Fetal Brain MRI Measurements Using a Deep Learning Landmark Network with Reliability Estimation

Netanell Avisdris*, Dafna Ben Bashat, Liat Ben-Sira, Leo Joskowicz

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

We present a new deep learning method, FML, that automatically computes linear measurements in a fetal brain MRI volume. The method is based on landmark detection and estimates their location reliability. It consists of four steps: 1) fetal brain region of interest detection with a two-stage anisotropic U-Net; 2) reference slice selection with a convolutional neural network (CNN); 3) linear measurement computation based on landmarks detection using a novel CNN, FMLNet; 4) measurement reliability estimation using a Gaussian Mixture Model. The advantages of our method are that it does not rely on heuristics to identify the landmarks, that it does not require fetal brain structures segmentation, and that it is robust since it incorporates reliability estimation. We demonstrate our method on three key fetal biometric measurements from fetal brain MRI volumes: Cerebral Biparietal Diameter (CBD), Bone Biparietal Diameter (BBD), and Trans Cerebellum Diameter (TCD). Experimental results on training (N= 164 ) and test (N= 46 ) datasets of fetal MRI volumes yield a 95% confidence interval agreement of 3.70 mm, 2.20 mm and 2.40 mm for CBD, BBD and TCD, in comparison to measurements performed by an expert fetal radiologist. All results were below the interobserver variability, and surpass previously published results. Our method is generic, as it can be directly applied to other linear measurements in volumetric scans and can be used in a clinical setup.

Original languageEnglish
Title of host publicationUncertainty for Safe Utilization of Machine Learning in Medical Imaging, and Perinatal Imaging, Placental and Preterm Image Analysis - 3rd International Workshop, UNSURE 2021, and 6th International Workshop, PIPPI 2021, Held in Conjunction with MICCAI 2021, Proceedings
EditorsCarole H. Sudre, Roxane Licandro, Christian Baumgartner, Andrew Melbourne, Adrian Dalca, Jana Hutter, Ryutaro Tanno, Esra Abaci Turk, Koen Van Leemput, Jordina Torrents Barrena, William M. Wells, Christopher Macgowan
PublisherSpringer Science and Business Media Deutschland GmbH
Pages210-220
Number of pages11
ISBN (Print)9783030877347
DOIs
StatePublished - 2021
Event3rd International Workshop on Uncertainty for Safe Utilization of Machine Learning in Medical Imaging, UNSURE 2021, and the 6th International Workshop on Perinatal, Preterm and Paediatric Image Analysis, PIPPI 2021, held in conjunction with the 24th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2021 - Virtual, Online
Duration: 1 Oct 20211 Oct 2021

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume12959 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference3rd International Workshop on Uncertainty for Safe Utilization of Machine Learning in Medical Imaging, UNSURE 2021, and the 6th International Workshop on Perinatal, Preterm and Paediatric Image Analysis, PIPPI 2021, held in conjunction with the 24th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2021
CityVirtual, Online
Period1/10/211/10/21

Funding

FundersFunder number
Israel Innovation Authority

    Keywords

    • Linear measurements
    • Reliability estimation
    • fetal MRI

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