A Data-Driven System Based on Deep Learning for Diagnosis Fetal Cavum Septum Pellucidum in Ultrasound Images

Yuzhou Wu, Cheng Peng, Xuechen Chen, Xin Yao and Zhigang Chen ()
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Yuzhou Wu: School of Computer Science and Engineering, Central South University, Changsha 410083, China
Cheng Peng: School of Computer Science and Engineering, Central South University, Changsha 410083, China
Xuechen Chen: School of Computer Science and Engineering, Central South University, Changsha 410083, China
Xin Yao: School of Computer Science and Engineering, Central South University, Changsha 410083, China
Zhigang Chen: School of Computer Science and Engineering, Central South University, Changsha 410083, China

Mathematics, 2022, vol. 10, issue 23, 1-18

Abstract: Cavum septum pellucidum (CSP) is one of the most important physiologic structures that should be detected in Ultrasound (US) scanning for the normal development of the fetal central nervous system. However, manual measurement of CSP is still a difficult and time-consuming task due to the high noise of US images, even for experienced sonographers. Especially considering that maternal mortality remains high in many developing countries, a data-driven system with a medical diagnosis can help sonographers and obstetricians make decisions rapidly and improve their work efficiency. In this study, we propose a novel data-driven system based on deep learning for the diagnosis of CSP called CA-Unet, which consists of a channel attention network to segment the CSP and a post-processing module to measure and diagnose the anomalies of CSP. We collected the US data from three hospitals in China from 2012 to 2018 year to validate the effectiveness of our system. Experiments on a fetal US dataset demonstrated that our proposed system is able to help doctors make decisions and has achieved the highest precision of 79.5% and the largest Dice score of 77.5% in the segmentation of CSP.

Keywords: data-driven system; cavum septum pellucidum (CSP); segmentation; U-net; attention network; deep learning (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2022
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