Abstract
Most automated segmentation approaches for quantitative assessment of sub-retinal fluid regions rely heavily on retinal anatomy knowledge (e.g. layer segmentation) and pixel-level annotation, which requires excessive manual intervention and huge learning costs. In this paper, we propose a weakly supervised learning method for the quantitative analysis of lesion regions in spectral domain optical coherence tomography (SD-OCT) images. Specifically, we first obtain more accurate positioning through improved class activation mapping; second, in the feature propagation learning network, the multi-scale features learned by the slice-level classification are employed to expand its activation area and generate soft labels; finally, we use generated soft labels to train a fully supervised network for more robust results. The proposed method is evaluated on subjects from a dataset with 23 volumes for cross-validation experiments. The experimental results demonstrate that the proposed method can achieve encouraging segmentation accuracy comparable to strong supervision methods only utilizing image-level labels.
This work was supported by the National Natural Science Foundation of China under Grant No. 61701192, 61671242, 61872419, 61873324.
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Wang, T., Niu, S., Dong, J., Chen, Y. (2020). Weakly Supervised Retinal Detachment Segmentation Using Deep Feature Propagation Learning in SD-OCT Images. In: Fu, H., Garvin, M.K., MacGillivray, T., Xu, Y., Zheng, Y. (eds) Ophthalmic Medical Image Analysis. OMIA 2020. Lecture Notes in Computer Science(), vol 12069. Springer, Cham. https://doi.org/10.1007/978-3-030-63419-3_15
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