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基于深度学习的表观扩散系数图上骨盆骨结构的半自动定量分析:参考范围的建立

Semi-automatic quantitative analysis of the pelvic bony structures on apparent diffusion coefficient maps based on deep learning: establishment of reference ranges.

作者信息

Liu Xiang, Han Chao, Lin Ziying, Sun Zhaonan, Zhang Yaofeng, Wang Xiangpeng, Zhang Xiaodong, Wang Xiaoying

机构信息

Department of Radiology, Peking University First Hospital, Beijing, China.

Beijing Smart Tree Medical Technology Co. Ltd., Beijing, China.

出版信息

Quant Imaging Med Surg. 2022 Jan;12(1):576-591. doi: 10.21037/qims-21-123.

Abstract

BACKGROUND

Apparent diffusion coefficient (ADC) maps provide quantitative information on both normal and abnormal tissues. However, it is difficult to distinguish between these tissues unless consistent and precise ADC values can be obtained from normal tissues. For this study we developed a deep learning-based convolutional neural network (CNN) for pelvic bony structure segmentation and established the reference ranges of ADC parameters for normal pelvic bony structures.

METHODS

We retrospectively enrolled 767 prostate cancer (PCa) patients for quantitative ADC analyses of normal pelvic bony structures. A subset of 288 patients who did not receive treatment for PCa (S1) were used to develop a CNN model for the segmentation of 8 pelvic bony structures (lumbar vertebra, sacrococcyx, ilium, acetabulum, femoral head, femoral neck, ischium, and pubis). The proposed CNN was used for the automated segmentation of these pelvic bony structures from a subset of 405 patients who did not receive treatment (S2) and 74 patients who received treatment [radiotherapy (S3) or endocrine therapy (S4)]. The 95% confidence interval (CI) was used to establish reference ranges for the ADC values from the normal pelvic bony structures of S1 and S2.

RESULTS

The Dice scores (Sørensen-Dice coefficient) for the CNN segmentation of the 8 pelvic bones on the ADC maps ranged from 0.90±0.02 (ilium) to 0.95±0.03 (femoral head) in the S1 testing set. In the S2 data set, the Dice scores showed no significant difference among the different scanners (P>0.05), and no significant differences were found among the S2, S3, and S4 data sets. The correlation analysis revealed that the b value and field strength were significantly correlated with ADC values (all P<0.001), while age and treatment were not significant variables (all P>0.05). The ADC reference ranges (95% CI) were as follows: lumbar vertebra, 1.11 (0.90-1.54); sacrococcyx, 0.82 (0.61-1.15); ilium, 0.57 (0.45-0.62); acetabulum, 0.59 (0.40-0.69); femoral head, 0.46 (0.25-0.58); femoral neck, 0.43 (0.25-0.48); ischium, 0.45 (0.26-0.55); and pubis, 0.57 (0.45-0.65).

CONCLUSIONS

This study preliminarily established reference ranges for the ADC values of normal pelvic bony structures. The image acquisition parameters had an influence on the ADC values.

摘要

背景

表观扩散系数(ADC)图可提供有关正常和异常组织的定量信息。然而,除非能从正常组织中获得一致且精确的ADC值,否则很难区分这些组织。在本研究中,我们开发了一种基于深度学习的卷积神经网络(CNN)用于骨盆骨结构分割,并建立了正常骨盆骨结构的ADC参数参考范围。

方法

我们回顾性纳入了767例前列腺癌(PCa)患者,以对正常骨盆骨结构进行定量ADC分析。288例未接受PCa治疗的患者子集(S1)用于开发用于分割8个骨盆骨结构(腰椎、骶尾骨、髂骨、髋臼、股骨头、股骨颈、坐骨和耻骨)的CNN模型。所提出的CNN用于从405例未接受治疗的患者子集(S2)和74例接受治疗的患者[放疗(S3)或内分泌治疗(S4)]中自动分割这些骨盆骨结构。使用95%置信区间(CI)来建立S1和S2正常骨盆骨结构ADC值的参考范围。

结果

在S1测试集中,ADC图上8个骨盆骨的CNN分割的Dice分数( Sørensen-Dice系数)范围为0.90±0.02(髂骨)至0.95±0.03(股骨头)。在S2数据集中,不同扫描仪之间的Dice分数无显著差异(P>0.05),并且在S2、S3和S4数据集之间也未发现显著差异。相关性分析显示,b值和场强与ADC值显著相关(所有P<0.001),而年龄和治疗不是显著变量(所有P>0.05)。ADC参考范围(95%CI)如下:腰椎,1.11(0.90 - 1.54);骶尾骨,0.82(0.61 - 1.15);髂骨,0.57(0.45 - 0.62);髋臼,0.59(0.40 - 0.69);股骨头,0.46(0.25 - 0.58);股骨颈,0.43(0.25 - 0.48);坐骨,0.45(0.26 - 0.55);耻骨,0.57(0.45 - 0.65)。

结论

本研究初步建立了正常骨盆骨结构ADC值的参考范围。图像采集参数对ADC值有影响。

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