Automatic Segmentation of Macular Edema in Retinal OCT Images Using Improved U-Net++

Gao, Zhijun; Wang, Xingle; Li, Yang

doi:10.3390/app10165701

Cited by 10 publications

(3 citation statements)

References 26 publications

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“…U-Net++ with deep supervision was obtained and tested in the field of medical image segmentation, such as chest nodules and liver segmentation, and the segmentation performance was better than U-Net. Zhijun Gao et al (2020) [23] redesigned convolutional blocks based on U-Net++ and used Deep Residual Nets (ResNet) as the backbone to segment the retinal macular edema region. The round holes as well as the cracked voids located around the segmentation target are very similar to the wormholes and cracks in computed tomography (CT) images of wood.…”

Section: Introductionmentioning

confidence: 99%

An Algorithm Based on DAF-Net++ Model for Wood Annual Rings Segmentation

Zhuang

Zhang²,

Li-ming

et al. 2023

Electronics

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The semantic segmentation of annual rings is a research topic of interest in wood chronology. To solve the problem of wood annual rings being difficult to segment in dense areas and being greatly affected by defects such as cracks and wormholes, this paper builds a DAF-Net++ model which is based on U-Net whose backbone network is VGG16 and filled with dense jump links, CBAM and DCAM. In this model, VGG16 is used to enhance the extraction ability of image features, dense jump links are used to fuse semantic information of different levels, DCAM provides weighting guidance for shallow features, and CBAM solves the loss of down-sampling information. Taking a Chinese fir wood as the experimental object, 1700 CT images of wood transverse section were obtained by medical CT equipment and 120 of them were randomly selected as the dataset, which was expanded by cropping and rotation, among others. DAF-Net++ was used for training the model and segmentation of the annual rings, and finally the performance of the model was evaluated. The training method is freeze training followed by thaw training, and takes Focal Loss as the loss function, ReLU as the activation function, and Adam as the optimizer. The experimental results show that, in the segmentation of CT images of Chinese fir annual rings, the MIoU of DAF-Net++ is 93.67%, the MPA is 96.76%, the PA is 96.63%, and the Recall is 96.76%. Compared with other semantic segmentation models such as U-Net, U-Net++, DeepLabv3+, etc., DAF-Net++ has better segmentation performance.

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Section: Introductionmentioning

confidence: 99%

An Algorithm Based on DAF-Net++ Model for Wood Annual Rings Segmentation

Zhuang

Zhang²,

Li-ming

et al. 2023

Electronics

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“…In 2020, Liu et al proposed an enhanced nested U-Net structure using multi-scale input, multi-scale side output, and dual attention mechanism, which achieved excellent segmentation performance on multilayer segmentation and multi-fluid segmentation [19]. In the same year, Gao et al used ResNet as the backbone network in U-Net++ [20], redesigned the skip connection structure, used ResNeSt [21] to improve the synthesized structure, studied the edema region in OCT images, and obtained good segmentation results [22]. In the same year, Xie et al used image enhancement and improved 3D U-Net to implement a fast and automatic hyper-reflection focus segmentation method, and also obtained good segmentation results [23].…”

Section: Introductionmentioning

confidence: 99%

Research on Semantic Segmentation Method of Macular Edema in Retinal OCT Images Based on Improved Swin-Unet

Gao

Chen

2022

Electronics

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Optical coherence tomography (OCT), as a new type of tomography technology, has the characteristics of non-invasive, real-time imaging and high sensitivity, and is currently an important medical imaging tool to assist ophthalmologists in the screening, diagnosis, and follow-up treatment of patients with macular disease. In order to solve the problem of irregular occurrence area of diabetic retinopathy macular edema (DME), multi-scale and multi-region cluster of macular edema, which leads to inaccurate segmentation of the edema area, an improved Swin-Unet networks model was proposed for automatic semantic segmentation of macular edema lesion areas in OCT images. Firstly, in the deep bottleneck of the Swin-Unet network, the Resnet network layer was used to increase the extraction of pairs of sub-feature images. Secondly, the Swin Transformer block and skip connection structure were used for global and local learning, and the regions after semantic segmentation were morphologically smoothed and post-processed. Finally, the proposed method was performed on the macular edema patient dataset publicly available at Duke University, and was compared with previous segmentation methods. The experimental results show that the proposed method can not only improve the overall semantic segmentation accuracy of retinal macular edema, but also further to improve the semantic segmentation effect of multi-scale and multi-region edema regions.

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“…This method, which was based on attention mechanisms and a variant of Recurrent Neural Networks (RNNs), demonstrated potential application in the brain-computer interface (BCI) system based on visual motion perception. A new method of automatic segmentation of macular edema regions in retinal OCT images is proposed [20]. It is based on an improved version of UNet (U-Net++), which exploits the ResNet architecture as the backbone, with re-designed skip pathways and a dense convolution block.…”

mentioning

confidence: 99%