Deep Learning Spatial Compounding from Multiple Fetal Head Ultrasound Acquisitions

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

“… Network Binarization 12 - 40 weeks Segmentation ( Qiao and Zulkernine, 2020 ) To segment the fetal skull boundary and fetal skull for fetal HC measurement U-NET Squeeze and Excitation (SE) blocks 12 - 40 weeks Segmentation ( Sobhaninia et al., 2019 ) To automatically segment and estimate HC ellipse. Multi-Task network based on Link-Net architecture (MTLN) Ellipse Tuner 12 - 40 weeks Segmentation ( Zhang et al., 2020b ) To capture more information with multiple-channel convolution from US images Multiple-Channel and Atrous MA-Net Encoder and Decoder Module N/A Segmentation ( Zeng et al., 2021 ) To automatically segment fetal ultrasound image and HC biometry Deeply Supervised Attention-Gated (DAG) V-Net Attention-Gated Module 12 - 40 weeks Segmentation ( Perez-Gonzalez et al., 2020 ) To compound a new US volume containing the whole brain anatomy U-NET + Incidence Angle Maps (IAM) CNN Normalized Mutual Information (NMI) 13 to 26 weeks Segmentation ( Zhang et al., 2020a ) To directly measure the head circumference, without having to resort the handcrafted features or manually labeled segmented images. CNN regressor (Reg-Resnet50) N/A 12 - 40 weeks Segmentation ( Fiorentino et al., 2021 ) To propose region-CNN for head localization and centering, and a regression CNN to accurately delineate the HC CNN regressor (U-net) Tiny-YOLOv2 12 - 40 weeks Miscellaneous ( Li et al., 2020 ) To present a novel end-to-end deep learning network to automatically measure the fetal HC, biparietal diameter (BPD), and occipitofrontal diameter (OFD) length from 2D US images FCNN (SAPNet) Regression network 12 - 40 weeks Mi...…”

“…2D US was used in 16 studies and one study used 3D US. Various network architecture were used to segment and locate the skull and perform HC as seen in ( Aji et al., 2019 ; Brahma et al., 2021 ; Budd et al., 2019 ; Desai et al., 2020 ; Namburete and Noble, 2013 ; Perez-Gonzalez et al., 2020 ; Qiao and Zulkernine, 2020 ; Skeika et al., 2020 ; Sobhaninia et al., 2020 , 2019 ; Xu et al., 2021 ; Zeng et al., 2021 ; Zhang et al., 2020b ). Besides identifying HC, in ( Sinclair et al., 2018 ) segmentation was also used to find fetal BPD.…”

“…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 , …”

A Review on Deep-Learning Algorithms for Fetal Ultrasound-Image Analysis

“…The model, when tested on 14 volumes, achieves DSC and IoU of 0.83 and 0.70 respectively. The work in [109] aims at merging several partially occluded US volumes, acquired by placing the US transducer at different projections of the fetal head, to compound a new US volume containing the whole brain anatomy. For this aim, the authors propose a pipeline of 4 CNNs.…”

“…For this aim, the authors propose a pipeline of 4 CNNs. The first 2 CNNs follow what is done in [109], while Fig. 6.…”