ArticleBMC medical informatics and decision making2024
Automatic segmentation of 15 critical anatomical labels and measurements of cardiac axis and cardiothoracic ratio in fetal four chambers using nnU-NetV2.
Article in BMC medical informatics and decision making, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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9 citing papers in PubMed.
- A Multi-Model, Pixel-Native Framework for Automated Computed Tomography Series Labeling and Characterization: Proof-of-Concept Study.JMIR medical informatics · 2026Article
- Topogram-based anatomical labelling of CT series: anatomy-aware CT data processing using deep learning.European radiology · 2026Article
- 4-D Reconstruction of Fetal Left Ventricle From Echocardiography via 2.5-D Radial Segmentation and Graph-Fourier Reconstruction.IEEE transactions on medical imaging · 2026Article
- Automated interpretation of fetal cardiac function evaluation from the echocardiogram.NPJ digital medicine · 2026Article
- Artificial Intelligence-Based Automated Assessment of the Four-Chamber View in Fetal Cardiac Ultrasound Videos.Bioengineering (Basel, Switzerland) · 2026Article
- Automated segmentation and diagnosis of parotid tumors using a combined deep learning and radiomics model on T2-weighted MRI: a multi-vendor validation study.Cancer imaging : the official publication of the International Cancer Imaging Society · 2025Article
- Federated nnU-Net for privacy-preserving medical image segmentation.Scientific reports · 2025Article
- Automatic Human Embryo Volume Measurement in First Trimester Ultrasound From the Rotterdam Periconception Cohort: Quantitative and Qualitative Evaluation of Artificial Intelligence.Journal of medical Internet research · 2025Article
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Abstract
backgroundAccurate segmentation of critical anatomical structures in fetal four-chamber view images is essential for the early detection of congenital heart defects. Current prenatal screening methods rely on manual measurements, which are time-consuming and prone to inter-observer variability. This study develops an AI-based model using the state-of-the-art nnU-NetV2 architecture for automatic segmentation and measurement of key anatomical structures in fetal four-chamber view images.
methodsA dataset, consisting of 1,083 high-quality fetal four-chamber view images, was annotated with 15 critical anatomical labels and divided into training/validation (867 images) and test (216 images) sets. An AI-based model using the nnU-NetV2 architecture was trained on the annotated images and evaluated using the mean Dice coefficient (mDice) and mean intersection over union (mIoU) metrics. The model's performance in automatically computing the cardiac axis (CAx) and cardiothoracic ratio (CTR) was compared with measurements from sonographers with varying levels of experience.
resultsThe AI-based model achieved a mDice coefficient of 87.11% and an mIoU of 77.68% for the segmentation of critical anatomical structures. The model's automated CAx and CTR measurements showed strong agreement with those of experienced sonographers, with respective intraclass correlation coefficients (ICCs) of 0.83 and 0.81. Bland-Altman analysis further confirmed the high agreement between the model and experienced sonographers.
conclusionWe developed an AI-based model using the nnU-NetV2 architecture for accurate segmentation and automated measurement of critical anatomical structures in fetal four-chamber view images. Our model demonstrated high segmentation accuracy and strong agreement with experienced sonographers in computing clinically relevant parameters. This approach has the potential to improve the efficiency and reliability of prenatal cardiac screening, ultimately contributing to the early detection of congenital heart defects.
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