Deep-Learning Based, Automated Segmentation of Macular Edema in Optical Coherence Tomography
By Cecilia S. Lee, Ariel J. Tyring, Nicolaas P. Deruyter, Yue Wu, Ariel S. Rokem, Aaron Y. Lee. bioRxiv, 2017
315 citations recorded by the source corpus.
Evaluation of clinical images is essential for diagnosis in many specialties and the development of computer vision algorithms to analyze biomedical images will be important. In ophthalmology, optical coherence tomography (OCT) is critical for managing retinal conditions. We developed a convolutional neural network (CNN) that detects intraretinal fluid (IRF) on OCT in a manner indistinguishable from clinicians. Using 1,289 OCT images, the CNN segmented images with a 0.911 cross-validated Dice coefficient, compared with segmentations by experts. Additionally, the agreement between experts and between experts and CNN were similar. Our results reveal that CNN can be trained to perform automated segmentations.
Subfields: Ophthalmology, Radiology, Nuclear Medicine and Imaging
View Paper: https://www.semanticscholar.org/paper/2bf42496de45923dae902fe285e34844750fd189
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In Radiology, Nuclear Medicine and Imaging
Citations in the corpus
- Artificial intelligence in retina. (2018, methodology): “One important target of quantification is retinal fluid and a few successful approaches have been presented, mainly based on supervised deep learning (Breger et al., 2017; Lee et al., 2017b; Montuoro et al., 2017; Roy et al., 2017).”