Kidney and Kidney Tumor Segmentation, 2019 (KiTS-19)

Abstract

Computed Tomography (CT) is the most widely used imaging procedure for locating and diagnosing kidney tumors. The standard treatment for kidney tumors is surgical removal. It is important to accurately segment the kidney and its tumor for effective surgical planning. The manual segmentation of kidney tumors is time-consuming and subject to variability between different radiologists. Therefore, automatic semantic segmentation of kidney tumors using deep learning networks has become increasingly popular in the past few years. Automatic segmentation of kidney tumors is a very challenging task due to their morphological heterogenicity. This work provides the application of 3D UNet and 3D SegResNet on KiTS19 challenge data for accurate segmentation of kidney and kidney tumors. An ensembling operation was added in the end to average the predictions of all models. The proposed method is based on the MONAI framework and focuses more on training procedure rather than complex architectural modifications. The models were trained using KiTS19 training set of 210 cases for which ground truth labels were available. The training data was divided into 190:20, for training and validation respectively. We evaluated the performance of our network on KiTS19 official test set and obtained mean dice of 0.8964, 0.9724 kidney dice, and 0.8204. Our approach outperforms many submissions in terms of kidney segmentation and gives promising results for tumor segmentation. We also used a local test set of 90 cases from KiTS21 challenge to check how well our method adopts to a new dataset. It scored a mean dice of 0.9160, kidney dice of 0.9771, and 0.8550 tumor dice. The obtained results on KiTS19 official test set and local test set show that our approach is effective and can be used for organ segmentation.