Fully-automatic segmentation of the ciliary muscle using anterior segment optical coherence tomography images

The study of the ciliary muscle represents a fundamental step in the diagnosis and treatment of many high-incidence diseases, such as glaucoma or myopia. Currently, Anterior Segment Optical Coherence Tomography (AS-OCT) is widely used by clinicians to analyse the morphological changes that affect this important ocular structure. AS-OCT is a non-invasive imaging technique that produces high-resolution cross-sectional images, allowing a precise visualization of the main ocular tissues of the anterior segment of the eye. In this work, we propose a novel methodology for the ciliary muscle segmentation using AS-OCT images, an emerging ophthalmic imaging technology with great potential to support early diagnosis of relevant ocular conditions. For this purpose, we have analysed the performance of the U-Net architecture with two different encoders (ResNet-18 and ResNet-34) combined with a transfer learning-based approach. The validation of the proposed system was performed through different and representative experiments, using an AS-OCT dataset that was specifically designed for this work. The results demonstrated that the proposed system is robust and reliable, achieving an average Precision of 0.8902 ± 0.0815, an average Recall of 0.8237 ± 0.1239, an average Accuracy of 0.9961 ± 0.0021, an average Jaccard of 0.7431 ± 0.1116 and an average Dice of 0.8445 ± 0.0870. These results demonstrate that the proposed method has a satisfactory performance that can help the clinicians to make a more accurate diagnosis and proceed with appropriate treatments of different diseases of interest.