained model are available at: https://github.com/MASILab/UNesT/wholebrainSeg
Digital Modeling on Large Kernel Metamaterial Neural Network
Deep neural networks (DNNs) utilized recently are physically deployed with
computational units (e.g., CPUs and GPUs). Such a design might lead to a heavy
computational burden, significant latency, and intensive power consumption,
which are critical limitations in applications such as the Internet of Things
(IoT), edge computing, and the usage of drones. Recent advances in optical
computational units (e.g., metamaterial) have shed light on energy-free and
light-speed neural networks. However, the digital design of the metamaterial
neural network (MNN) is fundamentally limited by its physical limitations, such
as precision, noise, and bandwidth during fabrication. Moreover, the unique
advantages of MNN's (e.g., light-speed computation) are not fully explored via
standard 3x3 convolution kernels. In this paper, we propose a novel large
kernel metamaterial neural network (LMNN) that maximizes the digital capacity
of the state-of-the-art (SOTA) MNN with model re-parametrization and network
compression, while also considering the optical limitation explicitly. The new
digital learning scheme can maximize the learning capacity of MNN while
modeling the physical restrictions of meta-optic. With the proposed LMNN, the
computation cost of the convolutional front-end can be offloaded into
fabricated optical hardware. The experimental results on two publicly available
datasets demonstrate that the optimized hybrid design improved classification
accuracy while reducing computational latency. The development of the proposed
LMNN is a promising step towards the ultimate goal of energy-free and
light-speed AI
Multi-Contrast Computed Tomography Atlas of Healthy Pancreas
With the substantial diversity in population demographics, such as
differences in age and body composition, the volumetric morphology of pancreas
varies greatly, resulting in distinctive variations in shape and appearance.
Such variations increase the difficulty at generalizing population-wide
pancreas features. A volumetric spatial reference is needed to adapt the
morphological variability for organ-specific analysis. Here, we proposed a
high-resolution computed tomography (CT) atlas framework specifically optimized
for the pancreas organ across multi-contrast CT. We introduce a deep
learning-based pre-processing technique to extract the abdominal region of
interests (ROIs) and leverage a hierarchical registration pipeline to align the
pancreas anatomy across populations. Briefly, DEEDs affine and non-rigid
registration are performed to transfer patient abdominal volumes to a fixed
high-resolution atlas template. To generate and evaluate the pancreas atlas
template, multi-contrast modality CT scans of 443 subjects (without reported
history of pancreatic disease, age: 15-50 years old) are processed. Comparing
with different registration state-of-the-art tools, the combination of DEEDs
affine and non-rigid registration achieves the best performance for the
pancreas label transfer across all contrast phases. We further perform external
evaluation with another research cohort of 100 de-identified portal venous
scans with 13 organs labeled, having the best label transfer performance of
0.504 Dice score in unsupervised setting. The qualitative representation (e.g.,
average mapping) of each phase creates a clear boundary of pancreas and its
distinctive contrast appearance. The deformation surface renderings across
scales (e.g., small to large volume) further illustrate the generalizability of
the proposed atlas template
Scaling Up 3D Kernels with Bayesian Frequency Re-parameterization for Medical Image Segmentation
With the inspiration of vision transformers, the concept of depth-wise
convolution revisits to provide a large Effective Receptive Field (ERF) using
Large Kernel (LK) sizes for medical image segmentation. However, the
segmentation performance might be saturated and even degraded as the kernel
sizes scaled up (e.g., ) in a Convolutional Neural
Network (CNN). We hypothesize that convolution with LK sizes is limited to
maintain an optimal convergence for locality learning. While Structural
Re-parameterization (SR) enhances the local convergence with small kernels in
parallel, optimal small kernel branches may hinder the computational efficiency
for training. In this work, we propose RepUX-Net, a pure CNN architecture with
a simple large kernel block design, which competes favorably with current
network state-of-the-art (SOTA) (e.g., 3D UX-Net, SwinUNETR) using 6
challenging public datasets. We derive an equivalency between kernel
re-parameterization and the branch-wise variation in kernel convergence.
Inspired by the spatial frequency in the human visual system, we extend to vary
the kernel convergence into element-wise setting and model the spatial
frequency as a Bayesian prior to re-parameterize convolutional weights during
training. Specifically, a reciprocal function is leveraged to estimate a
frequency-weighted value, which rescales the corresponding kernel element for
stochastic gradient descent. From the experimental results, RepUX-Net
consistently outperforms 3D SOTA benchmarks with internal validation (FLARE:
0.929 to 0.944), external validation (MSD: 0.901 to 0.932, KiTS: 0.815 to
0.847, LiTS: 0.933 to 0.949, TCIA: 0.736 to 0.779) and transfer learning (AMOS:
0.880 to 0.911) scenarios in Dice Score.Comment: Both codes and pretrained models are available at:
https://github.com/MASILab/RepUX-Ne
Deep conditional generative models for longitudinal single-slice abdominal computed tomography harmonization
Two-dimensional single-slice abdominal computed tomography (CT) provides a
detailed tissue map with high resolution allowing quantitative characterization
of relationships between health conditions and aging. However, longitudinal
analysis of body composition changes using these scans is difficult due to
positional variation between slices acquired in different years, which leading
to different organs/tissues captured. To address this issue, we propose
C-SliceGen, which takes an arbitrary axial slice in the abdominal region as a
condition and generates a pre-defined vertebral level slice by estimating
structural changes in the latent space. Our experiments on 2608 volumetric CT
data from two in-house datasets and 50 subjects from the 2015 Multi-Atlas
Abdomen Labeling Challenge dataset (BTCV) Challenge demonstrate that our model
can generate high-quality images that are realistic and similar. We further
evaluate our method's capability to harmonize longitudinal positional variation
on 1033 subjects from the Baltimore Longitudinal Study of Aging (BLSA) dataset,
which contains longitudinal single abdominal slices, and confirmed that our
method can harmonize the slice positional variance in terms of visceral fat
area. This approach provides a promising direction for mapping slices from
different vertebral levels to a target slice and reducing positional variance
for single-slice longitudinal analysis. The source code is available at:
https://github.com/MASILab/C-SliceGen
Improved dielectric performance of barium strontium titanate multilayered capacitor by means of pulsed laser deposition and slow injection sol-gel methods
A Pt/BST/NiFe/Cu multilayered capacitor was fabricated incorporating a polycrystalline Ba0.5Sr0.5TiO3 (BST) film deposited using the pulsed laser deposition technique. Qualitative X-ray diffraction analysis confirmed a perovskite structure for the deposited BST dielectric films which were fired at various temperatures. No intermediate phase was discernable with a post-annealing temperature of 750°C and highly crystallized thin film was obtained at a post-annealing temperature of 800°C. The fabricated capacitor with a BST film thickness of 665 nm exhibited respectable electrical performance with a dielectric constant, k of 657 and a dielectric loss, tan δ = 0.0137 at room temperature at an applied frequency of 1 MHz. The recorded charge storage density and leakage current density were 4.6 μC cm-2 and 33 nA cm-2, respectively, with ±5 V bias
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