Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Extracting 3D Vascular Structures from Microscopy Images using
  Convolutional Recurrent Networks   Vasculature is known to be of key biological significance, especially in the
study of cancer. As such, considerable effort has been focused on the automated
measurement and analysis of vasculature in medical and pre-clinical images. In
tumors in particular, the vascular networks may be extremely irregular and the
appearance of the individual vessels may not conform to classical descriptions
of vascular appearance. Typically, vessels are extracted by either a
segmentation and thinning pipeline, or by direct tracking. Neither of these
methods are well suited to microscopy images of tumor vasculature. In order to
address this we propose a method to directly extract a medial representation of
the vessels using Convolutional Neural Networks. We then show that these
two-dimensional centerlines can be meaningfully extended into 3D in anisotropic
and complex microscopy images using the recently popularized Convolutional Long
Short-Term Memory units (ConvLSTM). We demonstrate the effectiveness of this
hybrid convolutional-recurrent architecture over both 2D and 3D convolutional
comparators.

11
The categories of this paper are: hep-lat
31
The categories of this paper are: ao-sci
17
The categories of this paper are: q-fin
20
The categories of this paper are: acc-phys
6
The categories of this paper are: astro-ph
29
The categories of this paper are: funct-an
35
The categories of this paper are: mtrl-th
27
The categories of this paper are: comp-gas
26
The categories of this paper are: bayes-an
28
The categories of this paper are: alg-geom
10
The categories of this paper are: q-bio
37
The categories of this paper are: supr-con
13
The categories of this paper are: quant-ph
16
The categories of this paper are: stat
5
The categories of this paper are: gr-qc
23
The categories of this paper are: solv-int