A deep learning-based precision and automatic kidney segmentation system using efficient feature pyramid networks in computed tomography images. (June 2022) - Hsiao, Chiu-Han; Lin, Ping-Cherng; Chung, Li-An; Lin, Frank Yeong-Sung; Yang, Feng-Jung; Yang, Shao-Yu; Wu, Chih-Horng; Huang, Yennun; Sun, Tzu-Lung
Highlights: An lightweight 2D encoder-decoder architecture is proposed for kidney segmentation in abdomen CT images. An optimized framework is proposed to determine the appropriate hyper-parameters such as the selection of loss function, windowing method, and augmentation method. The deep learning model is evaluated on the KiTS19 database and achieved a Dice score of 0.969. Abstract: This paper proposes an encoder-decoder architecture for kidney segmentation. A hyperparameter optimization process is implemented, including the development of a model architecture, selecting a windowing method and a loss function, and data augmentation. The model consists of EfficientNet-B5 as the encoder and a feature pyramid network as the decoder that yields the best performance with a Dice score of 0.969 on the 2019 Kidney and Kidney Tumor Segmentation Challenge dataset. The proposed model is tested with different voxel spacing, anatomical planes, and kidney and tumor volumes. Moreover, case studies are conducted to analyze segmentation outliers. Finally, five-fold cross-validation and the 3D-IRCAD-01 dataset are used to evaluate the developed model in terms of the following evaluation metrics: the Dice score, recall, precision, and the Intersection over Union score. A new development and application of artificial intelligence algorithms to solve image analysis and interpretation will be demonstrated in this paper. Overall, our experiment results show that the proposed kidney segmentation solutions in CT images can be significantly applied to clinical needs to assist surgeons in surgical planning. It enables the calculation of the total kidney volume for kidney function estimation in ADPKD and supports radiologists or doctors in disease diagnoses and disease progression.