دورية أكاديمية
SADSNet: A robust 3D synchronous segmentation network for liver and liver tumors based on spatial attention mechanism and deep supervision.
| العنوان: | SADSNet: A robust 3D synchronous segmentation network for liver and liver tumors based on spatial attention mechanism and deep supervision. |
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| المؤلفون: | Yang S; School of Life and Environmental Science, Guilin University of Electronic Technology, Guilin, China., Liang Y; School of Life and Environmental Science, Guilin University of Electronic Technology, Guilin, China., Wu S; School of Life and Environmental Science, Guilin University of Electronic Technology, Guilin, China., Sun P; School of Electronic Engineering and Automation, Guilin University of Electronic Technology, Guilin, China., Chen Z; School of Life and Environmental Science, Guilin University of Electronic Technology, Guilin, China.; School of Electronic Engineering and Automation, Guilin University of Electronic Technology, Guilin, China.; Guangxi Colleges and Universities Key Laboratory of Biomedical Sensors and Intelligent Instruments, Guilin, China.; Guangxi Engineering Technology Research Center of Human Physiological Information Noninvasive Detection, Guilin, China. |
| المصدر: | Journal of X-ray science and technology [J Xray Sci Technol] 2024; Vol. 32 (3), pp. 707-723. |
| نوع المنشور: | Journal Article |
| اللغة: | English |
| بيانات الدورية: | Publisher: IOS Press Country of Publication: Netherlands NLM ID: 9000080 Publication Model: Print Cited Medium: Internet ISSN: 1095-9114 (Electronic) Linking ISSN: 08953996 NLM ISO Abbreviation: J Xray Sci Technol Subsets: MEDLINE |
| أسماء مطبوعة: | Publication: Original Publication: San Diego [i.e. Duluth, MN] : Academic Press, [c1989- |
| مواضيع طبية MeSH: | Liver Neoplasms*/diagnostic imaging , Liver*/diagnostic imaging , Tomography, X-Ray Computed*/methods , Algorithms*, Humans ; Imaging, Three-Dimensional/methods ; Neural Networks, Computer ; Deep Learning |
| مستخلص: | Highlights: • Introduce a data augmentation strategy to expand the required different morphological data during the training and learning phase, and improve the algorithm's feature learning ability for complex and diverse tumor morphology CT images.• Design attention mechanisms for encoding and decoding paths to extract fine pixel level features, improve feature extraction capabilities, and achieve efficient spatial channel feature fusion.• The deep supervision layer is used to correct and decode the final image data to provide high accuracy of results.• The effectiveness of this method has been affirmed through validation on the LITS, 3DIRCADb, and SLIVER datasets. Background: Accurately extracting liver and liver tumors from medical images is an important step in lesion localization and diagnosis, surgical planning, and postoperative monitoring. However, the limited number of radiation therapists and a great number of images make this work time-consuming. Objective: This study designs a spatial attention deep supervised network (SADSNet) for simultaneous automatic segmentation of liver and tumors. Method: Firstly, self-designed spatial attention modules are introduced at each layer of the encoder and decoder to extract image features at different scales and resolutions, helping the model better capture liver tumors and fine structures. The designed spatial attention module is implemented through two gate signals related to liver and tumors, as well as changing the size of convolutional kernels; Secondly, deep supervision is added behind the three layers of the decoder to assist the backbone network in feature learning and improve gradient propagation, enhancing robustness. Results: The method was testing on LITS, 3DIRCADb, and SLIVER datasets. For the liver, it obtained dice similarity coefficients of 97.03%, 96.11%, and 97.40%, surface dice of 81.98%, 82.53%, and 86.29%, 95% hausdorff distances of 8.96 mm, 8.26 mm, and 3.79 mm, and average surface distances of 1.54 mm, 1.19 mm, and 0.81 mm. Additionally, it also achieved precise tumor segmentation, which with dice scores of 87.81% and 87.50%, surface dice of 89.63% and 84.26%, 95% hausdorff distance of 12.96 mm and 16.55 mm, and average surface distances of 1.11 mm and 3.04 mm on LITS and 3DIRCADb, respectively. Conclusion: The experimental results show that the proposed method is effective and superior to some other methods. Therefore, this method can provide technical support for liver and liver tumor segmentation in clinical practice. |
| فهرسة مساهمة: | Keywords: Automatic segmentation; deep supervision; liver; liver tumors; spatial attention mechanism |
| تواريخ الأحداث: | Date Created: 20240329 Date Completed: 20240531 Latest Revision: 20240531 |
| رمز التحديث: | 20240531 |
| DOI: | 10.3233/XST-230312 |
| PMID: | 38552134 |
| قاعدة البيانات: | MEDLINE |
| تدمد: | 1095-9114 |
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| DOI: | 10.3233/XST-230312 |