Comparative Analysis of Deep Learning Architectures for Macular Hole Segmentation in OCT Images: A Performance Evaluation of U-Net Variants
This study presents a comprehensive comparison of U-Net variants with different backbone architectures for Macular Hole (MH) segmentation in optical coherence tomography (OCT) images. We evaluated eleven architectures, including U-Net combined with InceptionNetV4, VGG16, VGG19, ResNet152, DenseNet12...
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MDPI AG
2025-02-01
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| Series: | Journal of Imaging |
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| author | H. M. S. S. Herath S. L. P. Yasakethu Nuwan Madusanka Myunggi Yi Byeong-Il Lee |
| author_facet | H. M. S. S. Herath S. L. P. Yasakethu Nuwan Madusanka Myunggi Yi Byeong-Il Lee |
| author_sort | H. M. S. S. Herath |
| collection | DOAJ |
| description | This study presents a comprehensive comparison of U-Net variants with different backbone architectures for Macular Hole (MH) segmentation in optical coherence tomography (OCT) images. We evaluated eleven architectures, including U-Net combined with InceptionNetV4, VGG16, VGG19, ResNet152, DenseNet121, EfficientNet-B7, MobileNetV2, Xception, and Transformer. Models were assessed using the Dice coefficient and HD95 metrics on the OIMHS dataset. While HD95 proved unreliable for small regions like MH, often returning ‘nan’ values, the Dice coefficient provided consistent performance evaluation. InceptionNetV4 + U-Net achieved the highest Dice coefficient (0.9672), demonstrating superior segmentation accuracy. Although considered state-of-the-art, Transformer + U-Net showed poor performance in MH and intraretinal cyst (IRC) segmentation. Analysis of computational resources revealed that MobileNetV2 + U-Net offered the most efficient performance with minimal parameters, while InceptionNetV4 + U-Net balanced accuracy with moderate computational demands. Our findings suggest that CNN-based backbones, particularly InceptionNetV4, are more effective than Transformer architectures for OCT image segmentation, with InceptionNetV4 + U-Net emerging as the most promising model for clinical applications. |
| format | Article |
| id | doaj-art-a59fcc005e8b4e31a90e51a09d8737bf |
| institution | DOAJ |
| issn | 2313-433X |
| language | English |
| publishDate | 2025-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Journal of Imaging |
| spelling | doaj-art-a59fcc005e8b4e31a90e51a09d8737bf2025-08-20T02:44:39ZengMDPI AGJournal of Imaging2313-433X2025-02-011125310.3390/jimaging11020053Comparative Analysis of Deep Learning Architectures for Macular Hole Segmentation in OCT Images: A Performance Evaluation of U-Net VariantsH. M. S. S. Herath0S. L. P. Yasakethu1Nuwan Madusanka2Myunggi Yi3Byeong-Il Lee4Department of Industry 4.0 Convergence Bionics Engineering, Pukyoung National University, Busan 48513, Republic of KoreaFaculty of Technology, Sri Lanka Technological Campus, Padukka 10500, Sri LankaDigital Healthcare Research Center, Pukyong National University, Busan 48513, Republic of KoreaDepartment of Industry 4.0 Convergence Bionics Engineering, Pukyoung National University, Busan 48513, Republic of KoreaDepartment of Industry 4.0 Convergence Bionics Engineering, Pukyoung National University, Busan 48513, Republic of KoreaThis study presents a comprehensive comparison of U-Net variants with different backbone architectures for Macular Hole (MH) segmentation in optical coherence tomography (OCT) images. We evaluated eleven architectures, including U-Net combined with InceptionNetV4, VGG16, VGG19, ResNet152, DenseNet121, EfficientNet-B7, MobileNetV2, Xception, and Transformer. Models were assessed using the Dice coefficient and HD95 metrics on the OIMHS dataset. While HD95 proved unreliable for small regions like MH, often returning ‘nan’ values, the Dice coefficient provided consistent performance evaluation. InceptionNetV4 + U-Net achieved the highest Dice coefficient (0.9672), demonstrating superior segmentation accuracy. Although considered state-of-the-art, Transformer + U-Net showed poor performance in MH and intraretinal cyst (IRC) segmentation. Analysis of computational resources revealed that MobileNetV2 + U-Net offered the most efficient performance with minimal parameters, while InceptionNetV4 + U-Net balanced accuracy with moderate computational demands. Our findings suggest that CNN-based backbones, particularly InceptionNetV4, are more effective than Transformer architectures for OCT image segmentation, with InceptionNetV4 + U-Net emerging as the most promising model for clinical applications.https://www.mdpi.com/2313-433X/11/2/53optical coherence tomographysegmentationconvolutional neural networksmacular hole |
| spellingShingle | H. M. S. S. Herath S. L. P. Yasakethu Nuwan Madusanka Myunggi Yi Byeong-Il Lee Comparative Analysis of Deep Learning Architectures for Macular Hole Segmentation in OCT Images: A Performance Evaluation of U-Net Variants Journal of Imaging optical coherence tomography segmentation convolutional neural networks macular hole |
| title | Comparative Analysis of Deep Learning Architectures for Macular Hole Segmentation in OCT Images: A Performance Evaluation of U-Net Variants |
| title_full | Comparative Analysis of Deep Learning Architectures for Macular Hole Segmentation in OCT Images: A Performance Evaluation of U-Net Variants |
| title_fullStr | Comparative Analysis of Deep Learning Architectures for Macular Hole Segmentation in OCT Images: A Performance Evaluation of U-Net Variants |
| title_full_unstemmed | Comparative Analysis of Deep Learning Architectures for Macular Hole Segmentation in OCT Images: A Performance Evaluation of U-Net Variants |
| title_short | Comparative Analysis of Deep Learning Architectures for Macular Hole Segmentation in OCT Images: A Performance Evaluation of U-Net Variants |
| title_sort | comparative analysis of deep learning architectures for macular hole segmentation in oct images a performance evaluation of u net variants |
| topic | optical coherence tomography segmentation convolutional neural networks macular hole |
| url | https://www.mdpi.com/2313-433X/11/2/53 |
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