Towards an extended EfficientNet-based U-Net framework for joint optic disc and cup segmentation in the fundus image
2023; Elsevier BV; Volume: 85; Linguagem: Inglês
10.1016/j.bspc.2023.104906
ISSN1746-8108
AutoresJinke Wang, Xiang Li, Cheng Yuanzhi,
Tópico(s)Digital Imaging for Blood Diseases
ResumoAutomatic and accurate segmentation of the optic disc (OD) and optic cup (OC) is crucial for clinical glaucoma screening. However, the segmentation accuracy cannot meet the requirements of clinical diagnosis yet. This paper aims to propose a novel joint segmentation framework to improve accuracy. We suggested an extended EfficientNet-based U-Net, named EE-UNet. First, we use the EfficientNet to extract distinguishable features under different scales of receptive fields. Second, we employ the Conditional Random Field as a Recurrent Neural Network (CRF-RNN) to extend the U-Net framework, maintaining an end-to-end mode. Third, the Ranger optimizer is suggested for faster and more stable convergence with minimal computational cost. Finally, we designed a multi-label loss function to balance the foreground and background pixels. Extensive experiments were trained and verified on the datasets REFUGE and GAMMA and tested on Drishti-GS1 and RIM-ONE-v3. The proposed EE-UNet achieved a high DICE score in OD/OC segmentation (0.9624/0.9228, respectively), outperforming the state-of-the-art (SOTA) methods. Quantitative and qualitative results verified the superior performance of the proposed method in OC and OD segmentation, thus proving that it can be a promising tool in the early screening of large-scale glaucoma.
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