Automated Kidney MRI Segmentation using connected U-Net Architecture
DOI:
https://doi.org/10.52783/jns.v14.2253Keywords:
Magnetic Resonance Imaging, Kidney Segmentation, Deep Learning, Convolution Neural Networks, U-NetAbstract
Polycystic kidney syndrome, is a genetic disease in which the renal tubules become structurally abnormal, resulting in the outgrowth of multiple cysts and a decline in renal function. Thus, a crucial step in enhancing the quality of life for cancer patients is treatment planning. Although Magnetic Resonance Imaging (MRI) is a popular imaging method for evaluating these tumours, the volume of data it generates makes it difficult to manually segment the images in a reasonable length of time, which restricts the use of precise quantitative assessments in clinical practise. The enormous spatial and structural heterogeneity among brain tumours makes automatic segmentation a difficult task, hence effective and automatic segmentation methods are needed. UNet and its variants are one of the most advanced models for medical image segmentation, and they performed well on MRI images. So, in this paper we designed an automatic Kidney segmentation approach using Connected-UNets, which connects two UNets using additional modified skip connections. To highlight the contextual information within the encoder-decoder network design, we integrate Atrous Spatial Pyramid Pooling (ASPP) in the two conventional UNets. We also apply the proposed architecture on the Attention UNet (AUNet) and the Residual UNet (ResUNet). To evaluate the proposed model the dataset obtained from Myo clinic was considered. The experimental results give Dice Coefficient for the three architectures Connected UNets, Connected AUNets, and Connected ResUNets as 93.36%, 93.52 %, and 94.13%, and IoU score as 85.75%, 86.01%, and 87.63% respectively.
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B. Bikbov, C.A. Purcell, A.S. Levey, M. Smith, A. Abdoli, M. Abebe, O.M. Adebayo,M. Afarideh, S.K. Agarwal, M. Agudelo-Botero, et al., Global, regional, andnational burden of chronic kidney disease, 1990–2017: a systematic analysis for the global burden of disease study 2017, The Lancet 395 (10225) (2020) 709–733, doi: 10.1016/S0140- 6736(20)30045- 3 .
Global Cancer Observatory, Cancer today, ( https://gco.iarc.fr/today/home ), (accessed 13 March 2021). 2021
M. Levy, J. Feingold, Estimating prevalence in single-gene kidney diseases progressing to renal failure, Kidney Int. 58 (3) (20 0 0) 925–943, doi: 10.1046/j. 1523-1755.20 0 0.0 0250.x.
R. Magistroni, C. Corsi, T. Martí, R. Torra, A review of the imaging techniques for measuring kidney and cyst volume in establishing autosomal dominant polycystic kidney disease progression, Am. J. Nephrol. 48 (1) (2018) 67–78, doi: 10.1159/0 0 0491022.
S. Tokiwa, S. Muto, T. China, S. Horie, The relationship between renal volume and renal function in autosomal dominant polycystic kidney disease, Clin. Exp. Nephrol. 15 (4) (2011) 539–545, doi: 10.1007/s10157-011-0428-y.
G. Yang, G. Li, T. Pan, Y. Kong, J. Wu, H. Shu, L. Luo, J. Dillenseger, J. Coatrieux, L. Tang, X. Zhu, Automatic segmentation of kidney and renal tumor in ct images based on 3d fully convolutional neural network with pyramid pooling module, in: 2018 24th International Conference on Pattern Recognition (ICPR), 2018, pp. 3790–3795, doi: 10.1109/ICPR.2018.8545143.
R. Cuingnet, R. Prevost, D. Lesage, L.D. Cohen, B. Mory, R. Ardon, Automatic detection and segmentation of kidneys in 3d ct images using random forests, in: N. Ayache, H. Delingette, P. Golland, K. Mori (Eds.), Medical Image Computing and Computer-Assisted Intervention – MICCAI 2012, 2012, pp. 66–74, doi: 10.1007/978- 3- 642- 33454- 2 _ 9.
F. Khalifa, A. Soliman, A.C. Dwyer, G. Gimel’farb, A. El-Baz, A random forest-based framework for 3d kidney segmentation from dynamic contrastenhanced ct images, in: 2016 IEEE International Conference on Image Processing (ICIP), 2016, pp. 3399–3403, doi: 10.1109/ICIP.2016.7532990.
W. Zhao, D. Jiang, J. Peña Queralta, T. Westerlund, Mss u-net: 3d segmentation of kidneys and tumors from ct images with a multi-scale supervised u-net, Inf. Med. Unlocked 19 (2020) 100357–100368, doi: 10.1016/j.imu.2020.100357.
X. Hou, C. Xie, F. Li, J. Wang, C. Lv, G. Xie, Y. Nan, A triple-stage self-guided network for kidney tumor segmentation, in: 2020 IEEE 17th International Symposium on Biomedical Imaging (ISBI), 2020, pp. 341–344, doi: 10.1109/ISBI45749. 2020.9098609.
F. Türk, M. Lüy, N. Barıs¸ ç ı, Kidney and renal tumor segmentation using a hybrid v-net-based model, Mathematics 8 (10) (2020) 1772, doi: 10.3390/ math8101772.
O. Ronneberger, P. Fischer, T. Brox, U-Net: convolutional networks for biomedical image segmentation, in: International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI), 2015, pp. 234–241, doi: 10.1007/978- 3- 319- 24574- 4 _ 28 .
F. Isensee, K.H. Maier-Hein, An Attempt at Beating the 3D U-Net, 2019,
arXiv: 1908.02182 .
X. Hou, C. Xie, F. Li, J. Wang, C. Lv, G. Xie, Y. Nan, A triple-stage self-guided network for kidney tumor segmentation, in: 2020 IEEE 17th International Symposium on Biomedical Imaging (ISBI), 2020, pp. 341–344, doi: 10.1109/ISBI45749. 2020.9098609 .
F. Milletari, N. Navab, S.A. Ahmadi, V-Net: fully convolutional neural networks for volumetric medical image segmentationn, in: 2016 Fourth International Conference on 3D Vision (3DV), 2016, pp. 565–571, doi: 10.1109/3DV.2016.79 . G. Mu, Z. Lin, M. Han, G. Yao, Y. Gao, Segmentation of kidney tumor by multi-resolution VB-nets, 2019, http://results.kits-challenge.org/miccai2019/manuscripts/gr _ 6e.pdf .
X.F. Xi, L. Wang, V.S. Sheng, Z. Cui, B. Fu, F. Hu, Cascade U-ResNets for simultaneous liver and lesion segmentation, IEEE Access 8 (2020) 6 8944–6 8952, doi: 10.1109/ACCESS.2020.2985671 .
M.A. Hussain, G. Hamarneh, R. Garbi, Cascaded regression neural nets for kidney localization and segmentation-free volume estimation, IEEE Trans. Med. Imaging 40 (6) (2021) 1555–1567, doi: 10.1109/TMI.2021.3060465 .
B. Baheti, S. Innani, S. Gajre, S. Talbar, Eff-UNet: a novel architecture for semantic segmentation in unstructured environment, in: 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), 2020, pp. 1473–1481, doi: 10.1109/CVPRW50498.2020.00187 .
L.T.T. Hong, N.C. Thanh, T.Q. Long, Polyp segmentation in colonoscopy images using ensembles of U-Nets with EfficientNet and asymmetric similarity loss function, in: 2020 RIVF International Conference on Computing and Communication Technologies (RIVF), 2020, pp. 1–6, doi: 10.1109/RIVF4 86 85.2020. 9140793 .
Z. Zhou, M.M.R. Siddiquee, N. Tajbakhsh, J. Liang, UNet++: redesigning skip connections to exploit multiscale features in image segmentation, IEEE Trans. Med. Imaging 39 (6) (2020) 1856–1867, doi: 10.1109/TMI.2019.2959609 .
X. Yan, K. Yuan, W. Zhao, S. Wang, Z. Li, S. Cui, An efficient hybrid model for kidney tumor segmentation in CT images, in: 2020 IEEE 17th International Symposium on Biomedical Imaging (ISBI), 2020, pp. 333–336, doi: 10. 1109/ISBI45749.2020.9098325 .
LiverTumorSegmentationChallenge, 2017, ( https://competitions.codalab.org/ competitions/17094 ). Accessed: 2021-01-14.
European Medicines Agency (EMA). Draft qualification opinion: total kidney volume (TKV) as a prognostic biomarker for use in clinical trials evaluating patients with autosomal dominant polycystic kidney disease (ADPKD). EMA website.www.ema.europa. u/docs/en_GB/document_library/Regulatory_and_ procedural_guideline/2015/11/WC500196569.pdf. Published 2015. Accessed May 12, 2016
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