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...

Celý popis

Uloženo v:
Podrobná bibliografie
Vydáno v:2023 International Symposium on Image and Signal Processing and Analysis (ISPA) s. 1 - 6
Hlavní autoři: Kucukgoz, Burak, Yapici, M. Mutlu, Steel, David H, Obara, Boguslaw
Médium: Konferenční příspěvek
Jazyk:angličtina
Vydáno: IEEE 18.09.2023
Témata:
2D vs. 3D Image Analysis
Correlation coefficient
Deep learning
Machine Learning
Neural networks
Optical coherence tomography
Surgery
Three-dimensional displays
Visual Acuity Measurement
Visualization
ISSN:1849-2266
On-line přístup:Získat plný text
Tagy: Přidat tag
Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!
Popis
Shrnutí: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.
ISSN:1849-2266
DOI:10.1109/ISPA58351.2023.10279422