Diagnosis and monitoring of retina diseases related to pathologies such as accumulated fluid can be performed using optical coherence tomography (OCT). OCT acquires a series of 2D slices (Bscans). This work presents a fully-automated method based on graph shortest path algorithms and convolutional neural network (CNN) to segment and detect three types of fluid including sub-retinal fluid (SRF), intra-retinal fluid (IRF) and pigment epithelium detachment (PED) in OCT Bscans of subjects with age-related macular degeneration (AMD) and retinal vein occlusion (RVO) or diabetic retinopathy. The proposed method achieves an average dice coefficient of 76.44%, 92.25% and 82.14% in Cirrus, Spectralis and Topcon datasets, respectively. The effectiveness of the proposed methods was also demonstrated in segmenting fluid in OCT images from the 2017 Retouch challenge.
|Original language||English (US)|
|Title of host publication||40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2018|
|Publisher||Institute of Electrical and Electronics Engineers Inc.|
|Number of pages||4|
|State||Published - Oct 26 2018|
|Event||40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2018 - Honolulu, United States|
Duration: Jul 18 2018 → Jul 21 2018
|Name||Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS|
|Other||40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2018|
|Period||7/18/18 → 7/21/18|
Bibliographical noteFunding Information:
*This research was supported in part by the Minnesota Lions Foundation under grant UMF14601 and by the Research to Prevent Blindness.