A novel convolutional neural network for kidney ultrasound images segmentation. (May 2022)

• Record Type: Journal Article
• Authors: Chen, Gongping; Yin, Jingjing; Dai, Yu; Zhang, Jianxun; Yin, Xiaotao; Cui, Liang
• Is Part Of: Computer methods and programs in biomedicine. Volume 218(2022)
• Publication Date: 2022-05
• Subjects: Kidney ultrasound -- Automatic segmentation -- Deep supervised -- Deep learning; Medicine -- Computer programs -- Periodicals; Biology -- Computer programs -- Periodicals; Computers -- Periodicals; Medicine -- Periodicals; Médecine -- Logiciels -- Périodiques; Biologie -- Logiciels -- Périodiques; Biology -- Computer programs; Medicine -- Computer programs; Periodicals; Electronic journals; 610.28
• DOI: 10.1016/j.cmpb.2022.106712
• Languages: English
• ISSNs: 0169-2607

Abstract

..Highlights: First, we construct a multi-scale and deep-supervision architecture to segment kidney from ultrasound images. Second, we design a multi-scale input pyramid with parallel branches to obtain multi-scale feature input. Third, we develop a multi-output supervision module to guide network learns to predict precise and structurally complete segmentations scale-by-scale. Abstract: Background and Objective: Ultrasound imaging has been widely used in the screening of kidney diseases. The localization and segmentation of the kidneys in ultrasound images are helpful for the clinical diagnosis of diseases. However, it is a challenging task to segment the kidney accurately from ultrasound images due to the interference of various factors. Methods: In this paper, a novel multi-scale and deep-supervised CNN architecture is proposed to segment the kidney. The architecture consists of an encoder, a pyramid pooling module and a decoder. In the encoder, we design a multi-scale input pyramid with parallel branches to capture features at different scales. In the decoder, a multi-output supervision module is developed. The introduction of the multi-output supervision module enables the network to learn to predict more precise segmentation results scale-by-scale. In addition, we construct a kidney ultrasound dataset, which contains of 400 images and 400 labels. Results: To highlight effectiveness of the proposed approach, we use six quantitative indicators to compare with several state-of-the-art methods on the same kidney ultrasound dataset. The results of our method on the six indicators of accuracy, dice, jaccard, precision, recall and ASSD are 98.86%, 95.86%, 92.18%, 96.38%, 95.47% and 0.3510, respectively. Conclusions: The analysis of evaluation indicators and segmentation results shows that our method achieves the best performance in kidney ultrasound image segmentation.