Segmenting retinal OCT images with inter-B-scan and longitudinal information

Yufan He; Aaron Carass; Yihao Liu; Angeliki Filippatou; Bruno M. Jedynak; Sharon D. Solomon; Shiv Saidha; Peter A. Calabresi; Jerry L. Prince

doi:10.1117/12.2549857

10 March 2020 Segmenting retinal OCT images with inter-B-scan and longitudinal information

Yufan He, Aaron Carass, Yihao Liu, Angeliki Filippatou, Bruno M. Jedynak, Sharon D. Solomon, Shiv Saidha, Peter A. Calabresi, Jerry L. Prince

Author Affiliations +

Proceedings Volume 11313, Medical Imaging 2020: Image Processing; 113133C (2020) https://doi.org/10.1117/12.2549857
Event: SPIE Medical Imaging, 2020, Houston, Texas, United States

Abstract

Monitoring retinal thickness of persons with multiple sclerosis (MS) provides important bio-markers for disease progression. However, changes in retinal thickness can be small and concealed by noise in the acquired data. Consistent longitudinal retinal layer segmentation methods for optical coherence tomography (OCT) images are crucial for identifying the real longitudinal retinal changes of individuals with MS. In this paper, we propose an iterative registration and deep learning based segmentation method for longitudinal 3D OCT scans. Since 3D OCT scans are usually anisotropic with large slice separation, we extract B-scan features using 2D deep networks and utilize inter-B-scan context with convolutional long-short-term memory (LSTM). To incorporate longitudinal information, we perform fundus registration and interpolate the smooth retinal surfaces of the previous visit to use as a prior on the current visit.

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Yufan He, Aaron Carass, Yihao Liu, Angeliki Filippatou, Bruno M. Jedynak, Sharon D. Solomon, Shiv Saidha, Peter A. Calabresi, and Jerry L. Prince "Segmenting retinal OCT images with inter-B-scan and longitudinal information", Proc. SPIE 11313, Medical Imaging 2020: Image Processing, 113133C (10 March 2020); https://doi.org/10.1117/12.2549857

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