Abstract
Deep-learning-based approaches for retinal lesion segmentation often require an abundant amount of precise pixel-wise annotated data. However, coarse annotations such as circles or ellipses for outlining the lesion area can be six times more efficient than pixel-level annotation. Therefore, this paper proposes an annotation refinement network to convert a coarse annotation into a pixel-level segmentation mask. Our main novelty is the application of the prototype learning paradigm to enhance the generalization ability across different datasets or types of lesions. We also introduce a prototype weighing module to handle challenging cases where the lesion is overly small. The proposed method was trained on the publicly available IDRiD dataset and then generalized to the public DDR and our real-world private datasets. Experiments show that our approach substantially improved the initial coarse mask and outperformed the non-prototypical baseline by a large margin. Moreover, we demonstrate the usefulness of the prototype weighing module in both cross-dataset and cross-class settings.
Q. Yu and K. Dang—Contribute equally to this work.
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References
Chu, T., Li, X., Vo, H.V., Summers, R.M., Sizikova, E.: Improving weakly supervised lesion segmentation using multi-task learning. In: Medical Imaging with Deep Learning, pp. 60–73. PMLR (2021)
Ester, M., et al.: A density-based algorithm for discovering clusters in large spatial databases with noise. In: KDD, vol. 96, pp. 226–231 (1996)
He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016)
Huang, S., Li, J., Xiao, Y., Shen, N., Xu, T.: RTNet: relation transformer network for diabetic retinopathy multi-lesion segmentation. IEEE Trans. Med. Imaging. 41, 1596–1607 (2022)
Huang, Y., et al.: Automated hemorrhage detection from coarsely annotated fundus images in diabetic retinopathy. In: 2020 IEEE 17th International Symposium on Biomedical Imaging (ISBI), pp. 1369–1372. IEEE (2020)
Irving, B.: maskslic: regional superpixel generation with application to local pathology characterisation in medical images. arXiv preprint arXiv:1606.09518 (2016)
Li, G., Jampani, V., Sevilla-Lara, L., Sun, D., Kim, J., Kim, J.: Adaptive prototype learning and allocation for few-shot segmentation. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 8334–8343 (2021)
Li, T., Gao, Y., Wang, K., Guo, S., Liu, H., Kang, H.: Diagnostic assessment of deep learning algorithms for diabetic retinopathy screening. Inf. Sci. 501, 511–522 (2019)
Liu, Q., Liu, H., Liang, Y.: M2MRF: Many-to-many reassembly of features for tiny lesion segmentation in fundus images. arXiv preprint arXiv:2111.00193 (2021)
Liu, X., et al.: Weakly supervised segmentation of covid19 infection with scribble annotation on CT images. Pattern Recogn. 122, 108341 (2022)
Playout, C., Duval, R., Cheriet, F.: A novel weakly supervised multitask architecture for retinal lesions segmentation on fundus images. IEEE Trans. Med. Imaging 38(10), 2434–2444 (2019)
Porwal, P., et al.: IDRiD: diabetic retinopathy-segmentation and grading challenge. Med. Image Anal. 59, 101561 (2020)
Ronneberger, O., Fischer, P., Brox, T.: U-Net: convolutional networks for biomedical image segmentation. In: Navab, N., Hornegger, J., Wells, W.M., Frangi, A.F. (eds.) MICCAI 2015. LNCS, vol. 9351, pp. 234–241. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-24574-4_28
Tang, H., Liu, X., Sun, S., Yan, X., Xie, X.: Recurrent mask refinement for few-shot medical image segmentation. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 3918–3928 (2021)
Tang, Y., et al.: Weakly-supervised universal lesion segmentation with regional level set loss. In: de Bruijne, M., et al. (eds.) MICCAI 2021. LNCS, vol. 12902, pp. 515–525. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-87196-3_48
Wang, J., Xia, B.: Bounding box tightness prior for weakly supervised image segmentation. In: de Bruijne, M., et al. (eds.) MICCAI 2021. LNCS, vol. 12902, pp. 526–536. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-87196-3_49
Wang, K., Liew, J.H., Zou, Y., Zhou, D., Feng, J.: PANET: few-shot image semantic segmentation with prototype alignment. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9197–9206 (2019)
Wei, Q., et al.: Learn to segment retinal lesions and beyond. In: 2020 25th International Conference on Pattern Recognition (ICPR), pp. 7403–7410. IEEE (2021)
Yan, Z., Han, X., Wang, C., Qiu, Y., Xiong, Z., Cui, S.: Learning mutually local-global u-nets for high-resolution retinal lesion segmentation in fundus images. In: 2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019), pp. 597–600. IEEE (2019)
Yang, L., et al.: BoxNet: deep learning based biomedical image segmentation using boxes only annotation. arXiv preprint arXiv:1806.00593 (2018)
Yang, Y., Wang, Z., Liu, J., Cheng, K.T., Yang, X.: Label refinement with an iterative generative adversarial network for boosting retinal vessel segmentation. arXiv preprint arXiv:1912.02589 (2019)
Yu, Q., Dang, K., Tajbakhsh, N., Terzopoulos, D., Ding, X.: A location-sensitive local prototype network for few-shot medical image segmentation. In: 2021 IEEE 18th International Symposium on Biomedical Imaging (ISBI), pp. 262–266. IEEE (2021)
Yuan, Y., Chen, X., Wang, J.: Object-contextual representations for semantic segmentation (2020)
Zhang, G., et al.: RefineMask: towards high-quality instance segmentation with fine-grained features. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6861–6869 (2021)
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Yu, Q., Dang, K., Zhou, Z., Chen, Y., Ding, X. (2022). Coarse Retinal Lesion Annotations Refinement via Prototypical Learning. In: Lian, C., Cao, X., Rekik, I., Xu, X., Cui, Z. (eds) Machine Learning in Medical Imaging. MLMI 2022. Lecture Notes in Computer Science, vol 13583. Springer, Cham. https://doi.org/10.1007/978-3-031-21014-3_25
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