571 research outputs found
Fully Automatic Segmentation of Lumbar Vertebrae from CT Images using Cascaded 3D Fully Convolutional Networks
We present a method to address the challenging problem of segmentation of
lumbar vertebrae from CT images acquired with varying fields of view. Our
method is based on cascaded 3D Fully Convolutional Networks (FCNs) consisting
of a localization FCN and a segmentation FCN. More specifically, in the first
step we train a regression 3D FCN (we call it "LocalizationNet") to find the
bounding box of the lumbar region. After that, a 3D U-net like FCN (we call it
"SegmentationNet") is then developed, which after training, can perform a
pixel-wise multi-class segmentation to map a cropped lumber region volumetric
data to its volume-wise labels. Evaluated on publicly available datasets, our
method achieved an average Dice coefficient of 95.77 0.81% and an average
symmetric surface distance of 0.37 0.06 mm.Comment: 5 pages and 5 figure
High-Dimensional Low-Rank Tensor Autoregressive Time Series Modeling
Modern technological advances have enabled an unprecedented amount of
structured data with complex temporal dependence, urging the need for new
methods to efficiently model and forecast high-dimensional tensor-valued time
series. This paper provides the first practical tool to accomplish this task
via autoregression (AR). By considering a low-rank Tucker decomposition for the
transition tensor, the proposed tensor autoregression can flexibly capture the
underlying low-dimensional tensor dynamics, providing both substantial
dimension reduction and meaningful dynamic factor interpretation. For this
model, we introduce both low-dimensional rank-constrained estimator and
high-dimensional regularized estimators, and derive their asymptotic and
non-asymptotic properties. In particular, by leveraging the special balanced
structure of the AR transition tensor, a novel convex regularization approach,
based on the sum of nuclear norms of square matricizations, is proposed to
efficiently encourage low-rankness of the coefficient tensor. A truncation
method is further introduced to consistently select the Tucker ranks.
Simulation experiments and real data analysis demonstrate the advantages of the
proposed approach over various competing ones.Comment: 61 pages, 6 figure
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