Topology-preserving shape-based regression of retinal layers in oct image data using convolutional neural networks


Optical coherence tomography (OCT) is a non-invasive imaging modality that provides cross-sectional 3D images of biological tissue. Especially in ophthalmology OCT is used for the diagnosis of various eye diseases. Automatic retinal layer segmentation algorithms, which are increasingly based on deep learning techniques, can support diagnostics. However, topology properties, such as the order of retinal layers, are often not considered. In our work, we present an automatic segmentation approach based on shape regression using convolutional neural networks (CNNs). Here, shapes are represented by signed distance maps (SDMs) that assign the distance to the next object contour to each pixel. Thus, spatial regularization is introduced and plausible segmentations can be produced. Our method is evaluated on a public OCT dataset and is compared with two classification-based approaches. The results show that our method has fewer outliers with comparable segmentation performance. In addition, it has an improved topology preservation, which saves further post-processing.

Titel2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019)
Herausgeber (Verlag)IEEE
ISBN (Print)978-1-5386-3641-1
ISBN (elektronisch)978-1-5386-3642-8
PublikationsstatusVeröffentlicht - 04.2019
Veranstaltung16th IEEE International Symposium on Biomedical Imaging - Hilton Molino Stucky - Venice, Venice, Italien
Dauer: 08.04.201911.04.2019
Konferenznummer: 149553


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