Evidence map›Paper›PMID 38067812›Full record

ArticleSensors (Basel, Switzerland)2023

Inpainting Saturation Artifact in Anterior Segment Optical Coherence Tomography.

Jie Li, He Zhang, Xiaoli Wang, Haoming Wang, Jingzi Hao, Guanhua Bai

Abstract read
In one paragraph

Article in Sensors (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
  2. Article
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

6 authors.

Jie LiElectronics Information Engineering College, Changchun Univesity, Changchun 130022, China.
He ZhangElectronics Information Engineering College, Changchun Univesity, Changchun 130022, China.
Xiaoli WangElectronics Information Engineering College, Changchun Univesity, Changchun 130022, China.
Haoming WangElectronics Information Engineering College, Changchun Univesity, Changchun 130022, China.
Jingzi HaoElectronics Information Engineering College, Changchun Univesity, Changchun 130022, China.
Guanhua BaiElectronics Information Engineering College, Changchun Univesity, Changchun 130022, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The cornea is an important refractive structure in the human eye. The corneal segmentation technique provides valuable information for clinical diagnoses, such as corneal thickness. Non-contact anterior segment optical coherence tomography (AS-OCT) is a prevalent ophthalmic imaging technique that can visualize the anterior and posterior surfaces of the cornea. Nonetheless, during the imaging process, saturation artifacts are commonly generated due to the tangent of the corneal surface at that point, which is normal to the incident light source. This stripe-shaped saturation artifact covers the corneal surface, causing blurring of the corneal edge, reducing the accuracy of corneal segmentation. To settle this matter, an inpainting method that introduces structural similarity and frequency loss is proposed to remove the saturation artifact in AS-OCT images. Specifically, the structural similarity loss reconstructs the corneal structure and restores corneal textural details. The frequency loss combines the spatial domain with the frequency domain to ensure the overall consistency of the image in both domains. Furthermore, the performance of the proposed method in corneal segmentation tasks is evaluated, and the results indicate a significant benefit for subsequent clinical analysis.

Indexed as

ArtifactsTomography, Optical CoherenceCorneaHumansRefraction, OcularAS-OCTcorneal segmentationinpaintingsaturation artifact

Identifiers

PMID38067812
PMCPMC10708580

What OpenQuestion holds

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Registered trials

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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.