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Related Experiment Video

Updated: Jan 9, 2026

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Published on: November 30, 2022

STB-Net: a Siamese architecture-based reconstruction-segmentation network for ocular surface image segmentation.

Cheng Wan^1,2, Jimei Wu¹, Yulong Mao¹

¹College of Electronic and Information Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing, China.

Quantitative Imaging in Medicine and Surgery

|December 10, 2025

Summary

This summary is machine-generated.

This study introduces STB-Net, an automated model for precise eyelid measurements from ocular images. The novel deep learning approach significantly improves the diagnosis of eyelid disorders.

Keywords:

Deep learning assisted diagnosis image segmentation ocular surface imaging unsupervised learning

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Last Updated: Jan 9, 2026

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

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Published on: November 30, 2022

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Published on: November 11, 2022

Area of Science:

Ophthalmology
Medical Imaging
Computer Vision

Background:

Accurate eyelid morphological parameters are vital for diagnosing eyelid disorders.
Automated and precise measurement of these parameters remains a significant clinical challenge.

Purpose of the Study:

To develop an automated segmentation model for ocular surface images.
To accurately segment key anatomical structures for precise eyelid metric computation.

Main Methods:

Proposed STB-Net, a novel segmentation model enhancing TransUNet with a Bottom-up Local Attention Modulation (BLAM) module.
Integrated TB-Net with SRSNetwork for augmented reconstruction-task training, improving segmentation.
Model automatically computes palpebral fissure heights, width, and area.

Main Results:

Achieved high efficacy on a local dataset for palpebral fissure segmentation (Dice: 0.9875, GA: 0.9955, IoU: 0.9767).
Demonstrated strong corneal segmentation performance (Dice: 0.9891, GA: 0.9978, IoU: 0.9790).

Conclusions:

STB-Net offers a robust solution for automated ocular surface segmentation.
Enables precise quantification of eyelid morphological parameters, enhancing diagnostic objectivity and efficiency for eyelid disorders.