NEPHROBLASTOMA ANALYSIS IN MRI IMAGES
DOI:
https://doi.org/10.5566/ias.2000Keywords:
Continuous Max-Flow, Graph Segmentation, Kernel Induced Space, MRI images, Nephroblastoma, Wilms tumourAbstract
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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