Fetal Skull Reconstruction via Deep Convolutional Autoencoders

TY - CHAP

T1 - Fetal Skull Reconstruction via Deep Convolutional Autoencoders

AU - Cerrolaza, Juan J.

AU - Li, Yuanwei

AU - Biffi, Carlo

AU - Gomez, Alberto

AU - Matthew, Jaqueline

AU - Sinclair, Matthew

AU - Gupta, Chandni

AU - Knight, Caroline L.

AU - Rueckert, Daniel

PY - 2018/10/26

Y1 - 2018/10/26

N2 - Ultrasound (US) imaging is arguably the most commonly used modality for fetal screening. Recently, 3DUS has been progressively adopted in modern obstetric practice, showing promising diagnosis capabilities, and alleviating many of the inherent limitations of traditional 2DUS, such as subjectivity and operator dependence. However, the involuntary movements of the fetus, and the difficulty for the operator to inspect the entire volume in real-time can hinder the acquisition of the entire region of interest. In this paper, we present two deep convolutional architectures for the reconstruction of the fetal skull in partially occluded 3DUS volumes: a TL deep convolutional network (TL-Net), and a conditional variational autoencoder (CVAE). The performance of the two networks was evaluated for occlusion rates up to 50%, both showing accurate results even when only 60% of the skull is included in the US volume (Dice coeff. 0.84\pm 0.04 for CVAE and 0.83\pm 0.03 for TL-Net). The reconstruction networks proposed here have the potential to optimize image acquisition protocols in obstetric sonography, reducing the acquisition time and providing comprehensive anatomical information even from partially occluded images.

AB - Ultrasound (US) imaging is arguably the most commonly used modality for fetal screening. Recently, 3DUS has been progressively adopted in modern obstetric practice, showing promising diagnosis capabilities, and alleviating many of the inherent limitations of traditional 2DUS, such as subjectivity and operator dependence. However, the involuntary movements of the fetus, and the difficulty for the operator to inspect the entire volume in real-time can hinder the acquisition of the entire region of interest. In this paper, we present two deep convolutional architectures for the reconstruction of the fetal skull in partially occluded 3DUS volumes: a TL deep convolutional network (TL-Net), and a conditional variational autoencoder (CVAE). The performance of the two networks was evaluated for occlusion rates up to 50%, both showing accurate results even when only 60% of the skull is included in the US volume (Dice coeff. 0.84\pm 0.04 for CVAE and 0.83\pm 0.03 for TL-Net). The reconstruction networks proposed here have the potential to optimize image acquisition protocols in obstetric sonography, reducing the acquisition time and providing comprehensive anatomical information even from partially occluded images.

UR - http://www.scopus.com/inward/record.url?scp=85056620278&partnerID=8YFLogxK

U2 - 10.1109/EMBC.2018.8512282

DO - 10.1109/EMBC.2018.8512282

M3 - Conference paper

C2 - 30440533

AN - SCOPUS:85056620278

VL - 2018-July

SP - 887

EP - 890

BT - 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2018

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2018

Y2 - 18 July 2018 through 21 July 2018

ER -