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Automatic Kidney Volume Estimation System Using Transfer Learning Techniques

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Advanced Information Networking and Applications (AINA 2021)

Abstract

Deep learning technology is widely used in medicine. The automation of medical image classification and segmentation is essential and inevitable. This study proposes a transfer learning–based kidney segmentation model with an encoder–decoder architecture. Transfer learning was introduced through the utilization of the parameters from other organ segmentation models as the initial input parameters. The results indicated that the transfer learning–based method outperforms the single-organ segmentation model. Experiments with different encoders, such as ResNet-50 and VGG-16, were implemented under the same Unet structure. The proposed method using transfer learning under the ResNet-50 encoder achieved the best Dice score of 0.9689. The proposed model’s use of two public data sets from online competitions means that it requires fewer computing resources. The difference in Dice scores between our model and 3D Unet (Isensee) was less than 1%. The average difference between the estimated kidney volume and the ground truth was only 1.4%, reflecting a seven times higher accuracy than that of conventional kidney volume estimation in clinical medicine.

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References

  1. Tangri, N., Hougen, I., Alam, A., Perrone, R., McFarlane, P., Pei, Y.: Total kidney volume as a biomarker of disease progression in autosomal dominant polycystic kidney disease. Can. J. Kidney Health Dis. 4, 1–6 (2017)

    Article  Google Scholar 

  2. Levy, M., Feingold, J.: Estimating prevalence in single-gene kidney diseases progressing to renal failure. Kidney Int. 58(3), 925–943 (2000)

    Article  Google Scholar 

  3. Grantham, J., Torres, V.: The importance of total kidney volume in evaluating progression of polycystic kidney disease. Nat. Rev. Nephrol. 12, 667–678 (2016)

    Article  Google Scholar 

  4. Chapman, A.B., Wei, W.: Imaging approaches to patients with polycystic kidney disease. Semin. Nephrol. 31(3), 237–244 (2011)

    Article  Google Scholar 

  5. Chagot, L., et al.: Clinical kidney volume measurement accuracy using NEFROVOL. In: 2018 IEEE International Symposium on Medical Measurements and Applications (MeMeA), pp. 1–6 (2018)

    Google Scholar 

  6. Kaur, R., Juneja, M.: A survey of kidney segmentation techniques in CT images. Curr. Med. Imaging Rev. 14(2), 238–250 (2018)

    Article  Google Scholar 

  7. Sharma, K.: Automatic segmentation of kidneys using deep learning for total kidney volume quantification in autosomal dominant polycystic kidney disease. Sci. Rep. 7(2049), 1–10 (2017)

    Google Scholar 

  8. Bazgir, O., Barck, K., Carano, R.A.D., Weimer, R.M., Xie, L.: Kidney segmentation using 3D U-Net localized with expectation maximization. In: 2020 IEEE Southwest Symposium on Image Analysis and Interpretation (SSIAI), pp. 22–25 (2020)

    Google Scholar 

  9. Isensee, F., Maier-Hein, K.H.: An attempt at beating the 3D U-net. arXiv:1908.02182

  10. KiTS19 Challenge Homepage (2019). https://kits19.grand-challenge.org/. Accessed on 14 Jan 2021

  11. Liver Tumor Segmentation Challenge (2017). https://competitions.codalab.org/competitions/17094. Accessed 14 Jan 2021

  12. Kruthiventi, S.S.S., Ayush, K., Babu, R.V.: DeepFix: a fully convolutional neural network for predicting human eye fixations. IEEE Trans. Image Process. 26(9), 4446–4456 (2017)

    Article  MathSciNet  Google Scholar 

  13. Li, X., Morgan, P., Ashburner, J., Smith, J., Rorden, C.: The first step for neuroimaging data analysis: DICOM to NifTI conversion. Neurosci. Methods 264, 47–56 (2016)

    Article  Google Scholar 

  14. About DICOM: Overview (2021). https://www.dicomstandard.org/about. Accessed14 Jan 2021

  15. Alomari, R.S., Kompalli, S., Chaudhary, V.: Segmentation of the liver from abdominal CT using Markov random field model and GVF snakes. In: 2008 International Conference on Complex, Intelligent and Software Intensive Systems (CISIS), pp. 293–298 (2008)

    Google Scholar 

  16. Irazabal, M.V., et al.: Imaging classification of autosomal dominant polycystic kidney disease: a simple model for selecting patients for clinical trials. J. Am. Soc. Nephrol. 26, 161–187 (2015)

    Article  Google Scholar 

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Acknowledgement

This work was supported in part by Ministry of Science and Technology (MOST), Taiwan, under Grant Number MOST 109-2221-E-002-144.

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Correspondence to Chiu-Han Hsiao .

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Hsiao, CH. et al. (2021). Automatic Kidney Volume Estimation System Using Transfer Learning Techniques. In: Barolli, L., Woungang, I., Enokido, T. (eds) Advanced Information Networking and Applications. AINA 2021. Lecture Notes in Networks and Systems, vol 226. Springer, Cham. https://doi.org/10.1007/978-3-030-75075-6_30

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