Abstract
The liver is an irreplaceable organ in the human body, maintaining life activities and metabolism. Malignant tumors of the liver have a high mortality rate at present. Computer-aided segmentation of the liver and tumors has significant effects on clinical diagnosis and treatment. There are still many challenges in the segmentation of the liver and liver tumors simultaneously, such as, on the one hand, that convolutional kernels with fixed geometric structures do not match complex, irregularly shaped targets; on the other, pooling during convolution results in a loss of spatial contextual information of images. In this work, we designed a cascaded U-ADenseNet with coarse-to-fine processing for addressing the above issues of fully automatic segmentation. This work contributes multi-resolution input images and multi-layered channel attention combined with atrous spatial pyramid pooling densely connected in the fine segmentation. The proposed model was evaluated by a public dataset of the Liver Tumor Segmentation Challenge (LiTS). Our approach attained competitive liver and tumor segmentation scores that exceeded other methods across a wide range of metrics.
Highlights
The data were divided into a training set of 105 cases and a testing set of 26 cases, which were processed into axial slices in a portable network graphics (PNG) format
The techniques of dilated convolution and the multi-layered channel attention module enabled the network to extract more discriminative image features, which helps to improve the overall segmentation performance of the liver and tumors. This two-step cascade method solved the problem of unbalanced tumor segmentation data caused by the small proportion of liver tumors in the whole image
These experimental results on the publicly available Liver Tumor Segmentation Challenge (LiTS) dataset confirmed the effectiveness of the liver and tumors segmentation
Summary
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. Since the liver is a reasonably significant organ for abdominal metabolism, liver tumors in particular, malignancy certainly poses a serious threat to human health. Statistics from the World Health Organization have shown that commonly occurring liver cancer was accompanied by a high mortality rate worldwide. Locate and segment lesions has become the primary step in the development of subsequent precision treatment and individualized protocols. Accurate diagnostic treatment has a significant positive effect on reducing the number of patients suffering from diseases and improving disease prognosis [1]. The computed tomography (CT) image analysis technique is the main solution for diagnosis, and plays a vital role in the treatment of hepatoma [2]
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