Computed tomography image segmentation of renal tumors based on deep learning

Li Kang; Xinxin Song; Zhijian Gao

doi:10.1117/12.3023762

14 March 2024 Computed tomography image segmentation of renal tumors based on deep learning

Li Kang, Xinxin Song, Zhijian Gao

Proceedings Volume 13074, Fifth International Conference on Image, Video Processing, and Artificial Intelligence (IVPAI 2023); 1307405 (2024) https://doi.org/10.1117/12.3023762
Event: Fifth International Conference on Image, Video Processing and Artificial Intelligence, 2023, Shenzhen, China

Abstract

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.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Li Kang, Xinxin Song, and Zhijian Gao "Computed tomography image segmentation of renal tumors based on deep learning", Proc. SPIE 13074, Fifth International Conference on Image, Video Processing, and Artificial Intelligence (IVPAI 2023), 1307405 (14 March 2024); https://doi.org/10.1117/12.3023762

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