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Published on: February 8, 2019
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Morphological Rule-Constrained Object Detection of Key Structures in Infant Fundus Image
Summary
This study enhances deep learning for detecting optic disc and macula in Retinopathy of prematurity (ROP) diagnosis by adding morphological rules. This improves macula detection accuracy significantly.
Area of Science:
- Ophthalmology
- Medical Imaging
- Computer Vision
Background:
- Accurate detection of the optic disc and macula is crucial for diagnosing Retinopathy of prematurity (ROP).
- Existing deep learning object detection methods require enhancement for precise identification of these structures.
Purpose of the Study:
- To improve deep learning-based object detection of the optic disc and macula by incorporating domain-specific morphological rules.
- To enhance the accuracy and reliability of ROP diagnosis through improved anatomical landmark detection.
Main Methods:
- Defined five morphological rules based on fundus morphology: number, size, distance, angle/slope, and position restrictions.
- Applied these rules to refine object detection results from a deep learning model.
- Validated the method on a dataset of 2953 infant fundus images.
Main Results:
- Naïve object detection accuracy for optic disc was 0.955 and for macula was 0.719.
- The proposed method, incorporating morphological rules, increased macula detection accuracy to 0.811.
- Improved Intersection over Union (IoU) and Relative Center Error (RCE) metrics were observed.
Conclusions:
- Integrating domain-specific morphological rules significantly enhances the accuracy of optic disc and macula detection in infant fundus images.
- The refined detection aids in more reliable ROP zone segmentation and disease diagnosis.
- This approach effectively reduces false positives and improves key performance metrics.
