Next Article in Journal
Improved YOLOv7-Based Algorithm for Detecting Foreign Objects on the Roof of a Subway Vehicle
Next Article in Special Issue
MAM-E: Mammographic Synthetic Image Generation with Diffusion Models
Previous Article in Journal
A Multiplier-Free Convolution Neural Network Hardware Accelerator for Real-Time Bearing Condition Detection of CNC Machinery
Previous Article in Special Issue
Convolutional Networks and Transformers for Mammography Classification: An Experimental Study
 
 
Article
Peer-Review Record

Inpainting Saturation Artifact in Anterior Segment Optical Coherence Tomography

Sensors 2023, 23(23), 9439; https://doi.org/10.3390/s23239439
by Jie Li, He Zhang *, Xiaoli Wang, Haoming Wang, Jingzi Hao and Guanhua Bai
Reviewer 1:
Reviewer 2:
Reviewer 3: Anonymous
Sensors 2023, 23(23), 9439; https://doi.org/10.3390/s23239439
Submission received: 24 September 2023 / Revised: 22 November 2023 / Accepted: 23 November 2023 / Published: 27 November 2023
(This article belongs to the Special Issue Image Analysis and Biomedical Sensors)

Round 1

Reviewer 1 Report

Comments and Suggestions for Authors

The authors presents a segmentation technique for saturation artifacts with tomographic images of Cornea. The work offers significance in easing the work of ophthalmologist. However, the work requires some improvements before it could be published.

1- Some explanation regarding white mask is required. Is it applied in spatial domain or the frequency. What is its structure?

2- The authors use a generative network to regenerate the image, how do they ascertain that this technique will not compromise other anomalies in the image that can important for reaching a prognosis by an ophthalmologist.

3- What is the difference between Dice Similarity Coefficient and F1-Score.

4-Since it is a retrieval problem the authors can consider incorporating Precision-Recall metrics and graphs for the proposed and existing methods. This will help the reader to readily understand the performance of these methods.

5- The authors should explain in conclusion or discussion the theoretical basis on which the proposed model is able to perform better.

6- Since the work involves image processing and deep learning the author should consider citing

"Khan, Yaser Daanial; Mahmood, M Khalid; Ahmad, Daud; Al-Zidi, Nasser M; ",Content-Based Image Retrieval Using Gamma Distribution and Mixture Model,Journal of Function Spaces,2022,,,2022,Hindawi
Yang, Y., Gao, D., Xie, X., Qin, J., Li, J., Lin, H., ... & Deng, K. (2022). DeepIDC: a prediction framework of injectable drug combination based on heterogeneous information and deep learning. Clinical Pharmacokinetics, 61(12), 1749-1759.

Author Response

Please see the attachment.

Author Response File: Author Response.pdf

Reviewer 2 Report

Comments and Suggestions for Authors

Please answer one question before my comments. What is the difference between the current work and "

A Structure-Consistency GAN for Unpaired AS-OCT Image Inpainting

"

Comments on the Quality of English Language

Moderate language issues were detected.

Author Response

please see attachment

Author Response File: Author Response.pdf

Reviewer 3 Report

Comments and Suggestions for Authors

In this manuscript the authors present an approach to correct specific saturation artifacts in AS-OCT images. Comments on the manuscript appear below

-          The main issue of the manuscript has to do with the novelty of the results. Althought the specific approach might be somehow novel, the comparison with other similar approaches seem to provide slight differences in the quality of the results. What is more, the images are extracted from a commercial OCT setup, so there are probably several image treatment algorithms already applied to the images, that could influence the results.

-          Due to the same reason, the applicability of the results could be limited, as there are already several approaches with similar results. This should be clearly described in the manuscript.

-          The particular parameters employed in the commercial OCT system should be described in detail.

-          The general structure of the manuscript is a bit confusing. The introduction lacks a description of the sections that follow. The discussion of the results is embedded in the results section, and some comparisons remain poorly explained.

-          Distances L1 and L2 are not clearly defined in the explanation of frequency loss.

-          What does the expression “…masks are wider…” mean in section 3.1?

-          The authors seem to train the network only with synthetic artifacts images, why did you not to add real artifacts images in this phase?

-          Figures 6, 7 and 8 are not discussed in detail.

-          The results on real saturation artifacts are not analyzed quantitatively.

-          A general language editing shoul be made, to correct mistakes such as:

o   Abstract, “… the central artifacts is commonly…”

o   Section 2, “…without saturated artifacts”

o  

-          Figure 1 is not mentioned in the text

Figure 2 is not clearly and deeply explained.

Comments on the Quality of English Language

A general review of English expressions must be made.

Author Response

please see attachment

Author Response File: Author Response.pdf

Round 2

Reviewer 2 Report

Comments and Suggestions for Authors

The authors addressed my concerns, the manuscript is good for publication.

Author Response

Dear Reviewer,
      Thank you for your encouragement. It is a great honor to obtain your recognition of this work.

Reviewer 3 Report

Comments and Suggestions for Authors

The revised version of the manuscript addresses most of the comments exposed in the previous review round.

Comments on the Quality of English Language

A slight review of English expressions must be made.

Author Response

Dear Reviewer,
      Thank you very much for your suggestion. We have checked and revised the English expression of the paper.

Back to TopTop