Automated measurement of the minimum macular hole diameter based on optical coherence tomography images

Accurate measurement of minimum macular hole (MH) diameter is essential for diagnosis and treatment. The manual measurement approach by ophthalmologists is time-consuming, poorly reproducible, and exhibits high inter-observer variability. Threshold-based methods are sensitive to image quality, and perform inadequately in low-contrast optical coherence tomography (OCT) images. Deep learning can achieve higher measurement accuracy but shows limited generalization capability. We propose an automated measurement method comprising three sub-tasks. Specifically, a tailored MH dataset is first created by cropping publicly available OCT images to minimize interference from non-MH regions. Subsequently, an image processing pipeline, which consists of denoising, binarization, morphological operations, and edge detection, is implemented to extract the contours on both sides of MH. Finally, an automated measurement algorithm is designed to locate the closest points on the bilateral contours of MH and thereby calculate the minimum diameter. Extensive experiments are conducted to validate the effectiveness of this research. More concretely, on the public dataset, the Dₐ metric obtained by the proposed method closely aligns with the Dₘ metric. The ADE values are predominantly within 1 pixel, achieving as low as 0.00 in some cases. Simultaneously, the method demonstrates favorable performance in the RDE metric, with values ranging from 0.00% to 3.29%, most of which remain below 2.50%. Furthermore, in clinical testing, the measurement time per individual sample is approximately 4 seconds, indicating high efficiency. Overall, the comprehensive quantitative and qualitative experimental results confirm the method’s commendable accuracy and execution efficiency in measuring the minimum MH diameter, demonstrating its potential value in assisting ophthalmologists with the diagnosis and treatment assessment of MH conditions.