Journal
COMPUTERS IN BIOLOGY AND MEDICINE
Volume 140, Issue -, Pages -Publisher
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.compbiomed.2021.105070
Keywords
Image analysis; Machine learning; Annotation; External limiting membrane; Optical coherence tomography
Categories
Funding
- Northern Accelerator grant [OSR/0260/DSTE/HEIF]
- Newcastle University
Ask authors/readers for more resources
This study establishes a new benchmark for the segmentation of the retinal external limiting membrane in patients with idiopathic full-thickness macular holes using spectral domain optical coherence tomography images. The annotation of ELM line was performed with precise image-wise binary annotations after extensive quality analysis of OCT imaging and annotation data. Comparison with seven state-of-the-art machine learning-based segmentation methods was conducted to identify the ELM line with an automated system.
In this article, we present a new benchmark for the segmentation of the retinal external limiting membrane (ELM) using an image dataset of spectral domain optical coherence tomography (OCT) scans in a patient population with idiopathic full-thickness macular holes. Specifically, the dataset used contains OCT images from one eye of 107 patients with an idiopathic full-thickness macular hole. In total, the dataset contains 5243 individual 2-dimensional (2-D) OCT image slices, with each patient contributing 49 individual spectral-domain OCT tagged image slices. We display precise image-wise binary annotations to segment the ELM line. The OCT images present high variations in image contrast, motion, brightness, and speckle noise which can affect the robustness of applied algorithms, so we performed an extensive OCT imaging and annotation data quality analysis. Imaging data quality control included noise, blurriness and contrast scores, motion estimation, darkness and average pixel scores, and anomaly detection. Annotation quality was measured using gradient mapping of ELM line annotation confidence, and idiopathic full-thickness macular hole detection. Finally, we compared qualitative and quantitative results with seven state-of-the-art machine learning-based segmentation methods to identify the ELM line with an automated system.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available