Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: An Auxiliary Task for Learning Nuclei Segmentation in 3D Microscopy
  Images   Segmentation of cell nuclei in microscopy images is a prevalent necessity in
cell biology. Especially for three-dimensional datasets, manual segmentation is
prohibitively time-consuming, motivating the need for automated methods.
Learning-based methods trained on pixel-wise ground-truth segmentations have
been shown to yield state-of-the-art results on 2d benchmark image data of
nuclei, yet a respective benchmark is missing for 3d image data. In this work,
we perform a comparative evaluation of nuclei segmentation algorithms on a
database of manually segmented 3d light microscopy volumes. We propose a novel
learning strategy that boosts segmentation accuracy by means of a simple
auxiliary task, thereby robustly outperforming each of our baselines.
Furthermore, we show that one of our baselines, the popular three-label model,
when trained with our proposed auxiliary task, outperforms the recent
StarDist-3D. As an additional, practical contribution, we benchmark nuclei
segmentation against nuclei detection, i.e. the task of merely pinpointing
individual nuclei without generating respective pixel-accurate segmentations.
For learning nuclei detection, large 3d training datasets of manually annotated
nuclei center points are available. However, the impact on detection accuracy
caused by training on such sparse ground truth as opposed to dense pixel-wise
ground truth has not yet been quantified. To this end, we compare nuclei
detection accuracy yielded by training on dense vs. sparse ground truth. Our
results suggest that training on sparse ground truth yields competitive nuclei
detection rates.

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The categories of this paper are: nucl-th
21
The categories of this paper are: adap-org
10
The categories of this paper are: q-bio
33
The categories of this paper are: plasm-ph
18
The categories of this paper are: econ
2
The categories of this paper are: math
31
The categories of this paper are: ao-sci
26
The categories of this paper are: bayes-an
19
The categories of this paper are: eess
25
The categories of this paper are: patt-sol
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The categories of this paper are: mtrl-th
7
The categories of this paper are: hep-th
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The categories of this paper are: solv-int
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The categories of this paper are: acc-phys
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The categories of this paper are: hep-ex
30
The categories of this paper are: q-alg