Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Unsupervised Microvascular Image Segmentation Using an Active Contours
  Mimicking Neural Network   The task of blood vessel segmentation in microscopy images is crucial for
many diagnostic and research applications. However, vessels can look vastly
different, depending on the transient imaging conditions, and collecting data
for supervised training is laborious. We present a novel deep learning method
for unsupervised segmentation of blood vessels. The method is inspired by the
field of active contours and we introduce a new loss term, which is based on
the morphological Active Contours Without Edges (ACWE) optimization method. The
role of the morphological operators is played by novel pooling layers that are
incorporated to the network's architecture. We demonstrate the challenges that
are faced by previous supervised learning solutions, when the imaging
conditions shift. Our unsupervised method is able to outperform such previous
methods in both the labeled dataset, and when applied to similar but different
datasets. Our code, as well as efficient PyTorch reimplementations of the
baseline methods VesselNN and DeepVess is available on GitHub -
https://github.com/shirgur/UMIS.

31
The categories of this paper are: ao-sci
13
The categories of this paper are: quant-ph
14
The categories of this paper are: math-ph
33
The categories of this paper are: plasm-ph
37
The categories of this paper are: supr-con
7
The categories of this paper are: hep-th
8
The categories of this paper are: hep-ex
34
The categories of this paper are: chem-ph
23
The categories of this paper are: solv-int
10
The categories of this paper are: q-bio
36
The categories of this paper are: cmp-lg
25
The categories of this paper are: patt-sol
11
The categories of this paper are: hep-lat
26
The categories of this paper are: bayes-an
22
The categories of this paper are: chao-dyn
18
The categories of this paper are: econ