Abstract
Segmentation of liver tumors plays an important role in the choice of therapeutic strategies for liver disease and treatment monitoring. In this paper, we generalize the process of a level set with a novel algorithm of dynamic regulation to energy functional parameters. The presented method is fully automatic once the tumor has been detected. First, a 3D convolutional neural network with dense layers for classification is used to estimate current contour location relative to the tumor boundary. Second, the output 3D CNN probabilities can dynamically regulate parameters of the level set functional over the process of segmentation. Finally, for full automation, appropriate initializations and local window size are generated based on the current contour position probabilities. We demonstrate the proposed method on the dataset of MICCAI 2017 LiTS Challenge and 3DIRCADb that include low contrast and heterogeneous tumors as well as noisy images. To illustrate the strength of our method, we evaluated it against the state-of-the-art methods. Compared with the level set framework with fixed parameters, our method performed better significantly with an average DICE improvement of 0.15. We also analyzed a challenging dataset 3DIRCADb of tumors and obtained a competitive DICE of 0.85 ± 0.06 with the proposed method.
Highlights
In level set works, the choice of energy functional parameters causes troubles
We propose a significant improvement on the traditional level set process, a novel algorithm of dynamic regulation to functional parameters over iterations using the 3D convolutional neural network (DRLS)
To the best of our knowledge, this is the first use of 3D Convolutional neural network (CNN) to join in the level set for 3D segmentation. rough dynamic parameters, the proposed hybrid method of the 3D level set and deep learning is able to resolve the problems of low contrast, abnormal shape, and variant features of texture tumors, resulting in a generalized segmentation solution than methods available to date
Summary
The choice of energy functional parameters causes troubles. Inappropriate parameters result in disabled automatic segmentation [18]. The zero level set is able to escape from local minima acquiring better results. It lacks the research on 3D segmentation that reflects more significance in practice. We propose a significant improvement on the traditional level set process, a novel algorithm of dynamic regulation to functional parameters over iterations using the 3D convolutional neural network (DRLS). Rough dynamic parameters, the proposed hybrid method of the 3D level set and deep learning is able to resolve the problems of low contrast, abnormal shape, and variant features of texture tumors, resulting in a generalized segmentation solution than methods available to date To the best of our knowledge, this is the first use of 3D CNN to join in the level set for 3D segmentation. rough dynamic parameters, the proposed hybrid method of the 3D level set and deep learning is able to resolve the problems of low contrast, abnormal shape, and variant features of texture tumors, resulting in a generalized segmentation solution than methods available to date
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