A novel method with a deep network and directional edges for automatic detection of a fetal head

Abstract

In this paper, we propose a novel method for the automatic detection of fetal head in 2D ultrasound images. Fetal head detection has been a challenging task, as the ultrasound images usually have poor quality, the structures contained in the images are complex, and the gray scale distribution is highly variable. Our approach is based on a deep belief network and a modified circle detection method. The whole process can be divided into two steps: first, a deep learning architecture is applied to search the whole image and determine the result patch that contains the entire fetal head; second, a modified circle detection method is used along with Hough transform to detect the position and size of the fetal head. In order to validate our method, experiments are performed on both synthetic data and clinic ultrasound data. A good performance of the proposed method is shown in the paper.

Toward deep observation: A systematic survey on artificial intelligence techniques to monitor fetus via ultrasound images

“…Classification tasks for skull localization and HC are rarely used, as seen only in (n = 3, 2.80%) studies. Studies ( Li et al., 2018 ; Nie et al., 2015a ) used classification to localize the region of interest (ROI) and identify the fetus head based on 2D US taken in the first and second trimesters respectively. 3D US taken in the first trimester were used to detect fetal head in one study ( Nie et al., 2015b ).…”

“…DL was used heavily in all fetal organ experiments including general fetus issues, fetus head, fetus face, fetus heart, and fetus abdomen. Furthermore, as seen in most of the models (n = 74, 69.1%), Convolutional Neural Network (CNN) used as a backbone of deep learning and is used in all tasks including classification, segmentation, and object detection ( Aji et al., 2019 ; Al-Bander et al., 2020 ; Andriani and Mardhiyah, 2019 ; Arnaout et al., 2021 ; Brahma et al., 2021 ; Budd et al., 2019 ; Cai et al., 2020 , 2018 ; Chen et al., 2014 , 2017 , 2020a , 2020b , 2020c ; Deepika et al., 2021 ; Desai et al., 2020 ; Dinesh Simon and Kavitha, 2021 ; Dong et al., 2020 ; Dozen et al., 2020 ; Droste et al., 2020 ; Fathimuthu Joharah and Mohideen, 2020 ; Fiorentino et al., 2021 ; Gao and Noble, 2019 ; Gong et al., 2020 ; Huang et al., 2018 ; Jang et al., 2017 ; Kim et al., 2018 , 2019a ; Komatsu et al., 2021b ; Li et al., 2020 ; Li et al., 2017 ; Lin et al., 2019a , 2019 ; Liu et al., 2020 ; Liu et al., 2019 ; Miyagi et al., 2021 ; Namburete and Noble, 2013 ; Nie et al., 2015a ; Patra et al., 2017 ; Perez-Gonzalez et al., 2020 ; Prieto et al., 2021 ; Pu et al., 2021 ; Qiao and Zulkernine, 2020 ; Qu et al., 2020a ; Ravishankar et al., 2016 ; Ryou et al., 2016 , 2019 ; Sahli et al., 2020 ; Singh et al., 2021a , 2021b ; Sobhaninia et al., 2019 , …”

“…We found that, because of the noise and characteristics of US images, DL still relies on traditional feature extraction techniques in image analysis used to optimize the DL frameworks for better performance. For example, Hough transform was utilized with head and abdomen US images as seen in ( Deepika et al., 2021 ; Jang et al., 2017 ; Kim et al., 2018 ; Nie et al., 2015a ). In addition, image processing techniques such as histogram equalization was used in ( Nie et al., 2015a ).…”

“…For example, Hough transform was utilized with head and abdomen US images as seen in ( Deepika et al., 2021 ; Jang et al., 2017 ; Kim et al., 2018 ; Nie et al., 2015a ). In addition, image processing techniques such as histogram equalization was used in ( Nie et al., 2015a ). Besides these tools, Class Activation Maps (CAM) are a helpful classification tool in computer vision that was utilized for brain visualization in ( Liu et al., 2020 ; Xie et al., 2020a ), fetal part structure in ( Gao and Noble, 2019 ), and heart chamber view in ( Arnaout et al., 2021 ).…”

A Hessian plate filter and shape feature-based approach to automatically localizing the NT VOI of 3D ultrasound data

“…The method requires the prior knowledge of the standard sagittal plane, and the size and position of the fetal head, which can be obtained using our previous work. [17,18] The remainder of this paper is organized as follows. Section 2 describes the automatic method in detail.…”