Snake with Shifted Window: Learning to Adapt Vessel Pattern for OCTA Segmentation

• URL: http://arxiv.org/abs/2404.18096v1
• Date: Sun, 28 Apr 2024 07:01:55 GMT
• Title: Snake with Shifted Window: Learning to Adapt Vessel Pattern for OCTA Segmentation
• Authors: Xinrun Chen, Mei Shen, Haojian Ning, Mengzhan Zhang, Chengliang Wang, Shiying Li,
• Abstract summary: We propose the SSW- OCTA model, which integrates the advantages of deformable convolutions suited for tubular structures and the swin-transformer for global feature extraction. Our model underwent testing and comparison on the OCTA-500 dataset, achieving state-of-the-art performance.
• Score: 2.314516220934268
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Segmenting specific targets or structures in optical coherence tomography angiography (OCTA) images is fundamental for conducting further pathological studies. The retinal vascular layers are rich and intricate, and such vascular with complex shapes can be captured by the widely-studied OCTA images. In this paper, we thus study how to use OCTA images with projection vascular layers to segment retinal structures. To this end, we propose the SSW-OCTA model, which integrates the advantages of deformable convolutions suited for tubular structures and the swin-transformer for global feature extraction, adapting to the characteristics of OCTA modality images. Our model underwent testing and comparison on the OCTA-500 dataset, achieving state-of-the-art performance. The code is available at: https://github.com/ShellRedia/Snake-SWin-OCTA.

Related papers

• KaLDeX: Kalman Filter based Linear Deformable Cross Attention for Retina Vessel Segmentation [46.57880203321858]
  We propose a novel network (KaLDeX) for vascular segmentation leveraging a Kalman filter based linear deformable cross attention (LDCA) module. Our approach is based on two key components: Kalman filter (KF) based linear deformable convolution (LD) and cross-attention (CA) modules. The proposed method is evaluated on retinal fundus image datasets (DRIVE, CHASE_BD1, and STARE) as well as the 3mm and 6mm of the OCTA-500 dataset.
  arXiv  Detail & Related papers  (2024-10-28T16:00:42Z)
• KLDD: Kalman Filter based Linear Deformable Diffusion Model in Retinal Image Segmentation [51.03868117057726]
  This paper proposes a novel Kalman filter based Linear Deformable Diffusion (KLDD) model for retinal vessel segmentation. Our model employs a diffusion process that iteratively refines the segmentation, leveraging the flexible receptive fields of deformable convolutions. Experiments are evaluated on retinal fundus image datasets (DRIVE, CHASE_DB1) and the 3mm and 6mm of the OCTA-500 dataset.
  arXiv  Detail & Related papers  (2024-09-19T14:21:38Z)
• OCTAMamba: A State-Space Model Approach for Precision OCTA Vasculature Segmentation [10.365417594185685]
  We propose OCTAMamba, a novel U-shaped network based on the Mamba architecture to segment vasculature in OCTA accurately. OCTAMamba integrates a Quad Stream Efficient Mining Embedding Module for local feature extraction, a Multi-Scale Dilated Asymmetric Convolution Module to capture multi-scale vasculature, and a Focused Feature Recalibration Module to filter noise and highlight target areas. Our method achieves efficient global modeling and local feature extraction while maintaining linear complexity, making it suitable for low-computation medical applications.
  arXiv  Detail & Related papers  (2024-09-12T12:47:34Z)
• Serp-Mamba: Advancing High-Resolution Retinal Vessel Segmentation with Selective State-Space Model [45.682311387979944]
  We propose the first Serpentine Mamba (Serp-Mamba) network to address this challenging task. We first devise a Serpentine Interwoven Adaptive (SIA) scan mechanism, which scans UWF-SLO images along curved vessel structures in a snake-like crawling manner. Second, we propose an Ambiguity-Driven Dual Recalibration module to address the category imbalance problem intensified by high-resolution images.
  arXiv  Detail & Related papers  (2024-09-06T15:40:47Z)
• Synthetic optical coherence tomography angiographs for detailed retinal vessel segmentation without human annotations [12.571349114534597]
  We present a lightweight simulation of the retinal vascular network based on space colonization for faster and more realistic OCTA synthesis. We demonstrate the superior segmentation performance of our approach in extensive quantitative and qualitative experiments on three public datasets.
  arXiv  Detail & Related papers  (2023-06-19T14:01:47Z)
• Affinity Feature Strengthening for Accurate, Complete and Robust Vessel Segmentation [48.638327652506284]
  Vessel segmentation is crucial in many medical image applications, such as detecting coronary stenoses, retinal vessel diseases and brain aneurysms. We present a novel approach, the affinity feature strengthening network (AFN), which jointly models geometry and refines pixel-wise segmentation features using a contrast-insensitive, multiscale affinity approach.
  arXiv  Detail & Related papers  (2022-11-12T05:39:17Z)
• SIAN: Style-Guided Instance-Adaptive Normalization for Multi-Organ Histopathology Image Synthesis [63.845552349914186]
  We propose a style-guided instance-adaptive normalization (SIAN) to synthesize realistic color distributions and textures for different organs. The four phases work together and are integrated into a generative network to embed image semantics, style, and instance-level boundaries.
  arXiv  Detail & Related papers  (2022-09-02T16:45:46Z)
• ROSE: A Retinal OCT-Angiography Vessel Segmentation Dataset and New Model [41.444917622855606]
  We release a dedicated OCT-A SEgmentation dataset (ROSE), which consists of 229 OCT-A images with vessel annotations at either centerline-level or pixel level. Secondly, we propose a novel Split-based Coarse-to-Fine vessel segmentation network (SCF-Net), with the ability to detect thick and thin vessels separately. In the SCF-Net, a split-based coarse segmentation (SCS) module is first introduced to produce a preliminary confidence map of vessels, and a split-based refinement (SRN) module is then used to optimize the shape/contour of
  arXiv  Detail & Related papers  (2020-07-10T06:54:19Z)
• Pathological Retinal Region Segmentation From OCT Images Using Geometric Relation Based Augmentation [84.7571086566595]
  We propose improvements over previous GAN-based medical image synthesis methods by jointly encoding the intrinsic relationship of geometry and shape. The proposed method outperforms state-of-the-art segmentation methods on the public RETOUCH dataset having images captured from different acquisition procedures.
  arXiv  Detail & Related papers  (2020-03-31T11:50:43Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.