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Updated: Feb 15, 2026
10:25
Deep Learning-Based Segmentation of Cryo-Electron Tomograms
Published on: November 11, 2022
10.9K
Automated segmentation of pterygium lesions using multiscale deep learning networks.
1Department of Electrical, Electronic and Systems Engineering, Faculty of Engineering and Built Environment, Universiti Kebangsaan Malaysia, UKM, Bangi, 43600, Selangor, Malaysia.
Experimental Eye Research
|February 13, 2026
Summary
This study introduces a deep learning method for early pterygium detection. The best model accurately maps eye lesions, improving severity prediction and preventing vision loss.
Area of Science:
- Ophthalmology
- Medical Imaging
- Computer Science
Background:
- Pterygium is an eye condition requiring early detection to prevent visual impairment.
- Accurate measurement of fibrovascular tissue encroachment is crucial for assessing pterygium severity.
- Deep learning offers a promising approach for automated pterygium lesion quantification.
Purpose of the Study:
- To develop a semantic segmentation method for accurate pterygium lesion mapping.
- To explore multiscale deep learning networks for capturing variable lesion features.
- To improve the prediction of pterygium severity through precise lesion extraction.
Main Methods:
- Implemented multiscale deep learning modules (SPP, ASPP) within a UNet architecture.
- Investigated equal-flow (EF) and waterfall-flow (WF) patterns for parallel path construction.
- Evaluated segmentation performance using Hausdorff distance.
Main Results:
- The UNet architecture with three parallel paths of the EF-ASPP module achieved the lowest Hausdorff distance (16.75 pixels).
- This multiscale approach effectively captured variable pterygium lesion scales.
- Accurate lesion mapping facilitated better prediction of pterygium severity.
Conclusions:
- Multiscale deep learning networks, particularly EF-ASPP within UNet, show significant potential for pterygium segmentation.
- Accurate segmentation of pterygium lesions aids in early detection and management, mitigating visual impairment risks.
- Future work could explore diverse network architectures for enhanced performance, balancing accuracy and computational cost.
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