Abstract
Morphological changes in the glomerulus play a vital role in the diagnosis of kidney diseases. However, the detection of the glomerulus in the actual medical situation is challenging due to various factors such as lesions, tissue changes, and staining. These factors raise problems like high foreground-background similarity, blurred contours, and irregular shapes, thus pose difficulties for both physicians and Automatic computer detection. To address these challenges, we propose a foreground-aware feature extraction method, which is used to fully extract foreground information. Furthermore, we design the Foreground and Shape Joint Perception Network (FSJP-Net), a detection network that integrates object foreground information and shapes information, which improves the recall and precision of glomerular detection by fusing the extracted foreground and elliptical shape information from different feature extraction branches. The experiments demonstrate the effectiveness and superiority of our proposed method in detecting various categories of glomeruli.
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References
Haas, M., et al.: A multicenter study of the predictive value of crescents in IGA nephropathy. J. Am. Soc. Nephrol. 28(2), 691–701 (2017)
Farris, A.B., et al.: Morphometric and visual evaluation of fibrosis in renal biopsies. J. Am. Soc. Nephrol. 22(1), 176–186 (2011)
D’Agati, V.D., Kaskel, F.J., Falk, R.J.: Focal segmental glomerulosclerosis. N. Engl. J. Med. 365(25), 2398–2411 (2011)
Nyengaard, J., Bendtsen, T.: Glomerular number and size in relation to age, kidney weight, and body surface in normal man. Anat. Rec. 232(2), 194–201 (1992)
Puelles, V.G., Hoy, W.E., Hughson, M.D., Diouf, B., Douglas-Denton, R.N., Bertram, J.F.: Glomerular number and size variability and risk for kidney disease. Curr. Opin. Nephrol. Hypertens. 20(1), 7–15 (2011)
Kato, T., et al.: Segmental hog: new descriptor for glomerulus detection in kidney microscopy image. BMC Bioinform. 16, 1–16 (2015)
Simon, O., Yacoub, R., Jain, S., Tomaszewski, J.E., Sarder, P.: Multi-radial LBP features as a tool for rapid glomerular detection and assessment in whole slide histopathology images. Sci. Rep. 8(1), 2032 (2018)
Ginley, B., Tomaszewski, J.E., Yacoub, R., Chen, F., Sarder, P.: Unsupervised labeling of glomerular boundaries using Gabor filters and statistical testing in renal histology. J. Med. Imaging 4(2), 021102–021102 (2017)
Zhang, Y., et al.: U-net-and-a-half: convolutional network for biomedical image segmentation using multiple expert-driven annotations. arXiv preprint arXiv:2108.04658 (2021)
Gallego, J., et al.: Glomerulus classification and detection based on convolutional neural networks. J. Imaging 4(1), 20 (2018)
Wilbur, D.C., Smith, M.L., Cornell, L.D., Andryushkin, A., Pettus, J.R.: Automated identification of glomeruli and synchronised review of special stains in renal biopsies by machine learning and slide registration: a cross-institutional study. Histopathology 79(4), 499–508 (2021)
Kawazoe, Y., et al.: Faster R-CNN-based glomerular detection in multistained human whole slide images. J. Imaging 4(7), 91 (2018)
Heckenauer, R., et al.: Real-time detection of glomeruli in renal pathology. In: 2020 IEEE 33rd International Symposium on Computer-Based Medical Systems (CBMS), pp. 350–355. IEEE (2020)
Weis, C.A., et al.: Assessment of glomerular morphological patterns by deep learning algorithms. J. Nephrol. 35(2), 417–427 (2022)
Yao, X., Wang, X., Karaca, Y., Xie, J., Wang, S.: Glomerulus classification via an improved Googlenet. IEEE Access 8, 176916–176923 (2020)
Yang, H., et al.: Circlenet: anchor-free detection with circle representation. arXiv preprint arXiv:2006.02474 (2020)
Zhou, X., Wang, D., Krähenbühl, P.: Objects as points. arxiv 2019. arXiv preprint arXiv:1904.07850 (2019)
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)
Zheng, Z., Zhong, Y., Wang, J., Ma, A.: Foreground-aware relation network for geospatial object segmentation in high spatial resolution remote sensing imagery. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 4096–4105 (2020)
Chen, J., Zhang, Y., Wang, J., Zhou, X., He, Y., Zhang, T.: EllipseNet: anchor-free ellipse detection for automatic cardiac biometrics in fetal echocardiography. In: de Bruijne, M., et al. (eds.) MICCAI 2021. LNCS, vol. 12907, pp. 218–227. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-87234-2_21
Wang, T., Lu, C., Shao, M., Yuan, X., Xia, S.: Eldet: an anchor-free general ellipse object detector. In: Proceedings of the Asian Conference on Computer Vision, pp. 2580–2595 (2022)
Zhu, X., Lyu, S., Wang, X., Zhao, Q.: Tph-yolov5: improved yolov5 based on transformer prediction head for object detection on drone-captured scenarios. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 2778–2788 (2021)
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Han, Q., Hu, X., Chen, P., Xia, S. (2023). FSJP-Net: Foreground and Shape Joint Perception Network for Glomerulus Detection. In: Wachinger, C., Paniagua, B., Elhabian, S., Li, J., Egger, J. (eds) Shape in Medical Imaging. ShapeMI 2023. Lecture Notes in Computer Science, vol 14350. Springer, Cham. https://doi.org/10.1007/978-3-031-46914-5_12
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