Journal
IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING
Volume 66, Issue 9, Pages 2641-2650Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TBME.2019.2894123
Keywords
Computed tomography; low-dose CT; medical imaging; residual CNNs; liver extraction
Categories
Funding
- National Natural Science Foundation of China [61872241, 61572316]
- National Key Research and Development Program of China [2017YFE0104000, 2016YFC1300302]
- Macau Science and Technology Development Fund [0027/2018/A1]
- Science and Technology Commission of Shanghai Municipality [18410750700, 17411952600, 16DZ0501100]
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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 +/- 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.
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