OCT layer segmentation using U-NET semantic segmentation and RESNET34 encoder-decoder
Images using OCT (Optical Coherence Tomography) for producing cross-sections of images of retina light-sensitive tissue linings behind the human eye's black portions. Segmentation provides a better understanding of the retinal anatomy, which is essential in planning and evaluating treatment opt...
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| Format: | Article |
| Language: | English |
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Elsevier
2023-10-01
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| Series: | Measurement: Sensors |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2665917423001538 |
| _version_ | 1827815759172075520 |
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| author | K. Yojana L. Thillai Rani |
| author_facet | K. Yojana L. Thillai Rani |
| author_sort | K. Yojana |
| collection | DOAJ |
| description | Images using OCT (Optical Coherence Tomography) for producing cross-sections of images of retina light-sensitive tissue linings behind the human eye's black portions. Segmentation provides a better understanding of the retinal anatomy, which is essential in planning and evaluating treatment options. By measuring retinal thickness and analyzing the arrangement of retinal tissue. A semantic segmentation model based on U-Net deep learning model is proposed. It uses ResNet34 architecture as an encoder and decoder for efficient feature extraction from the input images. A complete convolution encoder-decoder network called U-Net has skip links between the blocks that deal with symmetric encoding and decoding. The proposed framework can segment the layers in the OCT scan accurately.As the light returns to the scanner from the retina, it provides fine-grained cross-sectional photographs of the retina.The effectiveness of the suggested strategy was evaluated at two benchmarks: DeepLabV3Plus and UnetP++.Accuracy and mean Intersection over Union (mIoU) were two of the typical pixel-wise measures that were taken into consideration while evaluating the models' effectiveness. The future enhancement of this work (1) Add other performance evaluation metrics like transmission time, precision, and recall, (2) Deep learning approach use optimization algorithms to improve accuracy value. |
| first_indexed | 2024-03-12T00:05:13Z |
| format | Article |
| id | doaj.art-7515edfc64354ae1a7fe847f0d936ec2 |
| institution | Directory Open Access Journal |
| issn | 2665-9174 |
| language | English |
| last_indexed | 2024-03-12T00:05:13Z |
| publishDate | 2023-10-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Measurement: Sensors |
| spelling | doaj.art-7515edfc64354ae1a7fe847f0d936ec22023-09-17T04:57:22ZengElsevierMeasurement: Sensors2665-91742023-10-0129100817OCT layer segmentation using U-NET semantic segmentation and RESNET34 encoder-decoderK. Yojana0L. Thillai Rani1Corresponding author.; Department of Electronics and Instrumentation Engineering, Annamalai University, Chidambaram, Tamilnadu, IndiaDepartment of Electronics and Instrumentation Engineering, Annamalai University, Chidambaram, Tamilnadu, IndiaImages using OCT (Optical Coherence Tomography) for producing cross-sections of images of retina light-sensitive tissue linings behind the human eye's black portions. Segmentation provides a better understanding of the retinal anatomy, which is essential in planning and evaluating treatment options. By measuring retinal thickness and analyzing the arrangement of retinal tissue. A semantic segmentation model based on U-Net deep learning model is proposed. It uses ResNet34 architecture as an encoder and decoder for efficient feature extraction from the input images. A complete convolution encoder-decoder network called U-Net has skip links between the blocks that deal with symmetric encoding and decoding. The proposed framework can segment the layers in the OCT scan accurately.As the light returns to the scanner from the retina, it provides fine-grained cross-sectional photographs of the retina.The effectiveness of the suggested strategy was evaluated at two benchmarks: DeepLabV3Plus and UnetP++.Accuracy and mean Intersection over Union (mIoU) were two of the typical pixel-wise measures that were taken into consideration while evaluating the models' effectiveness. The future enhancement of this work (1) Add other performance evaluation metrics like transmission time, precision, and recall, (2) Deep learning approach use optimization algorithms to improve accuracy value.http://www.sciencedirect.com/science/article/pii/S2665917423001538Optical coherence tomographySemantic segmentationU-NetResNet34EncoderDecoder |
| spellingShingle | K. Yojana L. Thillai Rani OCT layer segmentation using U-NET semantic segmentation and RESNET34 encoder-decoder Measurement: Sensors Optical coherence tomography Semantic segmentation U-Net ResNet34 Encoder Decoder |
| title | OCT layer segmentation using U-NET semantic segmentation and RESNET34 encoder-decoder |
| title_full | OCT layer segmentation using U-NET semantic segmentation and RESNET34 encoder-decoder |
| title_fullStr | OCT layer segmentation using U-NET semantic segmentation and RESNET34 encoder-decoder |
| title_full_unstemmed | OCT layer segmentation using U-NET semantic segmentation and RESNET34 encoder-decoder |
| title_short | OCT layer segmentation using U-NET semantic segmentation and RESNET34 encoder-decoder |
| title_sort | oct layer segmentation using u net semantic segmentation and resnet34 encoder decoder |
| topic | Optical coherence tomography Semantic segmentation U-Net ResNet34 Encoder Decoder |
| url | http://www.sciencedirect.com/science/article/pii/S2665917423001538 |
| work_keys_str_mv | AT kyojana octlayersegmentationusingunetsemanticsegmentationandresnet34encoderdecoder AT lthillairani octlayersegmentationusingunetsemanticsegmentationandresnet34encoderdecoder |