Computed tomography image segmentation of renal tumors based on deep learning

The incidence of renal tumors continues to rise each year, posing a serious threat to human health. Accurate segmentation of lesions is crucial for effective treatment. To enhance the segmentation performance of kidneys and renal tumors in CT images, this paper proposes a deep learning-based segmentation framework. The framework adopts a two-stage approach, starting from rough segmentation and progressing to fine segmentation, utilizing deep learning techniques. In the rough segmentation stage, a prior contour-assisted training technique is employed to extract the region of interest, namely kidneys and renal tumors. In the fine segmentation stage, an improved 3D convolution-based U-net model is proposed. Additionally, a novel loss function incorporating the mean and variance of pixel values of kidneys and renal tumors is introduced for fine-tuning. Given the limited data available, abdominal dataset images are used for pre-training the model. Through transfer learning, the model can learn common features from abdominal images.