Liver extraction using residual convolution neural networks from low-dose CT images

Muhammad Nadeem Cheema, Anam Nazir, Bin Sheng, Ping Li, Jing Qin, David Dagan Feng

Research output: Journal article publicationJournal articleAcademic researchpeer-review

16 Citations (Scopus)


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 LER-CN 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[Formula: see text], decreased volumetric overlap error up to 4.30[Formula: see text], and average symmetric surface distance less than 1.4 [Formula: see text]. These findings have shown that LER-CN is a favorable method for medical applications with high efficiency allowing low radiation risk to patients.

Original languageEnglish
Pages (from-to)2641-2650
Number of pages10
JournalIEEE Transactions on Biomedical Engineering
Issue number9
Publication statusPublished - Sept 2019

ASJC Scopus subject areas

  • Biomedical Engineering


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