Automatic linear measurements of the fetal brain on MRI with deep neural networks

Avisdris, Netanell; Yehuda, Bossmat; Ben-Zvi, Ori; Link-Sourani, Daphna; Ben‐Sira, Liat; Miller, Elka; Zharkov, Elena; Bashat, Dafna Ben; Joskowicz, Leo

doi:10.21203/rs.3.rs-161025/v1

Cited by 2 publications

(2 citation statements)

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“…Normal values for several cerebral structures have been defined by MRI 44 . Super‐resolution images 45 and machine‐learning‐based automatic measurement methods have been developed 46 . When measuring fluid‐containing structures, it is important to remember that MRI measurements are usually around 10% greater than the corresponding ultrasound measurements 47 .…”

Section: Performance Of Fetal Mrimentioning

confidence: 99%

ISUOG Practice Guidelines (updated): performance of fetal magnetic resonance imaging

Prayer

Malinger

Catte

et al. 2023

Ultrasound in Obstet & Gyne

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and Gynecology (ISUOG) is a scientific organization that encourages sound clinical practice and high-quality teaching and research related to diagnostic imaging in women's healthcare. The ISUOG Clinical Standards Committee (CSC) has a remit to develop Practice Guidelines and Consensus Statements as educational recommendations that provide healthcare practitioners with a consensus-based approach, from experts, for diagnostic imaging. They are intended to reflect what is considered by ISUOG to be the best practice at the time at which they are issued. Although ISUOG has made every effort to ensure that Guidelines are accurate when issued, neither the Society nor any of its employees or members accepts liability for the consequences of any inaccurate or misleading data, opinions or statements issued by the CSC. The ISUOG CSC documents are not intended to establish a legal standard of care, because interpretation of the evidence that underpins the Guidelines may be influenced by individual circumstances, local protocol and available resources. Approved Guidelines can be distributed freely with the permission of ISUOG (

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Section: Performance Of Fetal Mrimentioning

confidence: 99%

ISUOG Practice Guidelines (updated): performance of fetal magnetic resonance imaging

Prayer

Malinger

Catte

et al. 2023

Ultrasound in Obstet & Gyne

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“…Recently, methods for comprehensive biometry required for fetal weight estimation have been proposed [6,16]. Similar methods have been developed for other imaging modalities, e.g., fetal MRI [5] and 3D US [17]. These methods rely on obtaining segmentation masks to train fetal anatomy segmentation models, which is time-consuming, and on developing geometric methods for each measurement.…”

Section: Introductionmentioning

confidence: 99%

BiometryNet: Landmark-based Fetal Biometry Estimation from Standard Ultrasound Planes

Avisdris,

Joskowicz,

Dromey

et al. 2022

Preprint

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Fetal growth assessment from ultrasound is based on a few biometric measurements that are performed manually and assessed relative to the expected gestational age. Reliable biometry estimation depends on the precise detection of landmarks in standard ultrasound planes. Manual annotation can be time-consuming and operator dependent task, and may results in high measurements variability. Existing methods for automatic fetal biometry rely on initial automatic fetal structure segmentation followed by geometric landmark detection. However, segmentation annotations are time-consuming and may be inaccurate, and landmark detection requires developing measurement-specific geometric methods. This paper describes BiometryNet, an end-to-end landmark regression framework for fetal biometry estimation that overcomes these limitations. It includes a novel Dynamic Orientation Determination (DOD) method for enforcing measurement-specific orientation consistency during network training. DOD reduces variabilities in network training, increases landmark localization accuracy, thus yields accurate and robust biometric measurements. To validate our method, we assembled a dataset of 3,398 ultrasound images from 1,829 subjects acquired in three clinical sites with seven different ultrasound devices. Comparison and cross-validation of three different biometric measurements on two independent datasets shows that BiometryNet is robust and yields accurate measurements whose errors are lower than the clinically permissible errors, outperforming other existing automated biometry estimation methods. Code is available at https://github.com/netanellavisdris/fetalbiometry.

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Automatic linear measurements of the fetal brain on MRI with deep neural networks

Cited by 2 publications

References 30 publications

ISUOG Practice Guidelines (updated): performance of fetal magnetic resonance imaging

ISUOG Practice Guidelines (updated): performance of fetal magnetic resonance imaging

BiometryNet: Landmark-based Fetal Biometry Estimation from Standard Ultrasound Planes

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