Evaluation of 2D and 3D Deep Learning Approaches For Predicting Visual Acuity Following Surgery for Idiopathic Full-Thickness Macular Holes In Spectral Domain Optical Coherence Tomography Images
In this work, we compared the performance of 2D and 3D versions of four state-of-the-art deep learning neural networks on predicting visual acuity following surgery for idiopathic full-thickness macular holes using an image dataset of spectral-domain optical coherence tomography (OCT) scans. To make...
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| Vydáno v: | 2023 International Symposium on Image and Signal Processing and Analysis (ISPA) s. 1 - 6 |
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| Jazyk: | angličtina |
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18.09.2023
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| ISSN: | 1849-2266 |
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| Abstract | In this work, we compared the performance of 2D and 3D versions of four state-of-the-art deep learning neural networks on predicting visual acuity following surgery for idiopathic full-thickness macular holes using an image dataset of spectral-domain optical coherence tomography (OCT) scans. To make this study more comparable, using the same dataset revealed the differences between 2D and 3D versions of deep learning neural networks. Based on our results, 3D networks generally outperformed the 2D networks in R-squared and Pearson correlation coefficient; however, they fell behind in mean absolute error. 3D networks also come with the sacrifice of significantly more computational complexity. |
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| AbstractList | In this work, we compared the performance of 2D and 3D versions of four state-of-the-art deep learning neural networks on predicting visual acuity following surgery for idiopathic full-thickness macular holes using an image dataset of spectral-domain optical coherence tomography (OCT) scans. To make this study more comparable, using the same dataset revealed the differences between 2D and 3D versions of deep learning neural networks. Based on our results, 3D networks generally outperformed the 2D networks in R-squared and Pearson correlation coefficient; however, they fell behind in mean absolute error. 3D networks also come with the sacrifice of significantly more computational complexity. |
| Author | Kucukgoz, Burak Yapici, M. Mutlu Obara, Boguslaw Steel, David H |
| Author_xml | – sequence: 1 givenname: Burak surname: Kucukgoz fullname: Kucukgoz, Burak email: b.kucukgoz2@newcastle.ac.uk organization: School of Computing, Newcastle University,Newcastle upon Tyne,UK – sequence: 2 givenname: M. Mutlu surname: Yapici fullname: Yapici, M. Mutlu email: mutluyapici@ankara.edu.tr organization: Elmadag Vocational School, Ankara University,Ankara,TURKEY – sequence: 3 givenname: David H surname: Steel fullname: Steel, David H email: david.steel@newcastle.ac.uk organization: Biosciences Institute, Newcastle University,Newcastle upon Tyne,UK – sequence: 4 givenname: Boguslaw surname: Obara fullname: Obara, Boguslaw email: boguslaw.obara@newcastle.ac.uk organization: School of Computing, Newcastle University,Newcastle upon Tyne,UK |
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| Snippet | In this work, we compared the performance of 2D and 3D versions of four state-of-the-art deep learning neural networks on predicting visual acuity following... |
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| SubjectTerms | 2D vs. 3D Image Analysis Correlation coefficient Deep learning Machine Learning Neural networks Optical coherence tomography Surgery Three-dimensional displays Visual Acuity Measurement Visualization |
| Title | Evaluation of 2D and 3D Deep Learning Approaches For Predicting Visual Acuity Following Surgery for Idiopathic Full-Thickness Macular Holes In Spectral Domain Optical Coherence Tomography Images |
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