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Fluid-SegNet: Multi-dimensional loss-driven Y-Net with dilated convolutions for OCT B-scan fluid segmentation.

Xiaozhong Xue1, Weiwei Du1, Qinghua Hu2

  • 1Kyoto Institute of Technology, Kyoto, 606-8585, Japan.

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|August 5, 2025
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Summary
This summary is machine-generated.

Accurate segmentation of retinal fluid regions in Optical Coherence Tomography (OCT) B-scans is vital for diagnosing eye diseases. Fluid-SegNet, a new deep learning model, effectively segments these fluid regions, improving diagnostic accuracy.

Keywords:
Fluid region segmentationFluid-segNetMulti dimensional loss functionOptical coherence tomography

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Area of Science:

  • Ophthalmology
  • Medical Imaging
  • Artificial Intelligence

Background:

  • Optical Coherence Tomography (OCT) is essential for retinal imaging in ophthalmology.
  • Accurate segmentation of fluid regions in OCT B-scans is critical for disease diagnosis and treatment planning.

Purpose of the Study:

  • To introduce Fluid-SegNet, a novel deep learning framework for accurate fluid region segmentation in OCT B-scans.
  • To address challenges in segmenting fine details, small regions, and heterogeneous fluid areas.

Main Methods:

  • Development of Fluid-SegNet, a deep learning-based segmentation framework.
  • Evaluation of Fluid-SegNet on three public datasets: UMN, AROI, and OIMHS.

Main Results:

  • Fluid-SegNet achieved mean Dice scores of 0.8725 (UMN), 0.6967 (AROI), and 0.8020 (OIMHS).
  • The model demonstrated effectiveness and robustness in segmenting fluid regions across diverse datasets and imaging conditions.
  • Fluid-SegNet successfully addressed challenges of fine detail delineation and fluid region heterogeneity.

Conclusions:

  • Fluid-SegNet enhances the accuracy of fluid region segmentation in OCT B-scans.
  • The proposed method offers a robust solution for automated retinal disease diagnosis and visual acuity prediction.