Metraipolis › archive › dc_cb042bcfd2cc

Towards Reliable and Reproducible Fetal Brain Biometry: A Deep Learning Approach Using MRI — arXiv:2608.03724

Source: arXiv (en)
Original: https://arxiv.org/abs/2608.03724
License (as archived): CC0-1.0
Archived: 2026-10-31T12:59:46.524Z
SHA-256 of the archived text: dece4130a38eb633fc090dd3b6f4908eb39c77a61e82e0e185dea9572a5fd63d
Stable URL of this page: https://metraipolis.com/documento/dc_cb042bcfd2cc

How to cite

Towards Reliable and Reproducible Fetal Brain Biometry: A Deep Learning Approach Using MRI — arXiv:2608.03724 — arXiv (en), https://arxiv.org/abs/2608.03724 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_cb042bcfd2cc (Metraipolis archive dc_cb042bcfd2cc, sha256 dece4130a38eb633fc090dd3b6f4908eb39c77a61e82e0e185dea9572a5fd63d)

BibTeX

@misc{dccb042bcfd2cc,
  title = {Towards Reliable and Reproducible Fetal Brain Biometry: A Deep Learning Approach Using MRI — arXiv:2608.03724},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2608.03724},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_cb042bcfd2cc; sha256 dece4130a38eb633fc090dd3b6f4908eb39c77a61e82e0e185dea9572a5fd63d},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2608.03724. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Fetal brain biometry is essential for quantitative assessment of brain development, supporting gestational age estimation, developmental monitoring, and detection of abnormalities. In clinical practice, measurements are manually performed, making them time-consuming and prone to variability. While automated approaches have been proposed, reproducible methods remain limited, particularly those providing anatomically interpretable landmark localization. We present a fully automated deep learning-based framework for reliable and reproducible brain biometry from 3D super-resolution-reconstructed fetal brain MRI. The proposed four-step pipeline derives biometric parameters by jointly estimating linear measurements and their corresponding anatomical landmarks. A 3D convolutional neural network is trained to regress landmark coordinates from brain segmentation label maps, followed by measurement-specific geometric optimization to refine landmark positions and compute measurements. The pipeline is evaluated on two publicly available fetal MRI datasets comprising 150 volumes (gestational age range: 20-37 weeks) acquired across different scanners and protocols, assessing five key biometric measurements across varying acquisition settings and providing a comprehensive evaluation of both measurement accuracy and landmark localization using quantitative metrics and visual assessment. Compared with the only available automated pipeline, the proposed method achieves comparable or improved accuracy for most measurements. In conclusion, we introduce a straightforward pipeline for reliable biometry estimations, with efficiency, interpretability and scalability that support integration into clinical workflows.

Tema degli esploratori: statistica e scienza dei dati (retro-catalogo arXiv: annunci del 2026-08-05 in cs.CV).
Categorie arXiv: Computer Vision and Pattern Recognition (cs.CV).
Pagina del lavoro: https://arxiv.org/abs/2608.03724 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-10-31T12:45:33.274Z dalla pagina https://arxiv.org/catchup/cs.CV/2026-08-05?abs=True (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).