Abstract
Accurate segmentation of punctate white matter lesion (PWML) in infantile brains by an automatic algorithm can reduce the potential risk of postnatal development. How to segment PWML effectively has become one of the active topics in medical image segmentation in recent years. In this paper, we construct an efficient two-stage PWML semantic segmentation network based on the characteristics of the lesion, called refined segmentation R-CNN (RS R-CNN). We propose a heuristic RPN (H-RPN) which can utilize surrounding information around the PWML for heuristic segmentation. Also, we design a lightweight segmentation network to segment the lesion in a fast way. Densely connected conditional random field (DCRF) is used to optimize the segmentation results. We only use T1w MRIs to segment PWMLs. The result shows that the lesion of ordinary size or even pixel size can be well segmented by our model. The Dice similarity coefficient reaches 0.6616, the sensitivity is 0.7069, the specificity is 0.9997, and the Hausdorff distance is 52.9130. The proposed method outperforms the state-of-the-art algorithm. (The code of this paper is available on https://github.com/YalongLiu/Refined-Segmentation-R-CNN).
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References
Tortora, D., Panara, V., Mattei, P.A., et al.: Comparing 3T T1-weighted sequences in identifying hyperintense punctate lesions in preterm neonates. Am. J. Neuroradiol. 36(3), 581–586 (2015)
Kersbergen, K.J., Benders, M.J., Groenendaal, F., et al.: Different patterns of punctate white matter lesions in serially scanned preterm infants. PLoS ONE 9(10), e108904 (2014)
Li, X., et al.: Characterization of extensive microstructural variations associated with punctate white matter lesions in preterm neonates. Am. J. Neuroradiol. 38(6), 1228–1234 (2017)
Cheng, I., et al.: White matter injury detection in neonatal MRI. In: Proceedings of the International Society for Optical Engineering, vol. 8670, pp. 86702L. SPIE, Florida (2013)
Cheng, I., Miller, S.P., Duerden, E.G., et al.: Stochastic process for white matter injury detection in preterm neonates. NeuroImage Clin. 7, 622–630 (2015)
Mukherjee, S., Cheng, I., Miller, S., et al.: A fast segmentation-free fully automated approach to white matter injury detection in preterm infants. Med. Biol. Eng. Comput. 57(1), 71–87 (2019)
Milletari, F., Ahmadi, S.A., Kroll, C., et al.: Hough-CNN: deep learning for segmentation of deep brain regions in MRI and ultrasound. Comput. Vis. Image Underst. 164, 92–102 (2017)
Ghafoorian, M., et al.: Transfer learning for domain adaptation in MRI: application in brain lesion segmentation. In: Descoteaux, M., Maier-Hein, L., Franz, A., Jannin, P., Collins, D.Louis, Duchesne, S. (eds.) MICCAI 2017. LNCS, vol. 10435, pp. 516–524. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-66179-7_59
He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask R-CNN. In: International Conference on Computer Vision, pp. 2980–2988 (2017)
Ren, S., He, K., Girshick, R., Sun, J.: Faster R-CNN: towards real-time object detection with region proposal networks. In: NIPS, pp. 91–99 (2015)
He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: IEEE Computer Vision Pattern Recognition, pp. 770–778 (2016)
Lin, T. Y., Dollár, P., Girshick, R., He, K., Hariharan, B., Belongie, S.: Feature pyramid networks for object detection. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2117–2125 (2017)
Krähenbühl, P., Koltun, V.: Efficient inference in fully connected CRFs with gaussian edge potentials. In: NIPS, pp. 1–9 (2012)
Chen, L.C., Papandreou, G., Kokkinos, I., et al.: Deeplab: semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected CRFs. IEEE Trans. Pattern Anal. Mach. Intell. 40(4), 834–848 (2018)
Kamnitsas, K., Ledig, C., Newcombe, V.F., et al.: Efficient multi-scale 3D CNN with fully connected CRF for accurate brain lesion segmentation. Med. Image Anal. 36, 61–78 (2017)
Snoek, J., Larochelle, H., Adams, R.P.: Practical bayesian optimization of machine learning algorithms. In: Advances in neural information processing systems, pp. 2951–2959 (2012)
Acknowledgements
Yalong Liu, Miaomiao Wang, Xianjun Li contributed equally to this work. Please address correspondence to Jian Yang (yj1118@mail.xjtu.edu.cn); and Xingbo Gao (xbgao@ieee.org). This work was supported in part by the National Natural Science Foundation of China under Grant 61671339, 61432014 and 61772402, and in part by National High-Level Talents Special Support Program of China under Grant CS31117200001.
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Liu, Y. et al. (2019). Refined Segmentation R-CNN: A Two-Stage Convolutional Neural Network for Punctate White Matter Lesion Segmentation in Preterm Infants. In: Shen, D., et al. Medical Image Computing and Computer Assisted Intervention – MICCAI 2019. MICCAI 2019. Lecture Notes in Computer Science(), vol 11766. Springer, Cham. https://doi.org/10.1007/978-3-030-32248-9_22
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DOI: https://doi.org/10.1007/978-3-030-32248-9_22
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