Computation of Total Kidney Volume from CT Images in Autosomal Dominant Polycystic Kidney Disease Using Multi-task 3D Convolutional Neural Networks
Autosomal dominant polycystic kidney disease (ADPKD) characterized by progressive growth of renal cysts is the most prevalent and potentially lethal monogenic renal disease, affecting one in every 500–1000 people. Total Kidney Volume (TKV) and its growth computed from Computed Tomography images has been accepted as an essential prognostic marker for renal function loss. Due to large variation in shape and size of kidney in ADPKD, existing methods to compute TKV (i.e. to segment ADKP) including those based on 2D convolutional neural networks are not accurate enough to be directly useful in clinical practice. In this work, we propose multi-task 3D Convolutional Neural Networks to segment ADPK and achieve a mean DICE score of 0.95 and mean absolute percentage TKV error of 3.86%. Additionally, to solve the challenge of class imbalance, we propose to simply bootstrap cross entropy loss and compare results with recently prevalent dice loss in medical image segmentation community.
KeywordsAutosomal dominant polycystic kidney disease (ADKPD) Multi-task learning 3D fully convolutional network (3D FCN)
We acknowledge using Reedbush-L (SGI Rackable C2112-4GP3/C1102-GP8) HPC system in the Information Technology Center, The University of Tokyo for GPU computational resources used in this work.
- 2.Gansevoort, R.T., Arici, M., Benzing, T.: Recommendations for the use of tolvaptan in autosomal dominant polycystic kidney disease: a position statement on behalf of the ERA-EDTA working groups on inherited kidney disorders and european renal best practice. Nephrol. Dial. Transpl. 31(3), 337–348 (2016)CrossRefGoogle Scholar
- 3.NIH homepage: https://ghr.nlm.nih.gov/condition/polycystic-kidney-disease#statistics. Accessed 03 Feb 2017
- 5.Çiçek, Ö., Abdulkadir, A., Lienkamp, S.S., Brox, T., Ronneberger, O.: 3D U-Net: Learning Dense Volumetric Segmentation from Sparse Annotation. In: Ourselin, S., Joskowicz, L., Sabuncu, M.R., Unal, Gozde, Wells, W. (eds.) MICCAI 2016. LNCS, vol. 9901, pp. 424–432. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46723-8_49CrossRefGoogle Scholar
- 6.Milletari, F., Navab, N., Ahmadi, S.A.: V-net: Fully convolutional neural networks for volumetric medical image segmentation. In: Fourth International Conference on 3D Vision (3DV), 2016, pp. 565–571. IEEE (2016)Google Scholar
- 7.Moeskops, P., et al.: Deep Learning for Multi-task Medical Image Segmentation in Multiple Modalities. In: Ourselin, S., Joskowicz, L., Sabuncu, M.R., Unal, G., Wells, W. (eds.) MICCAI 2016. LNCS, vol. 9901, pp. 478–486. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46723-8_55CrossRefGoogle Scholar
- 8.Pohlen, T., Hermans, A., Mathias, M., Leibe, B.: Full-resolution residual networks for semantic segmentation in street scenes. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4151–4160 (2017)Google Scholar