Computer-aided diagnosis of gallbladder polyps based on high resolution ultrasonography. (March 2020)

• Record Type: Journal Article
• Authors: Chen, Tao; Tu, Shaoxiong; Wang, Haolu; Liu, Xuesong; Li, Fenghua; Jin, Wang; Liang, Xiaowen; Zhang, Xiaoqun; Wang, Jian
• Is Part Of: Computer methods and programs in biomedicine. Volume 185(2020)
• Publication Date: 2020-03
• Subjects: Gallbladder polyp -- Computer aided diagnosis -- Ultrasound image segmentation -- Variational methods; 62H35 -- 65K10 -- 35A15; 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.2019.105118
• Languages: English
• ISSNs: 0169-2607

Abstract

Highlights: Proposed an ultrasound image segmentation method for gallbladder polyps extraction. After data augmentation and dimensional reduction, constructed a CAD system. Achieved an improved result on 224 patients compared to existing studies. CAD system is competitive to the human eyes of expert sonographers. CAD system achieves a much fast diagnosis speed than sonographers. Graphical abstract: Abstract: Background and objective: Gallbladder polyp is a common disease with an overall population prevalence between 4 and 7%. It can be classified as neoplastic and non-neoplastic lesions. Surgical treatment is necessary for neoplastic polyps. Due to its easy accessibility and nonradioactive, ultrasonography is the mostly used preoperative diagnosis tool for gallbladder polyps. However, human image analysis depends greatly on levels of experience, which results in many overtreatment cases and undertreatment cases in clinics. Methods: In this study, we proposed an ultrasound image segmentation algorithm, combined with principal components analysis (PCA) and AdaBoost algorithms to construct a computer-aided diagnosis system for the differentiate diagnosis of neoplastic and non-neoplastic gallbladder polyps. Results: The proposed segmentation method achieved a high accuracy of 95% for outlining the gallbladder region. The accuracy, sensitivity, specificity for the proposed computer-aided diagnosis system based on the segmented images are 87.54%, 86.52% and 89.40%, compared to 69.05%, 67.86% and 70.17% with convolutional neural network. The diagnosis result is also slightly higher than the human eyes of sonologists (86.22%, 85.19% and 89.18% as an average of four sonologists), while with a much faster diagnosis speed (0.02s vs 3s). Conclusions: We proposed an efficient ultrasound image segmentation approach and a reliable system of automatic diagonals of neoplastic and non-neoplastic gallbladder polyps. The results show that the diagnosis accuracy is competitive to the expert sonologists while requires much less diagnosis time.