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CMS-NET: deep learning algorithm to segment and quantify the ciliary muscle in swept-source optical coherence

Wen Chen1, Xiangle Yu1, Yiru Ye2,3

  • 1School of Ophthalmology and Optometry, Wenzhou Medical University, Wenzhou, China.

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A new deep learning system automatically segments ciliary muscle images from OCT scans. This method accurately quantifies ciliary muscle changes during accommodation, aiding presbyopia research.

Keywords:
accommodationciliary muscledeep learningoptical coherence tomography (OCT)presbyopia

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Area of Science:

  • Ophthalmology
  • Medical Imaging
  • Artificial Intelligence

Background:

  • The ciliary muscle is crucial for lens accommodation, essential for clear vision during near tasks.
  • Understanding ciliary muscle dynamics during accommodation is key to unraveling presbyopia mechanisms.
  • Optical coherence tomography (OCT) is used for in vivo imaging of the ciliary muscle, but segmentation is challenging due to data volume and image quality.

Purpose of the Study:

  • To develop a fully automated method for segmenting and quantifying the ciliary muscle using OCT images.
  • To create a deep learning algorithm for precise ciliary muscle analysis.

Main Methods:

  • A novel deep learning algorithm, combining U-net and a full-resolution residual network, was developed using 3500 OCT images.
  • The algorithm was trained for automatic segmentation and quantification of the ciliary muscle.
  • Performance was evaluated by comparing algorithm predictions with manual annotations.

Main Results:

  • The automated system achieved high segmentation accuracy with a Dice coefficient of 93.8% and IoU of 88.7%.
  • The system's performance was comparable to that of experienced specialists.
  • The framework successfully segmented ciliary muscle images and quantified thickness changes during accommodation.

Conclusions:

  • An automated segmentation framework for ciliary muscle analysis was successfully developed.
  • This tool enables the analysis of ciliary muscle morphological parameters and dynamic changes during accommodation.
  • The developed system offers a valuable tool for research into accommodation and presbyopia.