• Djibril Kaba Department of Computer Science and Technology, University of Bedfordshire, University Square, Luton LU1 3JU, UK
  • Nigel McFarlane Department of Computer Science and Technology, University of Bedfordshire, University Square, Luton LU1 3JU, UK
  • Feng Dong Department of Computer Science and Technology, University of Bedfordshire, University Square, Luton LU1 3JU, UK
  • Norbert Graf Department for pediatric hematology and oncology at Saarland University Hospital, Building 9 66421 Homburg, Germany
  • Xujiong Ye School of Computer Science, University of Lincoln Brayford Pool, Lincoln, LN6 7TS, UK



Continuous Max-Flow, Graph Segmentation, Kernel Induced Space, MRI images, Nephroblastoma, Wilms tumour


The annotation of the tumour from medical scans is a crucial step in nephroblastoma treatment. Therefore, an accurate and reliable segmentation method is needed to facilitate the evaluation and the treatments of the tumour. The proposed method serves this purpose by performing the segmentation of nephroblastoma in MRI scans. The segmentation is performed by adapting and a 2D free hand drawing tool to select a region of interest in the scan slices. Results from 24 patients show a mean root-mean-square error of 0.0481 ± 0.0309, an average Dice coefficient of 0.9060 ± 0.0549 and an average accuracy of 99.59% ± 0.0039. Thus the proposed method demonstrated an effective agreement with manual annotations.


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How to Cite

Kaba, D., McFarlane, N., Dong, F., Graf, N., & Ye, X. (2019). NEPHROBLASTOMA ANALYSIS IN MRI IMAGES. Image Analysis and Stereology, 38(2), 173–183.



Original Research Paper