Kidney and Kidney Tumour Segmentation from 3D CT Scan using DeepLabv3+

Segmenting kidney and kidney tumour from CT scan is crucial in combating challenges in the early detection of kidney cancer. Several segmentation methods are available to segment kidney and kidney tumour from 3D CT scan. However, these methods pose several drawbacks, including dependency on pixel-wise classification, limited generalisation, and manual annotation requirements. Hence, this paper introduces a novel kidney and kidney tumour segmentation approach employing encoder-decoder-based architecture. The proposed segmentation approach is assessed against two encoder-decoder-based architectures, namely U-Net and DeepLabv3+. The proposed approach precisely identifies kidney and kidney tumour in a 3D CT scan. Its performance is analysed using the 2023 Kidney and Kidney Tumour Segmentation Challenge (KiTS23) dataset. The evaluation metrics such as dice coefficient and Intersection over Union (IoU) are used to assess the performance. Our results on the KiTS23 dataset show that DeepLabv3+ outperforms U-Net. Thus, the paper discusses the DeepLabv3+ approach in detail. DeepLabv3+ boasts an average improvement of 0.82% in dice coefficient, 1.60% in IoU, 39.28% in loss during training, and 0.94% in dice coefficient, 1.82% in IoU, 44.88% in loss during validation over U-Net.