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Reduced volume of diabetic pancreatic islets in rodents detected by synchrotron X-ray phase-contrast microtomography and deep learning network

Guo, Qingqing LU ; AlKendi, Abdulla ; Jiang, Xiaoping LU ; Mittone, Alberto ; Wang, Linbo ; Larsson, Emanuel LU orcid ; Bravin, Alberto ; Renström, Erik LU ; Fang, Xianyong and Zhang, Enming LU (2023) In Heliyon 9(2).
Abstract

The pancreatic islet is a highly structured micro-organ that produces insulin in response to rising blood glucose. Here we develop a label-free and automatic imaging approach to visualize the islets in situ in diabetic rodents by the synchrotron radiation X-ray phase-contrast microtomography (SRμCT) at the ID17 station of the European Synchrotron Radiation Facility. The large-size images (3.2 mm × 15.97 mm) were acquired in the pancreas in STZ-treated mice and diabetic GK rats. Each pancreas was dissected by 3000 reconstructed images. The image datasets were further analysed by a self-developed deep learning method, AA-Net. All islets in the pancreas were segmented and visualized by the three-dimension (3D) reconstruction. After... (More)

The pancreatic islet is a highly structured micro-organ that produces insulin in response to rising blood glucose. Here we develop a label-free and automatic imaging approach to visualize the islets in situ in diabetic rodents by the synchrotron radiation X-ray phase-contrast microtomography (SRμCT) at the ID17 station of the European Synchrotron Radiation Facility. The large-size images (3.2 mm × 15.97 mm) were acquired in the pancreas in STZ-treated mice and diabetic GK rats. Each pancreas was dissected by 3000 reconstructed images. The image datasets were further analysed by a self-developed deep learning method, AA-Net. All islets in the pancreas were segmented and visualized by the three-dimension (3D) reconstruction. After quantifying the volumes of the islets, we found that the number of larger islets (=>1500 μm3) was reduced by 2-fold (wt 1004 ± 94 vs GK 419 ± 122, P < 0.001) in chronically developed diabetic GK rat, while in STZ-treated diabetic mouse the large islets were decreased by half (189 ± 33 vs 90 ± 29, P < 0.001) compared to the untreated mice. Our study provides a label-free tool for detecting and quantifying pancreatic islets in situ. It implies the possibility of monitoring the state of pancreatic islets in vivo diabetes without labelling.

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author
; ; ; ; ; ; ; ; and
organization
publishing date
type
Contribution to journal
publication status
published
subject
keywords
Deep learning, Diabetes, Pancreatic islets, Synchrotron radiation, X-ray microtomography, X-ray phase-contrast
in
Heliyon
volume
9
issue
2
article number
e13081
publisher
Elsevier
external identifiers
  • scopus:85147385232
  • pmid:36718155
ISSN
2405-8440
DOI
10.1016/j.heliyon.2023.e13081
language
English
LU publication?
yes
id
5edf415f-7dbc-45f5-be3a-e15c85a9bd55
date added to LUP
2023-02-21 15:37:15
date last changed
2025-10-18 16:15:15
@article{5edf415f-7dbc-45f5-be3a-e15c85a9bd55,
  abstract     = {{<p>The pancreatic islet is a highly structured micro-organ that produces insulin in response to rising blood glucose. Here we develop a label-free and automatic imaging approach to visualize the islets in situ in diabetic rodents by the synchrotron radiation X-ray phase-contrast microtomography (SRμCT) at the ID17 station of the European Synchrotron Radiation Facility. The large-size images (3.2 mm × 15.97 mm) were acquired in the pancreas in STZ-treated mice and diabetic GK rats. Each pancreas was dissected by 3000 reconstructed images. The image datasets were further analysed by a self-developed deep learning method, AA-Net. All islets in the pancreas were segmented and visualized by the three-dimension (3D) reconstruction. After quantifying the volumes of the islets, we found that the number of larger islets (=&gt;1500 μm<sup>3</sup>) was reduced by 2-fold (wt 1004 ± 94 vs GK 419 ± 122, P &lt; 0.001) in chronically developed diabetic GK rat, while in STZ-treated diabetic mouse the large islets were decreased by half (189 ± 33 vs 90 ± 29, P &lt; 0.001) compared to the untreated mice. Our study provides a label-free tool for detecting and quantifying pancreatic islets in situ. It implies the possibility of monitoring the state of pancreatic islets in vivo diabetes without labelling.</p>}},
  author       = {{Guo, Qingqing and AlKendi, Abdulla and Jiang, Xiaoping and Mittone, Alberto and Wang, Linbo and Larsson, Emanuel and Bravin, Alberto and Renström, Erik and Fang, Xianyong and Zhang, Enming}},
  issn         = {{2405-8440}},
  keywords     = {{Deep learning; Diabetes; Pancreatic islets; Synchrotron radiation; X-ray microtomography; X-ray phase-contrast}},
  language     = {{eng}},
  number       = {{2}},
  publisher    = {{Elsevier}},
  series       = {{Heliyon}},
  title        = {{Reduced volume of diabetic pancreatic islets in rodents detected by synchrotron X-ray phase-contrast microtomography and deep learning network}},
  url          = {{http://dx.doi.org/10.1016/j.heliyon.2023.e13081}},
  doi          = {{10.1016/j.heliyon.2023.e13081}},
  volume       = {{9}},
  year         = {{2023}},
}