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The main application of optical coherence tomography (OCT) is in the field of ophthalmology, where it is used for diagnosis of various eye diseases. The automatic segmentation of the individual retinal layers as well as pathological structures in OCT scans is helpful for clinical examination and treatment planning. Current methods often do not consider the strict arrangement of retinal layers. Although graph-based methods are suitable for correcting topology errors, their applicability is costly and complex, especially in the presence of pathologies. In this work, a segmentation method is proposed that utilizes additional shape information of the retinal layers to provide improved topology preservation while maintaining simple applicability. For this purpose, a U-Netbased network architecture is extended to a multi-task approach to allow regression of the shape information of the retinal layers in addition to pixel-wise classification. This introduces spatial regularization and allows the generation of plausible segmentations. A consistency term ensures agreement between the classification and regression task, which also allows for semi-supervised training. In a comprehensive evaluation, the performance of the proposed multi-task approach is investigated, using OCT image data from patients with diabetic macular edema. The results demonstrate that the integration of shape information improves the preservation of the topology of the retinal layers. Moreover, the use of a semi-supervised training scheme via a consistency term improves the robustness and refines the fluid delineation of the proposed method.
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