Liver Extraction Using Residual Convolution Neural Networks From Low-Dose CT Images - Cheema, Muhammad Nadeem; Nazir, Anam; Sheng, Bin; Li, Ping; Qin, Jing; Feng, David Dagan (IEEE transactions on biomedical engineering, 2019)
An efficient and precise liver extraction from computed tomography (CT) images is a crucial step for computer-aided hepatic diseases diagnosis and treatment. Considering the possible risk to patient's health due to X-ray radiation of repetitive CT examination, low-dose CT (LDCT) is an effective solution for medical imaging. However, inhomogeneous appearances and indistinct boundaries due to additional noise and streaks artifacts in LDCT images often make it a challenging task. This study aims to extract a liver model from LDCT images for facilitating medical expert in surgical planning and post-operative assessment along with low radiation risk to the patient. Our method carried out liver extraction by employing residual convolutional neural networks (LER-CN), which is further refined by noise removal and structure preservation components. After patch-based training, our LER-CN shows a competitive performance relative to state-of-the-art methods for both clinical and publicly available MICCAI Sliver07 datasets. We have proposed training and learning algorithms for LERCN based on back propagation gradient descent. We have evaluated our method on 150 abdominal CT scans for liver extraction. LER-CN achieves dice similarity coefficient up to 96.5±1.8%, decreased volumetric overlap error up to 4.30±0.58% , and average symmetric surface distance less than 1.4 ±0.5mm. These findings have shown that LER-CN is a favorable method for medical applications with high efficiency allowing low radiation risk to patients.