Automatic measurement of fetal femur length in ultrasound images: a comparison of random forest regression model and SegNet

The aim of this work is the preliminary clinical validation and accuracy evaluation of our automatic algorithms in assessing progression fetal femur length (FL) in ultrasound images. To compare the random forest regression model with the SegNet model from the two aspects of accuracy and robustness....

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Autores principales:	Fengcheng Zhu, Mengyuan Liu, Feifei Wang, Di Qiu, Ruiman Li, Chenyang Dai
Formato:	article
Lenguaje:	EN
Publicado:	AIMS Press 2021
Materias:	ultrasonoscopy femur length measurement random forest deep learning prenatal diagnosis Biotechnology TP248.13-248.65 Mathematics QA1-939
Acceso en línea:	https://doaj.org/article/da486265ccba44a1bae06ee06ab81304
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spelling	oai:doaj.org-article:da486265ccba44a1bae06ee06ab813042021-11-23T02:47:37ZAutomatic measurement of fetal femur length in ultrasound images: a comparison of random forest regression model and SegNet10.3934/mbe.20213871551-0018https://doaj.org/article/da486265ccba44a1bae06ee06ab813042021-09-01T00:00:00Zhttps://www.aimspress.com/article/doi/10.3934/mbe.2021387?viewType=HTMLhttps://doaj.org/toc/1551-0018The aim of this work is the preliminary clinical validation and accuracy evaluation of our automatic algorithms in assessing progression fetal femur length (FL) in ultrasound images. To compare the random forest regression model with the SegNet model from the two aspects of accuracy and robustness. In this study, we proposed a traditional machine learning method to detect the endpoints of FL based on a random forest regression model. Deep learning methods based on SegNet were proposed for the automatic measurement method of FL, which utilized skeletonization processing and improvement of the full convolution network. Then the automatic measurement results of the two methods were evaluated quantitatively and qualitatively with the results marked by doctors. 436 ultrasonic fetal femur images were evaluated by the two methods above. Compared the results of the above three methods with doctor's manual annotations, the automatic measurement method of femur length based on the random forest regression model was 1.23 ± 4.66 mm and the method based on SegNet was 0.46 ± 2.82 mm. The indicator for evaluating distance was significantly lower than the previous literature. Measurement method based SegNet performed better in the case of femoral end adhesion, low contrast, and noise interference similar to the shape of the femur. The segNet-based method achieves promising performance compared with the random forest regression model, which can improve the examination accuracy and robustness of the measurement of fetal femur length in ultrasound images.Fengcheng ZhuMengyuan LiuFeifei WangDi QiuRuiman LiChenyang Dai AIMS Pressarticleultrasonoscopyfemur length measurementrandom forestdeep learningprenatal diagnosisBiotechnologyTP248.13-248.65MathematicsQA1-939ENMathematical Biosciences and Engineering, Vol 18, Iss 6, Pp 7790-7805 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	ultrasonoscopy femur length measurement random forest deep learning prenatal diagnosis Biotechnology TP248.13-248.65 Mathematics QA1-939
spellingShingle	ultrasonoscopy femur length measurement random forest deep learning prenatal diagnosis Biotechnology TP248.13-248.65 Mathematics QA1-939 Fengcheng Zhu Mengyuan Liu Feifei Wang Di Qiu Ruiman Li Chenyang Dai Automatic measurement of fetal femur length in ultrasound images: a comparison of random forest regression model and SegNet
description	The aim of this work is the preliminary clinical validation and accuracy evaluation of our automatic algorithms in assessing progression fetal femur length (FL) in ultrasound images. To compare the random forest regression model with the SegNet model from the two aspects of accuracy and robustness. In this study, we proposed a traditional machine learning method to detect the endpoints of FL based on a random forest regression model. Deep learning methods based on SegNet were proposed for the automatic measurement method of FL, which utilized skeletonization processing and improvement of the full convolution network. Then the automatic measurement results of the two methods were evaluated quantitatively and qualitatively with the results marked by doctors. 436 ultrasonic fetal femur images were evaluated by the two methods above. Compared the results of the above three methods with doctor's manual annotations, the automatic measurement method of femur length based on the random forest regression model was 1.23 ± 4.66 mm and the method based on SegNet was 0.46 ± 2.82 mm. The indicator for evaluating distance was significantly lower than the previous literature. Measurement method based SegNet performed better in the case of femoral end adhesion, low contrast, and noise interference similar to the shape of the femur. The segNet-based method achieves promising performance compared with the random forest regression model, which can improve the examination accuracy and robustness of the measurement of fetal femur length in ultrasound images.
format	article
author	Fengcheng Zhu Mengyuan Liu Feifei Wang Di Qiu Ruiman Li Chenyang Dai
author_facet	Fengcheng Zhu Mengyuan Liu Feifei Wang Di Qiu Ruiman Li Chenyang Dai
author_sort	Fengcheng Zhu
title	Automatic measurement of fetal femur length in ultrasound images: a comparison of random forest regression model and SegNet
title_short	Automatic measurement of fetal femur length in ultrasound images: a comparison of random forest regression model and SegNet
title_full	Automatic measurement of fetal femur length in ultrasound images: a comparison of random forest regression model and SegNet
title_fullStr	Automatic measurement of fetal femur length in ultrasound images: a comparison of random forest regression model and SegNet
title_full_unstemmed	Automatic measurement of fetal femur length in ultrasound images: a comparison of random forest regression model and SegNet
title_sort	automatic measurement of fetal femur length in ultrasound images: a comparison of random forest regression model and segnet
publisher	AIMS Press
publishDate	2021
url	https://doaj.org/article/da486265ccba44a1bae06ee06ab81304
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Automatic measurement of fetal femur length in ultrasound images: a comparison of random forest regression model and SegNet

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