Abstract
Image segmentation plays a vital role in the medical diagnosis and intervention field. The segmentation methods can be classified as fully automated, semiautomated or manual. Among them, manual segmentation can best improve the quality of the results, but it is time-consuming and tedious, and it may lead to operator bias. A continuity-aware probabilistic network based on the divide-and-conquer method was proposed in the current work. The proposed network comprised backbone network, local segmentation and a weight network. The backbone network extracts the features from image. The local segmentation divides the data space, whereas the weight network provides the continuity-aware weights. Therefore, combining those results of the weighted segments can eventually yield precise estimations. In this study, the proposed model was evaluated against several recent methods on the three datasets, and a several performance indexes of segmentation were evaluated for liver segmentation, the results showing that it is the most advanced liver segmentation approach. The source code of this work is publicly shared at https://github.com/licongsheng/DCSegNet for others to easily reproduce the work and build their own models with the introduced mechanisms.
Highlights
A basic task in planning liver operations is to detect and evaluate the liver shape using abdominal CT images and computer assistance, such as radiotherapy [1]
The deep convolutional neural networks (DCNNs) designed for image segmentation are categorized into two types, namely, 2D and 3D networks [10]
A liver segmentation model was proposed in line with the divide-and-conquer principle based on the structural similarity of the adjacent liver cross-sections
Summary
A basic task in planning liver operations is to detect and evaluate the liver shape using abdominal CT images and computer assistance, such as radiotherapy [1]. It is worth noting that a volumertical liver segmentation algorithm should be able to take into account the interslice and intraslice characteristics To address this issue, 3D DCNNs have been constructed [15], [16], such as V-net [17], denseVNet [16], Z-Net [18] and other 3D neural networks [19]. 3D DCNNs have been constructed [15], [16], such as V-net [17], denseVNet [16], Z-Net [18] and other 3D neural networks [19] They will greatly increase the model complexity and the number of hyperparameters in the model [17], [20]. Some studies have been carried out to improve the
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