Liver Extraction Using Residual Convolution Neural Networks From Low-Dose CT Images

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$pm text{1.8}%$, decreased volumetric overlap error up to 4.30$pm text{0.58}%$, and average symmetric surface distance less than 1.4 $pm text{0.5mm}$. These findings have shown that LER-CN is a favorable method for medical applications with high efficiency allowing low radiation risk to patients.
Source: IEEE Transactions on Biomedical Engineering - Category: Biomedical Engineering Source Type: research