Evidence map›Paper›PMID 34824975›Full record

ArticlePhotoacoustics2022

Full-view in vivo skin and blood vessels profile segmentation in photoacoustic imaging based on deep learning.

Cao Duong Ly, Van Tu Nguyen, Tan Hung Vo, Sudip Mondal, Sumin Park, Jaeyeop Choi, Thi Thu Ha Vu, Chang-Seok Kim, Junghwan Oh

Abstract read
In one paragraph

Article in Photoacoustics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.

0numbers the graph read from it
0cells of the map it votes in
15citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

15 citing papers in PubMed.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

9 authors.

Cao Duong LyIndustry 4.0 Convergence Bionics Engineering, Pukyong National University, Republic of Korea.
Van Tu NguyenIndustry 4.0 Convergence Bionics Engineering, Pukyong National University, Republic of Korea.
Tan Hung VoIndustry 4.0 Convergence Bionics Engineering, Pukyong National University, Republic of Korea.
Sudip MondalNew-senior Healthcare Innovation Center (BK21 Plus), Pukyong National University, Busan 48513, Republic of Korea.
Sumin ParkIndustry 4.0 Convergence Bionics Engineering, Pukyong National University, Republic of Korea.
Jaeyeop ChoiIndustry 4.0 Convergence Bionics Engineering, Pukyong National University, Republic of Korea.
Thi Thu Ha VuIndustry 4.0 Convergence Bionics Engineering, Pukyong National University, Republic of Korea.
Chang-Seok KimDepartment of Cogno-Mechatronics Engineering, Pusan National University, Busan 46241, Republic of Korea.
Junghwan OhIndustry 4.0 Convergence Bionics Engineering, Pukyong National University, Republic of Korea.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Photoacoustic (PA) microscopy allows imaging of the soft biological tissue based on optical absorption contrast and spatial ultrasound resolution. One of the major applications of PA imaging is its characterization of microvasculature. However, the strong PA signal from skin layer overshadowed the subcutaneous blood vessels leading to indirectly reconstruct the PA images in human study. Addressing the present situation, we examined a deep learning (DL) automatic algorithm to achieve high-resolution and high-contrast segmentation for widening PA imaging applications. In this research, we propose a DL model based on modified U-Net for extracting the relationship features between amplitudes of the generated PA signal from skin and underlying vessels. This study illustrates the broader potential of hybrid complex network as an automatic segmentation tool for the in vivo PA imaging. With DL-infused solution, our result outperforms the previous studies with achieved real-time semantic segmentation on large-size high-resolution PA images.

Indexed as

Deep learningHigh resolutionPhotoacoustic imagingSegmentationU-Net

Identifiers

PMID34824975
PMCPMC8603312

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.