Abstract
Optical coherence tomography (OCT) is an important imaging technique in ophthalmology, and accurate detection of retinal lesions plays an important role in computer-aided diagnosis. However, the particularities of retinal lesions, such as their complex appearance and large variation of scale, limit the successful application of conventional deep learning-based object detection networks for OCT lesion detection. In this study, we propose a positive-aware lesion detection network with cross-scale feature pyramid for OCT images. A cross-scale boost module with non-local network is firstly applied to enhance the ability of feature representation for OCT lesions with varying scales. To avoid lesion omission and misdetection, some positive-aware network designs are then added into a two-stage detection network, including global level positive estimation and local level positive mining. Finally, we establish a large OCT dataset with multiple retinal lesions, and perform sufficient comparative experiments on it. The results demonstrate that our proposed network achieves 92.36 mean average precision (mAP) for OCT lesion detection, which is superior to other existing detection approaches.
Dongyi Fan and Chengfen Zhang contribute equally and share the first authorship.
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References
Bourne, R.R.A., Flaxman, S.R., Braithwaite, T., et al.: Magnitude, temporal trends, and projections of the global prevalence of blindness and distance and near vision impairment: a systematic review and meta-analysis. Lancet Glob. Health 5, e888–e897 (2017)
Ting, D.S.W., Peng, L., Varadarajan, A.V., et al.: Deep learning in ophthalmology: the technical and clinical considerations. Progr. Retinal Eye Res. 72, 100759 (2019)
De Fauw, J., Ledsam, J.R., Romera-Paredes, B., et al.: Clinically applicable deep learning for diagnosis and referral in retinal disease. Nat. Med. 24, 1342–1350 (2018)
Fang, L., Wang, C., Li, S., et al.: Attention to lesion: Lesion-aware convolutional neural network for retinal optical coherence tomography image classification. IEEE Trans. Med. Imaging 38, 1959–1970 (2019)
Tennakoon, R., Gostar, A.K., Hoseinnezhad, R., et al.: Retinal fluid segmentation in oct images using adversarial loss based convolutional neural networks. In: 2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018), pp. 1436–1440. IEEE (2018)
Roy, A.G., Conjeti, S., Karri, S.P.K., et al.: ReLayNet: retinal layer and fluid segmentation of macular optical coherence tomography using fully convolutional networks. Biomed. Opt. Express 8, 3627–3642 (2017)
Zhao, Z.Q., Zheng, P., Xu, S., et al.: Object detection with deep learning: a review. IEEE Trans. Neural Netw. Learn. Syst. 30, 3212–3232 (2019)
Lin, T.Y., Dollár, P., Girshick, R., et al.: Feature pyramid networks for object detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2117–2125 (2017)
Ren, S., He, K., Girshick, R., et al.: Faster R-CNN: towards real-time object detection with region proposal networks. In: Advances in Neural Information Processing Systems, pp. 91–99 (2015)
Redmon, J., Farhadi, A.: Yolov3: an incremental improvement. arXiv preprint:1804.02767 (2018)
Ding, J., Li, A., Hu, Z., Wang, L.: Accurate pulmonary nodule detection in computed tomography images using deep convolutional neural networks. In: Descoteaux, M., Maier-Hein, L., Franz, A., Jannin, P., Collins, D.L., Duchesne, S. (eds.) MICCAI 2017. LNCS, vol. 10435, pp. 559–567. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-66179-7_64
Dou, Q., Chen, H., Jin, Y., Lin, H., Qin, J., Heng, P.-A.: Automated pulmonary nodule detection via 3D convnets with online sample filtering and hybrid-loss residual learning. In: Descoteaux, M., Maier-Hein, L., Franz, A., Jannin, P., Collins, D.Louis, Duchesne, S. (eds.) MICCAI 2017. LNCS, vol. 10435, pp. 630–638. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-66179-7_72
Li, Z., Zhang, S., Zhang, J., Huang, K., Wang, Y., Yu, Y.: MVP-Net: multi-view FPN with position-aware attention for deep Universal lesion detection. In: Shen, D., et al. (eds.) MICCAI 2019. LNCS, vol. 11769, pp. 13–21. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-32226-7_2
Shao, Q., Gong, L., Ma, K., Liu, H., Zheng, Y.: Attentive CT lesion detection using deep pyramid inference with multi-scale booster. In: Shen, D., et al. (eds.) MICCAI 2019. LNCS, vol. 11769, pp. 301–309. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-32226-7_34
Wang, X., Girshick, R., Gupta, A., et al.: Non-local neural networks. In: Proceedings of the IEEE conference on Computer Vision and Pattern Recognition, 7794–7803 (2018)
Zhou, X., Yao, C., Wen, H., et al.: EAST: an efficient and accurate scene text detector. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5551–5560 (2017)
Russakovsky, O., Deng, J., Su, H., et al.: Imagenet large scale visual recognition challenge. Int. J. Comput. Vis. 115, 211–252 (2015)
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Fan, D. et al. (2020). Positive-Aware Lesion Detection Network with Cross-scale Feature Pyramid for OCT Images. In: Martel, A.L., et al. Medical Image Computing and Computer Assisted Intervention – MICCAI 2020. MICCAI 2020. Lecture Notes in Computer Science(), vol 12265. Springer, Cham. https://doi.org/10.1007/978-3-030-59722-1_66
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