Automatic construction of filter tree by genetic programming for ultrasound guidance image segmentation. (July 2022)

• Record Type: Journal Article
• Authors: Yuan, Dalong; Zhang, Dong; Yang, Yan; Yang, Shuang
• Is Part Of: Biomedical signal processing and control. Volume 76(2022)
• Publication Date: 2022-07
• Subjects: Ultrasound guidance image -- Segmentation -- End-to-end -- Small dataset -- Genetic programming; Signal processing -- Periodicals; Biomedical engineering -- Periodicals; Signal Processing, Computer-Assisted -- Periodicals; Image Processing, Computer-Assisted -- Periodicals; Biomedical Engineering -- Periodicals; 610.28
• DOI: 10.1016/j.bspc.2022.103641
• Languages: English
• ISSNs: 1746-8094

Abstract

Highlights: Design a specific predefined function set for processing UGI data. Introduce position-determined function (PDF) to incorporate some prior knowledge. Use a linearly decreasing mutation rate to increase the population diversity. Design a bloat penalty term to reduce the bias for small-sized individuals. Abstract: Segmentation of ultrasound guidance images (UGIs) is a critical step in ultrasound-guided high intensity focused ultrasound (HIFU) therapy. However, the low signal-to-noise ratio characteristic of UGIs makes it difficult to acquire enough annotations. This paper proposes a novel genetic programming-based approach to achieve automatic construction of an image filter tree (IFT) for UGI segmentation since genetic programming has a natural advantage in training on small datasets. In the new approach, a set of predefined functions are adapted with better anti-noise performance to deal with noise interference. Moreover, a position-determined function is designed for incorporating preoperative information in each IFT to form a closed-loop system thereby facilitating the segmentation process. The optimal IFT evolved by genetic programming, along with a preprocessing step and a postprocessing step, constructs the pipeline for the segmentation of UGIs. The quantitative evaluation of the segmentation results shows the mean true positive rate, the mean false positive rate, the mean intersection over union, the mean norm Hausdorff distance and the mean norm maximum average distance are found to be 94.86%, 6.72%, 89.14%, 3.20% and 0.83%, respectively, outperforming the popular convolutional neural network-based segmentation methods. The segmentation results reveal that the evolved IFT can achieve accurate segmentation of UGIs and indicate that the proposed approach can be a promising option for medical image segmentation when there are only a few training samples available.