Unsupervised Domain Adaptation Based Image Synthesis and Feature Alignment for Joint Optic Disc and Cup Segmentation

Lei, Haijun; Liu, Weixin; Xie, Hai; Zhao, Benjian; Yue, Guanghui; Lei, Baiying

doi:10.1109/jbhi.2021.3085770

Cited by 52 publications

(21 citation statements)

References 47 publications

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“…In addition, considering the contradiction between the model and data, on the one hand, global scientific research institutions can be called on to open source relevant data in accordance with relevant regulations. On the other hand, we can try to use domain adaptation [86], [87] to better transfer the model trained on the normal medical segmentation dataset to the COVID-19 infection segmentation, which technically makes up for the lack of training data.…”

Section: Discussionmentioning

confidence: 99%

Boundary Guided Semantic Learning for Real-Time COVID-19 Lung Infection Segmentation System

Cong

Zhang

Yang

et al. 2022

IEEE Trans. Consumer Electron.

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The coronavirus disease 2019 (COVID-19) continues to have a negative impact on healthcare systems around the world, though the vaccines have been developed and national vaccination coverage rate is steadily increasing. At the current stage, automatically segmenting the lung infection area from CT images is essential for the diagnosis and treatment of COVID-19. Thanks to the development of deep learning technology, some deep learning solutions for lung infection segmentation have been proposed. However, due to the scattered distribution, complex background interference and blurred boundaries, the accuracy and completeness of the existing models are still unsatisfactory. To this end, we propose a boundary guided semantic learning network (BSNet) in this paper. On the one hand, the dualbranch semantic enhancement module that combines the toplevel semantic preservation and progressive semantic integration is designed to model the complementary relationship between different high-level features, thereby promoting the generation of more complete segmentation results. On the other hand, the mirror-symmetric boundary guidance module is proposed to accurately detect the boundaries of the lesion regions in a mirrorsymmetric way. Experiments on the publicly available dataset demonstrate that our BSNet outperforms the existing state-ofthe-art competitors and achieves a real-time inference speed of 44 FPS. The code and results of our BSNet can be found from the link of https://github.com/rmcong/BSNet.

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Section: Discussionmentioning

confidence: 99%

Boundary Guided Semantic Learning for Real-Time COVID-19 Lung Infection Segmentation System

Cong

Zhang

Yang

et al. 2022

IEEE Trans. Consumer Electron.

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“…In the future, we would like to extend our work to several other segmentation tasks in Retinopathy for instance exudates and lesion segmentation in fundus images. We also plan to incorporate adversarial learning similar to [30] to address the unavailability of large-sized datasets in the domain. Fuzzy fusion [42] of multiple encoded features can be an option to improve the representational ability of the segmentation frameworks.…”

Section: Discussionmentioning

confidence: 99%

RecU-Net++: Improved Utilization of Receptive Fields in U-Net++ for Skin Lesion Segmentation

Hussain

Basak

2021

2021 IEEE 18th India Council International Conference (INDICON)

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Glaucoma is a chronic visual disease that may cause permanent irreversible blindness. Measurement of the cup-to-disc ratio (CDR) plays a pivotal role in the detection of glaucoma in its early stage, preventing visual disparities. Therefore, accurate and automatic segmentation of optic disc (OD) and optic cup (OC) from retinal fundus images is a fundamental requirement. Existing CNNbased segmentation frameworks resort to building deep encoders with aggressive downsampling layers, which suffer from a general limitation on modeling explicit long-range dependency. To this end, in this paper, we propose a new segmentation pipeline, called UT-Net, availing the advantages of UNet and transformer both in its encoding layer, followed by an attention-gated bilinear fusion scheme. In addition to this, we incorporate Multi-Head Contextual attention to enhance the regular self-attention used in traditional vision transformers. Thus low-level features along with global dependencies are captured in a shallow manner. Besides, we extract context information at multiple encoding layers for better exploration of receptive fields, and to aid the model to learn deep hierarchical representations. Finally, an enhanced mixing loss is proposed to tightly supervise the overall learning process. The proposed model has been implemented for joint OD and OC segmentation on three publicly available datasets: DRISHTI-GS, RIM-ONE R3, and REFUGE. Additionally, to validate our proposal, we have performed exhaustive experimentation on Glaucoma detection from all three datasets by measuring the Cup to Disc Ratio (CDR) value. Experimental results demonstrate the superiority of UT-Net as compared to the state-of-the-art methods.

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“…Lei et al [24] introduced an unsupervised DA method which is based on the image synthesis and feature alignment (ISFA) approach, in order to segment OD and OC on fundus images for assessment of glaucoma disease. They utilized the GANbased image synthesis strategy along with boundary information for OD and OC to create target-like query data, that can act as the intermediate latent space among source and target domains data, thereby decreasing the domain discrepancy problem.…”

Section: B Deep Domain Adaptation In Glaucoma Researchmentioning

confidence: 99%

Domain Adaptation-Based Deep Learning Models for Forecasting and Diagnosis of Glaucoma Disease

Madadi¹,

Abu-Serhan²,

Yousefi³

2022

Preprint

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<p>Domain adaptation methods are designed to extract shared domain-invariant features by projecting data on a common subspace in order to align their domain distributions. However, these methods do not usually consider domain-specific features, and therefore their distributions may not be well aligned. To address this problem, we introduce a novel model that learns domain-invariant and domain-specific representations to extract both their general and specific features. We also propose progressive weighting to accurately transfer the source domain knowledge and to mitigate negative knowledge transfer from the source to the target domain and to employ low-rank coding for aligning the source and target distributions. We evaluated the model based on several real-world datasets and showed that our method significantly improved the accuracy for forecasting and diagnosis of glaucoma disease from fundus photographs.</p>

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Unsupervised Domain Adaptation Based Image Synthesis and Feature Alignment for Joint Optic Disc and Cup Segmentation

Cited by 52 publications

References 47 publications

Boundary Guided Semantic Learning for Real-Time COVID-19 Lung Infection Segmentation System

Boundary Guided Semantic Learning for Real-Time COVID-19 Lung Infection Segmentation System

RecU-Net++: Improved Utilization of Receptive Fields in U-Net++ for Skin Lesion Segmentation

Domain Adaptation-Based Deep Learning Models for Forecasting and Diagnosis of Glaucoma Disease

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