Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Artificial confocal microscopy for deep label-free imaging   Widefield microscopy methods applied to optically thick specimens are faced
with reduced contrast due to spatial crosstalk, in which the signal at each
point is the result of a superposition from neighboring points that are
simultaneously illuminated. In 1955, Marvin Minsky proposed confocal microscopy
as a solution to this problem. Today, laser scanning confocal fluorescence
microscopy is broadly used due to its high depth resolution and sensitivity,
which come at the price of photobleaching, chemical, and photo-toxicity. Here,
we present artificial confocal microscopy (ACM) to achieve confocal-level depth
sectioning, sensitivity, and chemical specificity, on unlabeled specimens,
nondestructively. We augmented a laser scanning confocal instrument with a
quantitative phase imaging module, which provides optical pathlength maps of
the specimen on the same field of view as the fluorescence channel. Using pairs
of phase and fluorescence images, we trained a convolution neural network to
translate the former into the latter. The training to infer a new tag is very
practical as the input and ground truth data are intrinsically registered and
the data acquisition is automated. Remarkably, the ACM images present
significantly stronger depth sectioning than the input images, enabling us to
recover confocal-like tomographic volumes of microspheres, hippocampal neurons
in culture, and 3D liver cancer spheroids. By training on nucleus-specific
tags, ACM allows for segmenting individual nuclei within dense spheroids for
both cell counting and volume measurements. Furthermore, taking the estimated
fluorescence volumes, as annotation for the phase data, we extracted dry mass
information for individual nuclei. Finally, our results indicate that the
network learning can be transferred between spheroids suspended in different
media.

27
The categories of this paper are: comp-gas
6
The categories of this paper are: astro-ph
37
The categories of this paper are: supr-con
36
The categories of this paper are: cmp-lg
21
The categories of this paper are: adap-org
12
The categories of this paper are: nucl-th
7
The categories of this paper are: hep-th
29
The categories of this paper are: funct-an
16
The categories of this paper are: stat
13
The categories of this paper are: quant-ph
11
The categories of this paper are: hep-lat
35
The categories of this paper are: mtrl-th
2
The categories of this paper are: math
26
The categories of this paper are: bayes-an
9
The categories of this paper are: nlin
30
The categories of this paper are: q-alg