Optic Cup Segmentation
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Benchmarks
REFUGE Challenge
Most implemented
G1020: A Benchmark Retinal Fundus Image Dataset for Computer-Aided Glaucoma Detection
MedSegDiff: Medical Image Segmentation with Diffusion Probabilistic Model
Medical Image Segmentation Using Squeeze-and-Expansion Transformers
Papers
DDS-UDA: Dual-Domain Synergy for Unsupervised Domain Adaptation in Joint Segmentation of Optic Disc and Optic Cup
Convolutional neural networks (CNNs) have achieved exciting performance in joint segmentation of optic disc and optic cup on single-institution datasets. However, their clinical translation is hindered by two major chall…
Unsupervised Domain AdaptationOptic Cup SegmentationDomain GeneralizationLightHCG: a Lightweight yet powerful HSIC Disentanglement based Causal Glaucoma Detection Model framework
As a representative optic degenerative condition, glaucoma has been a threat to millions due to its irreversibility and severe impact on human vision fields. Mainly characterized by dimmed and blurred visions, or periphe…
Representation LearningOptic Cup SegmentationFunduSegmenter: Leveraging the RETFound Foundation Model for Joint Optic Disc and Optic Cup Segmentation in Retinal Fundus Images
Purpose: This study introduces the first adaptation of RETFound for joint optic disc (OD) and optic cup (OC) segmentation. RETFound is a well-known foundation model developed for fundus camera and optical coherence tomog…
Optic Cup SegmentationDomain GeneralizationRethinking domain generalization in medical image segmentation: One image as one domain
Domain shifts in medical image segmentation, particularly when data comes from different centers, pose significant challenges. Intra-center variability, such as differences in scanner models or imaging protocols, can cau…
DisentanglementDomain GeneralizationImage SegmentationMedical Image Segmentation+4Adaptive Feature Fusion Neural Network for Glaucoma Segmentation on Unseen Fundus Images
Fundus image segmentation on unseen domains is challenging, especially for the over-parameterized deep models trained on the small medical datasets. To address this challenge, we propose a method named Adaptive Feature-f…
DecoderDomain GeneralizationImage SegmentationMulti-Task Learning+3Uncertainty-Aware Adapter: Adapting Segment Anything Model (SAM) for Ambiguous Medical Image Segmentation
The Segment Anything Model (SAM) gained significant success in natural image segmentation, and many methods have tried to fine-tune it to medical image segmentation. An efficient way to do so is by using Adapters, specia…
Image SegmentationMedical Image SegmentationOptic Cup SegmentationSegmentation+1