A fine‐to‐coarse‐to‐fine weakly supervised framework for volumetric SD‐OCT image segmentation

Abstract

Abstract Obtaining accurate segmentation of central serous chorioretinopathy in spectral‐domain optical coherence tomography (SD‐OCT) is critical for the determination of the disease severity. Although existing methods achieve considerable segmentation results, they heavily depend on large‐scale data with high‐quality annotations. Also, the lesions bear a large shape variation across different patients, which are often difficult to encode. To address the above problems, we propose a fine‐to‐coarse‐to‐fine weakly supervised framework. Specifically, global alternate max‐avg pooling (GTP) network can be employed to locate the lesion regions accurately by using only image‐level annotations. A network module based on the GTP network and a semantic transfer module are proposed to iteratively guide the network to continuously discover and expand the target lesion regions. Then, we employ 3D grey distribution histogram to generate pseudo‐volumetric labels. Finally, a novel 3D level set loss function is proposed to perform coarse‐to‐fine volumetric segmentation. Experiments on a challenging dataset demonstrate that the performance of our proposed method is closer to those of models trained with pixel‐level supervision.