Next Article in Journal
Orbital Angular Momentum Multiplexed Free-Space Optical Communication Systems Based on Coded Modulation
Next Article in Special Issue
Development of Low-Cost Fast Photoacoustic Computed Tomography: System Characterization and Phantom Study
Previous Article in Journal
Transbulbar B-Mode Sonography for Clinical Phenotyping Multiple Sclerosis
Previous Article in Special Issue
Biomedical Photoacoustic Imaging Optimization with Deconvolution and EMD Reconstruction
Article Menu
Issue 11 (November) cover image

Export Article

Open AccessArticle
Appl. Sci. 2018, 8(11), 2178;

Adipocyte Size Evaluation Based on Photoacoustic Spectral Analysis Combined with Deep Learning Method

Schoool of Electronic Science and Engineering, Nanjing University, Xianlin, Nanjing 210046, China
Institute of Acoustics, Tongji University, Shanghai 200092, China
Department of Biomedical Engineering, University of Michigan, Ann Arbor, MI 48109, USA
Department of Surgery, University of Michigan Medical School and Michigan Medicine, Ann Arbor, MI 48109, USA
Department of Pediatrics and Communicable Diseases, University of Michigan Medical School and Michigan Medicine, Ann Arbor, MI 48109, USA
Ann Arbor Veterans Administration Hospital, Ann Arbor, MI 48109, USA
Author to whom correspondence should be addressed.
Received: 10 October 2018 / Revised: 4 November 2018 / Accepted: 5 November 2018 / Published: 7 November 2018
(This article belongs to the Special Issue Photoacoustic Tomography (PAT))
PDF [1868 KB, uploaded 7 November 2018]


Adipocyte size, i.e., the cell area of adipose tissue, is correlated directly with metabolic disease risk in obese humans. This study proposes an approach of processing the photoacoustic (PA) signal power spectrum using a deep learning method to evaluate adipocyte size in human adipose tissue. This approach has the potential to provide noninvasive assessment of adipose tissue dysfunction, replacing traditional invasive methods of evaluating adipose tissue via biopsy and histopathology. A deep neural network with fully connected layers was used to fit the relationship between PA spectrum and average adipocyte size. Experiments on human adipose tissue specimens were performed, and the optimal parameters of the deep learning method were applied to establish the relationship between the PA spectrum and average adipocyte size. By studying different spectral bands in the entire spectral range using the deep network, a spectral band mostly sensitive to the adipocyte size was identified. A method of combining all frequency components of PA spectrum was tested to achieve a more accurate evaluation. View Full-Text
Keywords: photoacoustics; tissue characterization; absorption photoacoustics; tissue characterization; absorption

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

Share & Cite This Article

MDPI and ACS Style

Ma, X.; Cao, M.; Shen, Q.; Yuan, J.; Feng, T.; Cheng, Q.; Wang, X.; Washabaugh, A.R.; Baker, N.A.; Lumeng, C.N.; O’Rourke, R.W. Adipocyte Size Evaluation Based on Photoacoustic Spectral Analysis Combined with Deep Learning Method. Appl. Sci. 2018, 8, 2178.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics



[Return to top]
Appl. Sci. EISSN 2076-3417 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top