Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: DeepVesselNet: Vessel Segmentation, Centerline Prediction, and
  Bifurcation Detection in 3-D Angiographic Volumes   We present DeepVesselNet, an architecture tailored to the challenges faced
when extracting vessel networks or trees and corresponding features in 3-D
angiographic volumes using deep learning. We discuss the problems of low
execution speed and high memory requirements associated with full 3-D
convolutional networks, high-class imbalance arising from the low percentage of
vessel voxels, and unavailability of accurately annotated training data - and
offer solutions as the building blocks of DeepVesselNet.
  First, we formulate 2-D orthogonal cross-hair filters which make use of 3-D
context information at a reduced computational burden. Second, we introduce a
class balancing cross-entropy loss function with false positive rate correction
to handle the high-class imbalance and high false positive rate problems
associated with existing loss functions. Finally, we generate synthetic dataset
using a computational angiogenesis model capable of generating vascular trees
under physiological constraints on local network structure and topology and use
these data for transfer learning.
  DeepVesselNet is optimized for segmenting and analyzing vessels, and we test
the performance on a range of angiographic volumes including clinical MRA data
of the human brain, as well as X-ray tomographic microscopy scans of the rat
brain. Our experiments show that, by replacing 3-D filters with cross-hair
filters in our network, we achieve over 23% improvement in speed, lower memory
footprint, lower network complexity which prevents overfitting and comparable
accuracy (with a Cox-Wilcoxon paired sample significance test p-value of 0.07
when compared to full 3-D filters). Our class balancing metric is crucial for
training the network and transfer learning with synthetic data is an efficient,
robust, and very generalizable approach leading to a network that excels in a
variety of angiography segmentation tasks.

17
The categories of this paper are: q-fin
33
The categories of this paper are: plasm-ph
8
The categories of this paper are: hep-ex
37
The categories of this paper are: supr-con
15
The categories of this paper are: nucl-ex
4
The categories of this paper are: cond-mat
29
The categories of this paper are: funct-an
32
The categories of this paper are: atom-ph
19
The categories of this paper are: eess
14
The categories of this paper are: math-ph
31
The categories of this paper are: ao-sci
3
The categories of this paper are: physics
7
The categories of this paper are: hep-th
18
The categories of this paper are: econ
9
The categories of this paper are: nlin
26
The categories of this paper are: bayes-an