Semantic Segmentation of Kidney Tumors Using Variants of U-Net Architecture

Geethanjali T M; Minavathi; Dinesh  M S

doi:10.3991/ijoe.v18i10.31347

Semantic Segmentation of Kidney Tumors Using Variants of U-Net Architecture

Authors

Geethanjali T M P E S College of Engineering, Mandya, Karnataka
Minavathi P E S College of Engineering, Mandya
Dinesh M S P E S College of Engineering, Mandya

DOI:

https://doi.org/10.3991/ijoe.v18i10.31347

Keywords:

Kidney-Tumor Segmentation, U-Net architecture, Attention U-Net Architecture, Ensemble, Computed Tomography, Intersection Over Union

Abstract

Kidney Cancer is one of the most prevalent diseases that is more common in men than in women. Detecting kidney tumors at an early stage has been found to increase survival rates of patients. It is therefore important to accurately segment tumors in Computed Tomography(CT) images. To assist in early detection of kidney tumors in CT images, we present a method for segmenting kidney tumors using deep convolutional neural networks. Predicted models using U-Net and Attention U-Net architectures are ensemble for effective tumor segmentation. Experimental and visual results obtained using the KiTS2019 dataset clearly demonstrate the enhanced Intersection Over Union(IoU) score of the ensemble model.

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Published

2022-07-26

How to Cite

T M, G., Minavathi, & M S, D. . (2022). Semantic Segmentation of Kidney Tumors Using Variants of U-Net Architecture. International Journal of Online and Biomedical Engineering (iJOE), 18(10), pp. 143–153. https://doi.org/10.3991/ijoe.v18i10.31347