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首页> 外文会议>IEEE Applied Imagery Pattern Recognition Workshop >Categorizating 3D Fetal Ultrasound Image Database in First Trimester Pregnancy based on Mid-Sagittal Plane Assessments
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Categorizating 3D Fetal Ultrasound Image Database in First Trimester Pregnancy based on Mid-Sagittal Plane Assessments

机译：基于矢状面中位评估对孕早期的3D胎儿超声图像数据库进行分类

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Mid-Sagittal Plane (MSP) detection is crucial for the biometry assessments in ultrasound examinations. Screening on the correct MSP has been proven as the key condition for acquiring good quality of specified biometry measurements. In this paper, we proposed to categorize the 3D fetal ultrasound volume images based on the results of MSP detection. Based on MSP-detection results, our main focus here is to find the distinct descriptions or factors for database categorization. It is essential to realize how robust and effective the MSP-detection algorithm achieves with these factors. The database, including 381 fetal ultrasound image volumes have been collected from 141 different normal pregnant women, has been collected for more than three years in NCKU Hospital. The five factors adopted in categorizing the database include levels of image blurring, levels of weak edges, fetal adhesion, fetal posture and fetal size. The proposed MSP detection algorithm has been applied on 268 cases from the whole database (excluding the worst levels), and found the correct rate achieving 85.1 %. Then, the correct rate increases up to 90.0% by using the cases with the best conditions of all factors. Furthermore, the degree of influence for these factors in MSP detection has been discussed. At first, the results show that the image with highly weak edges (level 3) results in poor detections. Secondly, the poor fetal posture makes the highest effects on MSP detection (with 32% incorrect rate). It may be caused by having deep adhesions with the endometrium so that the fetal head boundary could not be fitted well. In fine-quality images, the adhesion factor reveals more determinative than the rough-quality factors. Thirdly, two factors of adhesion and weak edges achieved similar effects (not significant in statistics), with 23% and 25.7% incorrect rates, respectively. The less-influential factors are the fetus size and image blurring, achieving up to 14% and 16% incorrect rates, respectively.
机译：中矢状平面（MSP）检测对于超声检查中的生物学评估至关重要。已被证明在正确的MSP上筛选作为获取良好质量的指定生物测量测量的关键条件。在本文中，我们提出基于MSP检测结果对3D胎儿超声音量图像进行分类。根据MSP检测结果，我们在此处的主要侧重于此是找到数据库分类的不同描述或因素。必须实现MSP检测算法如何实现这些因素的稳健和有效。数据库，包括381个胎儿超声图像体积，从141名不同的常规孕妇收集，在Ncku医院收集了三年以上。分类数据库中采用的五种因素包括图像模糊，弱边缘水平，胎儿粘附，胎儿姿势和胎儿尺寸。拟议的MSP检测算法已从整个数据库（不包括最差水平）的268个案例中应用，并找到了达到85.1 \％的正确速率。然后，通过使用所有因素的最佳条件的情况，正确的速率增加到90.0 \％。此外，已经讨论了MSP检测中这些因素的影响程度。首先，结果表明，具有高弱边缘（3级）的图像导致差的检测。其次，胎儿姿势不良对MSP检测产生最高影响（具有32 \％不正确的速率）。它可能是由与子宫内膜具有深厚的粘连来引起的，使得胎儿头边界不能很好地安装。在精细质量的图像中，粘合因子揭示了比粗糙质量因素更多的确定性。第三，两种粘附和弱边缘的因素达到了类似的效果（统计学中不显着），分别具有23 \％和25.7 \％不正确的速率。较小的影响因素是胎儿尺寸和图像模糊，分别实现高达14 \％和16 \％不正确的速率。

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