RETINAL VESSEL SEGMENTATION IN MEDICAL IMAGES USING DEEP LEARNING

Date

2023-8

Author

Bilal, Hazrat

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In a variety of medical applications, such as diagnostics, surgery planning, and treatment plans, medical picture segmentation makes it easier to employ innovative computational methods. Particularly, the morphology of retinal vessels is intricately linked to various ophthalmic conditions, making their precise segmentation crucial for effective medical interventions. Even though deep learning approaches have significantly improved the segmentation of retinal vessels, some difficult problems still exist and demand additional study and development. Recently, there have been notable advancements in medical image segmentation techniques utilizing U-Net and KiU-Net, showcasing promising results. The U-Net architecture falls into the category of undercomplete autoencoders, which neglects the semantic characteristics of thin and low-contrast vessels. Conversely, the KiU-Net incorporates both undercomplete and overcomplete architectures, enabling more accurate segmentation of small structures and intricate edges compared to the U-Net. Nevertheless, this approach still lacks the desired level of accuracy and poses significant computational complexity. We introduce a novel Optimized KiU-Net model aimed at enhancing the precision of segmenting thin and low-contrast retinal blood vessels, while concurrently reducing the computational complexity. The proposed model was developed by improving the convolution channel selection and increasing the depth of the encoder. Our model achieves faster convergence by utilizing a smaller parameter count and employing feature map concatenation at the final layer instead of connecting them at each block. On the RITE dataset, we run tests to verify the viability of our proposed method. The outcomes show that our optimized KiU-Net outperforms other cutting-edge techniques. Best to our knowledge, the proposed model outperforms all existing methods used for retinal vessel segmentation, with an excellent F1 score of 79.80 and an IoU of 66.30. Additionally, the Optimized KiU-Net model’s performance was assessed in contrast to cutting-edge methods using the GlaS dataset. The proposed method outperformed these methods, attaining an F1 score of 82.21 and an IoU of 71.03.

Subject Keywords

Retinal Vessel Segmentation, Optimized KiU-Net, Deep learning, U-Net, Low Contrast

URI

https://hdl.handle.net/11511/105496

Collections

Northern Cyprus Campus, Thesis