Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: ASIST: Annotation-free Synthetic Instance Segmentation and Tracking by
  Adversarial Simulations   Background: The quantitative analysis of microscope videos often requires
instance segmentation and tracking of cellular and subcellular objects. The
traditional method consists of two stages: (1) performing instance object
segmentation of each frame, and (2) associating objects frame-by-frame.
Recently, pixel-embedding-based deep learning approaches these two steps
simultaneously as a single stage holistic solution. In computer vision,
annotated training data with consistent segmentation and tracking is resource
intensive, the severity of which is multiplied in microscopy imaging due to (1)
dense objects (e.g., overlapping or touching), and (2) high dynamics (e.g.,
irregular motion and mitosis). Adversarial simulations have provided successful
solutions to alleviate the lack of such annotations in dynamics scenes in
computer vision, such as using simulated environments (e.g., computer games) to
train real-world self-driving systems. Methods: In this paper, we propose an
annotation-free synthetic instance segmentation and tracking (ASIST) method
with adversarial simulation and single-stage pixel-embedding based learning.
Contribution: The contribution of this paper is three-fold: (1) the proposed
method aggregates adversarial simulations and single-stage pixel-embedding
based deep learning; (2) the method is assessed with both the cellular (i.e.,
HeLa cells) and subcellular (i.e., microvilli) objects; and (3) to the best of
our knowledge, this is the first study to explore annotation-free instance
segmentation and tracking study for microscope videos. Results: The ASIST
method achieved an important step forward, when compared with fully supervised
approaches: ASIST shows 7% to 11% higher segmentation, detection and tracking
performance on microvilli relative to fully supervised methods, and comparable
performance on Hela cell videos.

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The categories of this paper are: plasm-ph
22
The categories of this paper are: chao-dyn
7
The categories of this paper are: hep-th
21
The categories of this paper are: adap-org
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The categories of this paper are: quant-ph
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The categories of this paper are: solv-int
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The categories of this paper are: hep-lat
9
The categories of this paper are: nlin
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The categories of this paper are: acc-phys
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The categories of this paper are: comp-gas
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The categories of this paper are: math-ph
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The categories of this paper are: cmp-lg
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The categories of this paper are: bayes-an
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The categories of this paper are: nucl-th
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The categories of this paper are: stat
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The categories of this paper are: alg-geom