Opto-Electronic Engineering, Volume. 50, Issue 1, 220118(2023)
Intravascular ultrasound image segmentation combining polar coordinate modeling and a neural network
Figures & Tables(16)
Fig. 1. Ideal hypothesis diagrams. (a) The mask image that meets the ideal hypothesis; (b) The situation that does not meet the ideal hypothesis
Fig. 2. Modeling schematics. (a) Original image of IVUS; (b) Schematic diagram of modeling result. The intima contour and media contour are marked with red and green curves, respectively. The modeling results of the lumen area and plaque area are marked with red and green line segments, respectively
Fig. 4. Schematic diagram of the intersection of the true value and the predicted value patch area. Note: For the convenience of observation, the true value ray and the predicted value ray are staggered by a certain angle, and the two are actually on the same ray
Fig. 6. Visualization of segmentation results of different modeling methods
Fig. 7. Comparison of the visual effects of the segmentation results
Table 1. Information of the IVUS dataset
Table 2. The performance of the proposed method under different depths of backbone and different numbers of SEB modules
View tableView in ArticleTable 2. The performance of the proposed method under different depths of backbone and different numbers of SEB modules
Backbone SEB num JM HD/mm PAD TER Med Lum Plaque Med Lum Med Lum - ResNet18 0 0.8630 0.8589 0.6935 0.2361 0.1501 0.1077 0.1038 0 1 0.8658 0.8520 0.6947 0.2252 0.1603 0.1048 0.1174 0 2 0.8659 0.8634 0.6979 0.2167 0.1555 0.1078 0.1039 0 3 0.8655 0.8598 0.7016 0.2258 0.1513 0.1117 0.1038 0 ResNet34 0 0.8866 0.8674 0.7302 0.1906 0.1446 0.0849 0.0950 0 1 0.8803 0.8606 0.7173 0.2080 0.1475 0.0891 0.1013 0 2 0.8716 0.8610 0.7071 0.2357 0.1558 0.0979 0.1046 0 3 0.8818 0.8574 0.7167 0.2227 0.1559 0.0833 0.1077 0 ResNet50 0 0.8804 0.8692 0.7221 0.1855 0.1462 0.0937 0.0967 0 1 0.8738 0.8687 0.7131 0.2248 0.1385 0.0978 0.0946 0 2 0.8760 0.8671 0.7152 0.2266 0.1445 0.0901 0.0990 0 3 0.8805 0.8757 0.7211 0.2153 0.1351 0.0915 0.0879 0 ResNet101 0 0.8853 0.8665 0.7298 0.2014 0.1380 0.0860 0.0928 0 1 0.8910 0.8646 0.7346 0.1873 0.1553 0.0816 0.1099 0 2 0.8934 0.8738 0.7430 0.1761 0.1355 0.0794 0.1005 0 3 0.8902 0.8725 0.7337 0.1775 0.1396 0.0745 0.0827 0 ResNet152 0 0.8852 0.8646 0.7302 0.2012 0.1412 0.0881 0.1036 0 1 0.8845 0.8711 0.7256 0.2073 0.1402 0.0874 0.0958 0 2 0.8974 0.8708 0.7424 0.1879 0.1481 0.0736 0.0962 0 3 0.8849 0.8627 0.7255 0.2101 0.1545 0.0854 0.1088 0
Table 3. Experimental results with different loss functions
View tableView in ArticleTable 3. Experimental results with different loss functions
Loss function JM HD/mm PAD TER Med Lum Plaque Med Lum Med Lum - Smoothl1 0.8732 0.8698 0.7047 0.2131 0.1436 0.0999 0.0930 0 Ll+Lp 0.8850 0.8757 0.7319 0.2145 0.1350 0.0912 0.0929 0 Lm+Lp 0.8904 0.8636 0.7313 0.1997 0.1509 0.0794 0.1116 0 Ll+Lm 0.8808 0.8736 0.7183 0.2172 0.1447 0.0894 0.0892 0 IVUS Polar IoU Loss 0.8934 0.8738 0.7430 0.1761 0.1355 0.0794 0.1005 0
Table 4. Experimental results with different modeling methods
View tableView in ArticleTable 4. Experimental results with different modeling methods
建模方式 Loss JM HD/mm PAD TER Med Lum Plaque Med Lum Med Lum - Ellipse Smoothl1 0.8208 0.8124 0.6100 0.2633 0.1793 0.1399 0.1402 0.0767 PCM-PK 0.8732 0.8698 0.7047 0.2131 0.1436 0.0999 0.0930 0
Table 5. Performance comparison of different segmentation models
View tableView in ArticleTable 5. Performance comparison of different segmentation models
Backbone JM HD/mm PAD TER Med Lum Plaque Med Lum Med Lum - SegNet[ 30 ]- 0.8856 0.8618 0.7148 0.5367 1.5783 0.0948 0.1245 0.2328 UNet[ 31 ]- 0.8857 0.8846 0.7300 0.4700 0.1776 0.0956 0.0950 0.1319 Deeplabv3+[ 29 ]ResNet101 0.9026 0.8886 0.7567 0.2427 0.1302 0.0677 0.0787 0.0390 Ours ResNet101 0.8934 0.8738 0.7430 0.1761 0.1355 0.0794 0.1005 0
Table 6. Results of linear regression analysis of key clinical parameters
View tableView in ArticleTable 6. Results of linear regression analysis of key clinical parameters
斜率 截距 Pearson相关系数 LCSA 0.9825 0.2359 0.9427 VCSA 1.1259 −1.3911 0.9626 PCSA 1.2016 −1.5044 0.9432
Table 7. Results of Bland-Altman analysis of key clinical parameters
View tableView in ArticleTable 7. Results of Bland-Altman analysis of key clinical parameters
均值 均值偏移 偏移程度/% 离群值比例/% LCSA 5.9898 −0.1320 −2.20 5.25 VCSA 15.8044 −0.5628 −3.56 6.59 PCSA 9.8146 −0.4308 −4.40 7.27
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Jingyu Liu, Huaiyu Cai, Wenyue Hao, Tingtao Zuo, Zhongwei Jia, Yi Wang, Xiaodong Chen. Intravascular ultrasound image segmentation combining polar coordinate modeling and a neural network[J]. Opto-Electronic Engineering, 2023, 50(1): 220118
Paper Information
Category: Article
Received: Jun. 8, 2022
Accepted: Sep. 16, 2022
Published Online: Feb. 27, 2023
The Author Email: Cai Huaiyu (hycai@tju.edu.cn)
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