Automatic fetal presentation diagnosis from ultrasound images for rural zones: head location as an indicator for fetal presentation

被引:3
作者
Arroyo, Junior [1 ]
Saavedra, Ana C. [1 ]
Tamayo, Lorena [2 ]
Egoavil, Miguel [2 ]
Ramos, Berta [3 ]
Castaneda, Benjamin [1 ]
机构
[1] Pontificia Univ Catolica Peru, Engn Dept, Med Imaging Lab, Lima, Peru
[2] Med Innovat & Technol, Res & Dev, Lima, Peru
[3] Pontificia Univ Catolica Peru, Hlth Serv, Lima, Peru
来源
MEDICAL IMAGING 2021: COMPUTER-AIDED DIAGNOSIS | 2021年 / 11597卷
关键词
Deep Learning; fetal presentation; ultrasound; rural zones; BREECH PRESENTATION; DELIVERY;
D O I
10.1117/12.2580946
中图分类号
R318 [生物医学工程];
学科分类号
0831 ;
摘要
Pregnancy requires constant monitoring by health care providers to avoid conditions that may threaten the lives of the fetus and the mother at birth. For labor management, the diagnosis of fetal presentation is essential to guarantee delivery viability. A direct indicator of fetal presentation is the fetal head location, which can be placed close to the canal birth (cephalic, head-first) or far from the canal birth (breech, feet first). Unlike urban areas, the population in rural zones experience difficulties in accessing healthcare monitoring. Although telemedicine has helped bring medical technology closer to these regions, the diagnosis still requires medical specialists. This study presents an automatic three-stage detection framework for assessing the fetal head position (and hence the fetal presentation). The first stage involves applying morphological filtering, intensity-based thresholding, and shape-based filtering for a preliminary head detection and segmentation. The second stage comprises segmentation enhancement using a combinatory approach. The third stage uses the detection results to depict the spatial location likelihood of the head, which indicates the head location and fetal presentation. Fifteen volunteers in the third trimester of pregnancy were evaluated, and the fetal presentation was diagnosed. These results were compared with the diagnosis of a radiologist (used as a gold standard). The proposed method presented a 100% accuracy in determining the fetal presentation, albeit a limited number of cases evaluated.
引用
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页数:9
相关论文
共 23 条
[1]  
Anto EA, 2015, IEEE ENG MED BIO, P793, DOI 10.1109/EMBC.2015.7318481
[2]   Ultrasound Examination of the Fetal Heart [J].
Bishop, Katherine C. ;
Kuller, Jeffrey A. ;
Boyd, Brita K. ;
Rhee, Eleanor H. ;
Miller, Stephen ;
Barker, Piers .
OBSTETRICAL & GYNECOLOGICAL SURVEY, 2017, 72 (01) :54-61
[3]   Common determinants of breech presentation at birth in singletons: a population-based study [J].
Cammu, Hendrik ;
Dony, Noelie ;
Martens, Guy ;
Colman, Roos .
EUROPEAN JOURNAL OF OBSTETRICS & GYNECOLOGY AND REPRODUCTIVE BIOLOGY, 2014, 177 :106-109
[4]  
Ferrer J, 2017, IEEE ENG MED BIO, P2622, DOI 10.1109/EMBC.2017.8037395
[5]   Difference of Gaussians revolved along elliptical paths for ultrasound fetal head segmentation [J].
Foi, Alessandro ;
Maggioni, Matteo ;
Pepe, Antonietta ;
Rueda, Sylvia ;
Noble, J. Alison ;
Fapageorghiou, Aris T. ;
Tohka, Jussi .
COMPUTERIZED MEDICAL IMAGING AND GRAPHICS, 2014, 38 (08) :774-784
[6]   LEAST-SQUARES FITTING OF CIRCLES AND ELLIPSES [J].
GANDER, W ;
GOLUB, GH ;
STREBEL, R .
BIT, 1994, 34 (04) :558-578
[7]   Fetal age assessment based on ultrasound head biometry and the effect of maternal and fetal factors [J].
Johnsen, SL ;
Rasmussen, S ;
Sollien, R ;
Kiserud, T .
ACTA OBSTETRICIA ET GYNECOLOGICA SCANDINAVICA, 2004, 83 (08) :716-723
[8]  
Kearney-Nunnery R., 2015, ADV YOUR CAREER CONC
[9]   Automatic evaluation of fetal head biometry from ultrasound images using machine learning [J].
Kim, Hwa Pyung ;
Lee, Sung Min ;
Kwon, Ja-Young ;
Park, Yejin ;
Kim, Kang Cheol ;
Seo, Jin Keun .
PHYSIOLOGICAL MEASUREMENT, 2019, 40 (06)
[10]  
Liu P., 2020, INT SOC OPTICS PHOTO, V11427