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:10.1007/s11548-021-02436-8

Cited by 17 publications

(11 citation statements)

References 30 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Avisdris et al [ 24 ] have developed a fully automated approach for calculating the bone biparietal diameter (BBD), trans-cerebellum diameter (TCD), and cerebral biparietal diameter (CBD) from fetal brain MRI. The suggested automatic technique for calculating fetal brain biometric linear measurements from MR imaging performs at a manual level.…”

Section: Deep Learning (Dl) Overviewmentioning

confidence: 99%

An Automated Deep Learning Model for the Cerebellum Segmentation from Fetal Brain Images

Sreelakshmy

Titus²,

Sasirekha³

et al. 2022

BioMed Research International

View full text Add to dashboard Cite

Cerebellum measures taken from routinely obtained ultrasound (US) images have been frequently employed to determine gestational age and identify developing central nervous system’s anatomical abnormalities. Standardized cerebellar assessments from large-scale clinical datasets are required to investigate correlations between the growing cerebellum and postnatal neurodevelopmental results. These studies could uncover structural abnormalities that could be employed as indicators to forecast neurodevelopmental and growth consequences. To achieve this, higher-throughput, precise, and impartial measures must be used to replace the existing human, semiautomatic, and advanced algorithms, which seem to be time-consuming and inaccurate. In this article, we presented an innovative deep learning (DL) technique for automatic fetal cerebellum segmentation from 2-dimensional (2D) US brain images. We present ReU-Net, a semantic segmentation network tailored to the anatomy of the fetal cerebellum. Moreover, we use U-Net as a foundation models with the incorporation of residual blocks and Wiener filter over the last 2 layers to segregate the cerebellum (c) from the noisy US data. 590 images for training and 150 images for testing were taken; also, we employed a 5-fold cross-assessment method. Our ReU-Net scored 91%, 92%, 25.42, 98%, 92%, and 94% for Dice Score Coefficient (DSC), F1-score, Hausdorff Distance (HD), accuracy, recall, and precision, correspondingly. The suggested method outperforms the other U-Net predicated techniques by a quantitatively significant margin ( p 0.001 ). Our presented approach can be used to allow high bandwidth imaging techniques in medical study fetal US images as well as biometric evaluation on a broader scale in fetal US images.

show abstract

Section: Deep Learning (Dl) Overviewmentioning

confidence: 99%

An Automated Deep Learning Model for the Cerebellum Segmentation from Fetal Brain Images

Sreelakshmy

Titus²,

Sasirekha³

et al. 2022

BioMed Research International

View full text Add to dashboard Cite

show abstract

“…However, while several automatic approaches for the computation of ultrasound-based biometric linear measurements are provided (Khan et al, 2017 ; van den Heuvel et al, 2018 ; Al-Bander et al, 2019 ), in MRI only a few algorithms are available, e.g. for the evaluation of the cerebral biparietal diameter, the bone biparietal diameter, and the transcerebellar diameter (Avisdris et al, 2021a , b ). These methods mimic the radiologist’s manual annotation workflow, but in some cases lack accuracy in the segmentation of the fetal brain or in the selection of the slice to be used for the measurements.…”

Section: Introductionmentioning

confidence: 99%

Geometric Reliability of Super-Resolution Reconstructed Images from Clinical Fetal MRI in the Second Trimester

et al. 2023

View full text Add to dashboard Cite

Fetal Magnetic Resonance Imaging (MRI) is an important noninvasive diagnostic tool to characterize the central nervous system (CNS) development, significantly contributing to pregnancy management. In clinical practice, fetal MRI of the brain includes the acquisition of fast anatomical sequences over different planes on which several biometric measurements are manually extracted. Recently, modern toolkits use the acquired two-dimensional (2D) images to reconstruct a Super-Resolution (SR) isotropic volume of the brain, enabling three-dimensional (3D) analysis of the fetal CNS.We analyzed 17 fetal MR exams performed in the second trimester, including orthogonal T2-weighted (T2w) Turbo Spin Echo (TSE) and balanced Fast Field Echo (b-FFE) sequences. For each subject and type of sequence, three distinct high-resolution volumes were reconstructed via NiftyMIC, MIALSRTK, and SVRTK toolkits. Fifteen biometric measurements were assessed both on the acquired 2D images and SR reconstructed volumes, and compared using Passing-Bablok regression, Bland-Altman plot analysis, and statistical tests.Results indicate that NiftyMIC and MIALSRTK provide reliable SR reconstructed volumes, suitable for biometric assessments. NiftyMIC also improves the operator intraclass correlation coefficient on the quantitative biometric measures with respect to the acquired 2D images. In addition, TSE sequences lead to more robust fetal brain reconstructions against intensity artifacts compared to b-FFE sequences, despite the latter exhibiting more defined anatomical details.Our findings strengthen the adoption of automatic toolkits for fetal brain reconstructions to perform biometry evaluations of fetal brain development over common clinical MR at an early pregnancy stage.

show abstract

“…Nearly two thirds of the articles ( n = 23, 59%) 6–28 were published in non-clinical journals (computer science, data science and engineering journals), with the remainder published in more clinical and radiologically targeted journals ( n = 16, 41%). 29–44 The vast majority described using AI for imaging the fetal brain ( n = 26, 67%), 11–22,28,30–41,44 and a minority for the fetal body ( n = 5, 13%), 6–9,43 placenta ( n = 6, 15%) 23–27,42 or both ( n = 2, 5%). 10,29 …”

Section: Resultsmentioning

confidence: 99%

“…The ‘use cases’ for AI in fetal MRI imaging were broadly classified into several main categories, and a selection of the most clinically relevant papers are expanded upon in more detail in the text below: Image pre-processing: Dynamic motion correction ( n = 8, 21%) 7–9,14,17,19,22,28 Image post-processing: Segmentation of anatomy ( n = 16, 41%), 6,12,13,15,16,20,21,24,26,27,29,32,34,36,37,40 Automated fetal biometry measurement ( n = 1, 3%), 11 Texture analysis ( n = 1, 3%), 33 Classification of image quality ( n = 1, 3%) 39 Data interpretation: Classification of disease ( n = 3, 8%), 18,30,31 Prognostication of outcomes ( n = 4, 10%), 23,41–43 Gestational age prediction ( n = 2, 5%), 38,44 Generation of clinical 3-D models ( n = 1, 3%) 25 Miscellaneous: Generation of synthetic data ( n = 2, 5%) 10,35 …”

Section: Resultsmentioning

confidence: 99%

Artificial intelligence applied to fetal MRI: A scoping review of current research

Meshaka

Gaunt

Shelmerdine

2022

BJR

View full text Add to dashboard Cite

Artificial intelligence (AI) is defined as the development of computer systems to perform tasks normally requiring human intelligence. A subset of AI, known as machine learning (ML), takes this further by drawing inferences from patterns in data to ‘learn’ and ‘adapt’ without explicit instructions meaning that computer systems can ‘evolve’ and hopefully improve without necessarily requiring external human influences. The potential for this novel technology has resulted in great interest from the medical community regarding how it can be applied in healthcare. Within radiology, the focus has mostly been for applications in oncological imaging, although new roles in other subspecialty fields are slowly emerging. In this scoping review we performed a literature search of the current state-of-the-art and emerging trends for the use of artificial intelligence as applied to fetal magnetic resonance imaging (MRI). Our search yielded several publications covering AI tools for anatomical organ segmentation, improved imaging sequences and aiding in diagnostic applications such as automated biometric fetal measurements and the detection of congenital and acquired abnormalities. We highlight our own perceived gaps in this literature and suggest future avenues for further research. It is our hope that the information presented highlights the varied ways and potential that novel digital technology could make an impact to future clinical practice with regards to fetal MRI.

show abstract

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

Cited by 17 publications

References 30 publications

An Automated Deep Learning Model for the Cerebellum Segmentation from Fetal Brain Images

An Automated Deep Learning Model for the Cerebellum Segmentation from Fetal Brain Images

Geometric Reliability of Super-Resolution Reconstructed Images from Clinical Fetal MRI in the Second Trimester

Artificial intelligence applied to fetal MRI: A scoping review of current research

Contact Info

Product

Resources

About