Abstract
Circle representation has recently been introduced as a “medical imaging optimized" representation for more effective instance object detection on ball-shaped medical objects. With its superior performance on instance detection, it is appealing to extend the circle representation to instance medical object segmentation. In this work, we propose CircleSnake, a simple end-to-end circle contour deformation-based segmentation method for ball-shaped medical objects. Compared to the prevalent DeepSnake method, our contribution is threefold: (1) We replace the complicated bounding box to octagon contour transformation with a computation-free and consistent bounding circle to circle contour adaption for segmenting ball-shaped medical objects; (2) Circle representation has fewer degrees of freedom (DoF = 2) as compared with the octagon representation (DoF = 8), thus yielding a more robust segmentation performance and better rotation consistency; (3) To the best of our knowledge, the proposed CircleSnake method is the first end-to-end circle representation deep segmentation pipeline method with consistent circle detection, circle contour proposal, and circular convolution. The key innovation is to integrate the circular graph convolution with circle detection into an end-to-end instance segmentation framework, enabled by the proposed simple and consistent circle contour representation. Glomeruli are used to evaluate the performance of the benchmarks. From the results, CircleSnake increases the average precision of glomerular detection from 0.559 to 0.614. The Dice score increased from 0.804 to 0.849. The code has been released: .
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Bueno, G., Fernandez-Carrobles, M.M., Gonzalez-Lopez, L., Deniz, O.: Glomerulosclerosis identification in whole slide images using semantic segmentation. Comput. Methods Programs Biomed. 184, 105273 (2020)
D’Agati, V.D., Mengel, M.: The rise of renal pathology in nephrology: structure illuminates function. Am. J. Kidney Dis. 61(6), 1016–1025 (2013)
Gadermayr, M., Dombrowski, A.K., Klinkhammer, B.M., Boor, P., Merhof, D.: CNN cascades for segmenting whole slide images of the kidney. arXiv preprint arXiv:1708.00251 (2017)
Ginley, B., Lutnick, B., Jen, K.Y., Fogo, A.B., Jain, S., Rosenberg, A., Walavalkar, V., Wilding, G., Tomaszewski, J.E., Yacoub, R., et al.: Computational segmentation and classification of diabetic glomerulosclerosis. J. Am. Soc. Nephrol. 30(10), 1953–1967 (2019)
He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask R-CNN. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017)
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)
Huo, Y., Deng, R., Liu, Q., Fogo, A.B., Yang, H.: Ai applications in renal pathology. Kidney International (2021)
Kannan, S., Morgan, L.A., Liang, B., Cheung, M.G., Lin, C.Q., Mun, D., Nader, R.G., Belghasem, M.E., Henderson, J.M., Francis, J.M., et al.: Segmentation of glomeruli within trichrome images using deep learning. Kidney international reports 4(7), 955–962 (2019)
Kawazoe, Y.: Faster R-CNN-based glomerular detection in multistained human whole slide images. J. Imaging 4(7), 91 (2018)
Law, Hei, Deng, Jia: CornerNet: Detecting Objects as Paired Keypoints. Int. J. Comput. Vis. 128(3), 642–656 (2019). https://doi.org/10.1007/s11263-019-01204-1
Lin, T.Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE international conference on computer vision, pp. 2980–2988 (2017)
Lin, T.-Y., et al.: Microsoft COCO: Common Objects in Context. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014. LNCS, vol. 8693, pp. 740–755. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-10602-1_48
Lo, Y.-C.: Glomerulus Detection on Light Microscopic Images of Renal Pathology with the Faster R-CNN. In: Cheng, Long, Leung, Andrew Chi Sing., Ozawa, Seiichi (eds.) ICONIP 2018. LNCS, vol. 11307, pp. 369–377. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-04239-4_33
Luo, X., et al.: SCPM-net: An anchor-free 3D lung nodule detection network using sphere representation and center points matching. arXiv preprint arXiv:2104.05215 (2021)
Nguyen, E.H., et al.: Circle representation for medical object detection. IEEE Transactions on Medical Imaging (2021)
Peng, S., Jiang, W., Pi, H., Li, X., Bao, H., Zhou, X.: Deep snake for real-time instance segmentation. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 8533–8542 (2020)
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)
Rehem, J.M.C., Dos Santos, W.L.C., Duarte, A.A., De Oliveira, L.R., Angelo, M.F.: Automatic glomerulus detection in renal histological images. In: Medical Imaging 2021: Digital Pathology, vol. 11603, p. 116030K. International Society for Optics and Photonics (2021)
Yang, H., et al.: Circle Net: Anchor-Free Glomerulus Detection with Circle Representation. In: Martel, A.L., et al. (eds.) MICCAI 2020. LNCS, vol. 12264, pp. 35–44. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-59719-1_4
Yu, F., Wang, D., Shelhamer, E., Darrell, T.: Deep layer aggregation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2403–2412 (2018)
Zhou, X., Wang, D., Krähenbühl, P.: Objects as points. arXiv preprint arXiv:1904.07850 (2019)
Acknowledgements
This work was supported by NIH NIDDK DK56942(ABF).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 Springer Nature Switzerland AG
About this paper
Cite this paper
Nguyen, E.H., Yang, H., Asad, Z., Deng, R., Fogo, A.B., Huo, Y. (2022). CircleSnake: Instance Segmentation with Circle Representation. 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_31
Download citation
DOI: https://doi.org/10.1007/978-3-031-21014-3_31
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-21013-6
Online ISBN: 978-3-031-21014-3
eBook Packages: Computer ScienceComputer Science (R0)