571 research outputs found
Human Treelike Tubular Structure Segmentation: A Comprehensive Review and Future Perspectives
Various structures in human physiology follow a treelike morphology, which
often expresses complexity at very fine scales. Examples of such structures are
intrathoracic airways, retinal blood vessels, and hepatic blood vessels. Large
collections of 2D and 3D images have been made available by medical imaging
modalities such as magnetic resonance imaging (MRI), computed tomography (CT),
Optical coherence tomography (OCT) and ultrasound in which the spatial
arrangement can be observed. Segmentation of these structures in medical
imaging is of great importance since the analysis of the structure provides
insights into disease diagnosis, treatment planning, and prognosis. Manually
labelling extensive data by radiologists is often time-consuming and
error-prone. As a result, automated or semi-automated computational models have
become a popular research field of medical imaging in the past two decades, and
many have been developed to date. In this survey, we aim to provide a
comprehensive review of currently publicly available datasets, segmentation
algorithms, and evaluation metrics. In addition, current challenges and future
research directions are discussed.Comment: 30 pages, 19 figures, submitted to CBM journa
Enhancing Super-Resolution Networks through Realistic Thick-Slice CT Simulation
This study aims to develop and evaluate an innovative simulation algorithm
for generating thick-slice CT images that closely resemble actual images in the
AAPM-Mayo's 2016 Low Dose CT Grand Challenge dataset. The proposed method was
evaluated using Peak Signal-to-Noise Ratio (PSNR) and Root Mean Square Error
(RMSE) metrics, with the hypothesis that our simulation would produce images
more congruent with their real counterparts. Our proposed method demonstrated
substantial enhancements in terms of both PSNR and RMSE over other simulation
methods. The highest PSNR values were obtained with the proposed method,
yielding 49.7369 2.5223 and 48.5801 7.3271 for D45 and B30
reconstruction kernels, respectively. The proposed method also registered the
lowest RMSE with values of 0.0068 0.0020 and 0.0108 0.0099 for D45
and B30, respectively, indicating a distribution more closely aligned with the
authentic thick-slice image. Further validation of the proposed simulation
algorithm was conducted using the TCIA LDCT-and-Projection-data dataset. The
generated images were then leveraged to train four distinct super-resolution
(SR) models, which were subsequently evaluated using the real thick-slice
images from the 2016 Low Dose CT Grand Challenge dataset. When trained with
data produced by our novel algorithm, all four SR models exhibited enhanced
performance.Comment: 11 pages, 4 figure
Machine learning reconstruction of depth-dependent thermal conductivity profile from frequency-domain thermoreflectance signals
Characterizing materials with spatially varying thermal conductivities is
significant to unveil the structure-property relationship for a wide range of
functional materials, such as chemical-vapor-deposited diamonds, ion-irradiated
materials, nuclear materials under radiation, and battery electrode materials.
Although the development of thermal conductivity microscopy based on
time/frequency-domain thermoreflectance (TDTR/FDTR) enabled in-plane scanning
of thermal conductivity profile, measuring depth-dependent thermal conductivity
remains challenging. This work proposed a machine-learning-based reconstruction
method for extracting depth-dependent thermal conductivity K(z) directly from
frequency-domain phase signals. We demonstrated that the simple
supervised-learning algorithm kernel ridge regression (KRR) can reconstruct
K(z) without requiring pre-knowledge about the functional form of the profile.
The reconstruction method can not only accurately reproduce typical K(z)
distributions such as the pre-assumed exponential profile of
chemical-vapor-deposited (CVD) diamonds and Gaussian profile of ion-irradiated
materials, but also complex profiles artificially constructed by superimposing
Gaussian, exponential, polynomial, and logarithmic functions. In addition, the
method also shows excellent performances of reconstructing K(z) of
ion-irradiated semiconductors from Fourier-transformed TDTR signals. This work
demonstrates that combining machine learning with pump-probe thermoreflectance
is an effective way for depth-dependent thermal property mapping
Tier Balancing: Towards Dynamic Fairness over Underlying Causal Factors
The pursuit of long-term fairness involves the interplay between
decision-making and the underlying data generating process. In this paper,
through causal modeling with a directed acyclic graph (DAG) on the
decision-distribution interplay, we investigate the possibility of achieving
long-term fairness from a dynamic perspective. We propose Tier Balancing, a
technically more challenging but more natural notion to achieve in the context
of long-term, dynamic fairness analysis. Different from previous fairness
notions that are defined purely on observed variables, our notion goes one step
further, capturing behind-the-scenes situation changes on the unobserved latent
causal factors that directly carry out the influence from the current decision
to the future data distribution. Under the specified dynamics, we prove that in
general one cannot achieve the long-term fairness goal only through one-step
interventions. Furthermore, in the effort of approaching long-term fairness, we
consider the mission of "getting closer to" the long-term fairness goal and
present possibility and impossibility results accordingly
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