Categorizating 3D Fetal Ultrasound Image Database in First Trimester Pregnancy based on Mid-Sagittal Plane Assessments

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.