LF-UNet – A novel anatomical-aware dual-branch cascaded deep neural network for segmentation of retinal layers and fluid from optical coherence tomography images. (December 2021) - Ma, Da; Lu, Donghuan; Chen, Shuo; Heisler, Morgan; Dabiri, Setareh; Lee, Sieun; Lee, Hyunwoo; Ding, Gavin Weiguang; Sarunic, Marinko V.; Beg, Mirza Faisal
Abstract: Computer-assistant diagnosis of retinal disease relies heavily on the accurate detection of retinal boundaries and other pathological features such as fluid accumulation. Optical coherence tomography (OCT) is a non-invasive ophthalmological imaging technique that has become a standard modality in the field due to its ability to detect cross-sectional retinal pathologies at the micrometer level. In this work, we presented a novel framework to achieve simultaneous retinal layers and fluid segmentation. A dual-branch deep neural network, termed LF-UNet, was proposed which combines the expansion path of the U-Net and original fully convolutional network, with a dilated network. In addition, we introduced a cascaded network framework to include the anatomical awareness embedded in the volumetric image. Cross validation experiments showed that the proposed LF-UNet has superior performance compared to the state-of-the-art methods, and that incorporating the relative positional map structural prior information could further improve the performance regardless of the network. The generalizability of the proposed network was demonstrated on an independent dataset acquired from the same types of device with different field of view, or images acquired from different device. Highlights: We introduce a dual-branch deep neural network, which combines the expansion path of the U-Net and original fully convolutional network, combined with a dilated network. Cross validation experiments proved that the proposed network architecture has superior performance comparing with the state-of-the-art methods. We introduced a cascaded network framework to include the anatomical awareness embedded in the volumetric image further improve the performance regardless the network. Further experiment showed the generalizability of the proposed network when testing on independent dataset that were acquired either from the same types of device with different field of view, as well as images acquired from different device.