English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Timely, accurate and reliable assessment of fetal brain development isessential to reduce short and long-term risks to fetus and mother. Fetal MRI isincreasingly used for fetal brain assessment. Three key biometric linearmeasurements important for fetal brain evaluation are Cerebral BiparietalDiameter (CBD), Bone Biparietal Diameter (BBD), and Trans-Cerebellum Diameter(TCD), obtained manually by expert radiologists on reference slices, which istime consuming and prone to human error. The aim of this study was to develop afully automatic method computing the CBD, BBD and TCD measurements from fetalbrain MRI. The input is fetal brain MRI volumes which may include the fetalbody and the mother's abdomen. The outputs are the measurement values andreference slices on which the measurements were computed. The method, whichfollows the manual measurements principle, consists of five stages: 1)computation of a Region Of Interest that includes the fetal brain with ananisotropic 3D U-Net classifier; 2) reference slice selection with aConvolutional Neural Network; 3) slice-wise fetal brain structures segmentationwith a multiclass U-Net classifier; 4) computation of the fetal brainmidsagittal line and fetal brain orientation, and; 5) computation of themeasurements. Experimental results on 214 volumes for CBD, BBD and TCDmeasurements yielded a mean $L_1$ difference of 1.55mm, 1.45mm and 1.23mmrespectively, and a Bland-Altman 95% confidence interval ($CI_{95}$) of 3.92mm,3.98mm and 2.25mm respectively. These results are similar to the manualinter-observer variability. The proposed automatic method for computingbiometric linear measurements of the fetal brain from MR imaging achieves humanlevel performance. It has the potential of being a useful method for theassessment of fetal brain biometry in normal and pathological cases, and ofimproving routine clinical practice.

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