Evidence map›Paper›PMID 35578386›Full record

ArticleJournal of biomedical optics2022

Unsupervised super-resolution reconstruction of hyperspectral histology images for whole-slide imaging.

Ling Ma, Armand Rathgeb, Hasan Mubarak, Minh Tran, Baowei Fei

Erratum issuedAbstract read
In one paragraph

Article in Journal of biomedical optics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 22 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
22citing papers in PubMed, 1 pooled it
–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

22 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Article
  3. Article
  4. Article
  5. Review
  6. Review
  7. Article
  8. Optimization of Transfer Learning of Foundation Models for Hyperspectral Histologic Imaging.Proceedings of SPIE--the International Society for Optical Engineering · 2025
    Article
  9. A Dual-Camera High-Resolution Hyperspectral Imaging System for the Retina.Proceedings of SPIE--the International Society for Optical Engineering · 2025
    Article
  10. An automatic processing framework for hyperspectral histologic images and benchmark dataset.Proceedings of SPIE--the International Society for Optical Engineering · 2025
    Article
  11. Article
  12. Article
  13. Article
  14. Article
  15. Nerve Detection and Visualization Using Hyperspectral Imaging for Surgical Guidance.Proceedings of SPIE--the International Society for Optical Engineering · 2024
    Article
  16. A hyperspectral surgical microscope with super-resolution reconstruction for intraoperative image guidance.Proceedings of SPIE--the International Society for Optical Engineering · 2024
    Article
  17. Article
  18. Extended Depth of Field Imaging for Mosaic Hyperspectral Images.Proceedings of SPIE--the International Society for Optical Engineering · 2023
    Article
  19. A Video Transformer Network for Thyroid Cancer Detection on Hyperspectral Histologic Images.Proceedings of SPIE--the International Society for Optical Engineering · 2023
    Article
  20. Article
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

5 authors.

Ling MaUniversity of Texas at Dallas, Department of Bioengineering, Richardson, Texas, United States.
Armand RathgebUniversity of Texas at Dallas, Department of Bioengineering, Richardson, Texas, United States.
Hasan MubarakUniversity of Texas at Dallas, Department of Bioengineering, Richardson, Texas, United States.
Minh TranUniversity of Texas at Dallas, Department of Bioengineering, Richardson, Texas, United States.
Baowei FeiUniversity of Texas at Dallas, Department of Bioengineering, Richardson, Texas, United States.

Funding

Image-guided Intravascular Robotic System for Mitral Valve Repair and ImplantsR01HL140325 · NHLBI · GEORGIA INSTITUTE OF TECHNOLOGY · PI DESAI, JAYDEV P., FEI, BAOWEI · 2018 to 2021
$3.0M
ACADEMIC-INDUSTRIAL PARTNERSHIP FOR TRANSLATION OF PET/TRUS GUIDED INTERVENTIONR01CA204254 · NCI · UNIVERSITY OF TEXAS DALLAS · PI FEI, BAOWEI · 2017 to 2021
$2.0M
MOLECULAR IMAGING DIRECTED, 3D ULTRASOUND-GUIDED, BIOPSY SYSTEMR01CA156775 · NCI · UNIVERSITY OF TEXAS DALLAS · PI FEI, BAOWEI · 2011 to 2016
$1.7M
STAN-CT: Standardization and Normalization of CT images for Lung Cancer PatientsR21CA231911 · NCI · UNIVERSITY OF KENTUCKY · PI CHEN, JIN, FEI, BAOWEI · 2019 to 2020
$389k
NCI NIH HHS R01 CA156775NCI NIH HHS R01 CA204254NCI NIH HHS R21 CA231911NHLBI NIH HHS R01 HL140325
6 · The paper itself

Abstract

significanceHyperspectral imaging (HSI) provides rich spectral information for improved histopathological cancer detection. However, acquiring high-resolution HSI data for whole-slide imaging (WSI) can be time-consuming and requires a huge amount of storage space.

aimWSI using a color camera can be achieved with fast speed, high image resolution, and excellent image quality due to the established techniques. We aim to develop an RGB-guided unsupervised hyperspectral super-resolution reconstruction method that is hypothesized to improve image quality while maintaining the spectral characteristics. APPROACH: High-resolution hyperspectral images of 32 histologic slides were obtained via automated WSI. High-resolution RGB histology images were registered to the hyperspectral images for RGB guidance. An unsupervised super-resolution network was trained to take the downsampled low-resolution hyperspectral patches (LR-HSI) and high-resolution RGB patches (HR-RGB) as inputs to reconstruct high-resolution hyperspectral patches (HR-HSI). Then, an Inception-based network was trained with the HR-RGB, original HR-HSI, and generated HR-HSI, respectively, for whole-slide histopathological cancer detection.

resultsOur super-resolution reconstruction network generated high-resolution hyperspectral images with well-maintained spectral characteristics and improved image quality. Image classification using the original hyperspectral data outperformed RGB because of the extra spectral information. The generated hyperspectral image patches further improved the results.

conclusionsThe proposed method potentially reduces image acquisition time, saves storage space without compromising image quality, and improves the image classification performance.

Indexed as

Hyperspectral ImagingNeoplasmsHistological TechniquesHumanshyperspectral imagingimage classificationRGB guidancesuper-resolution reconstructionunsupervised

Identifiers

PMID35578386
PMCPMC9110022

What OpenQuestion holds

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LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.