Automated Fetal Brain MRI Biometry in Healthy and Pathological Cases

• URL: http://arxiv.org/abs/2608.15692v1
• Date: Sun, 16 Aug 2026 11:48:58 GMT
• Title: Automated Fetal Brain MRI Biometry in Healthy and Pathological Cases
• Abstract summary: We evaluate an automated biometric analysis pipeline that localizes 22 anatomical landmarks on NeSVoR-reconstructed 3D volumes.<n>We compare two landmark localization models, H3DE-Net and SCN, on a heterogeneous cohort of 122 acquisitions.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Automated biometric analysis of fetal brain MRI enables reproducible, observer-independent quantitative assessment, yet existing methods are often restricted to few measurements or evaluated only on healthy cases. We assemble and evaluate an automated biometric analysis pipeline that localizes 22 anatomical landmarks on NeSVoR-reconstructed 3D volumes and derives 11 clinically relevant measurements spanning supratentorial, ventricular, cerebellar, and midline structures. We compare two landmark localization models, H3DE-Net and SCN, on a heterogeneous cohort of 122 acquisitions (both healthy controls and range pathologies). Localization accuracy was assessed with a linear mixed-effects model, agreement with normative growth trajectories with calibrated centile charts, and diagnostic utility with a decision tree classifying VM severity. H3DE-Net achieved significantly lower localization error than SCN across all landmarks (mean 1.36 mm vs. 3.58 mm in HC and 1.90 mm vs. 4.13 mm in PC; p < 0.001), and outperformed a GA-based regression baseline in 7 of 11 measurements. H3DE-Net measurements yielded higher classification AUC in every diagnostic group, with the clearest advantage in separating healthy controls from VM. Decision tree thresholds for ventricular width fell near the clinical 10 mm and 15 mm cut-offs used to define and grade VM.

Related papers

• Anatomy-Change-Aware Bidirectional Selective State-Space Memory for Clinically Deployed Thoracic Radiotherapy Auto-Contouring [0.6439594960617159]
  DAMM-Net++ is a 2.5D architecture for thoracic radiotherapy auto-contouring.<n>It addresses inter-slice surface incoherence, systematic failure on small low-contrast targets, and the absence of per-case reliability signals.<n>It achieves a mean Dice of 0.955 and HD95 of 3.78 mm, with the largest gains on low-contrast organs-at-risk.
  arXiv  Detail & Related papers  (2026-09-11T11:13:06Z)
• Automated 2D and 3D Segmentation of AMD and DME Lesions in OCT [5.625893116312849]
  Age-related macular degeneration and diabetic macular edema are leading causes of vision loss.<n>Most deep-learning segmentation work for optical coherence tomography is validated only in-domain.<n>This work develops and systematically ablates four lesion-segmentation pipelines.
  arXiv  Detail & Related papers  (2026-08-27T13:13:18Z)
• NeuroMosaic: Anatomically Grounded Multimodal Large Language Modeling for Molecularly Aware Glioma Reasoning from 3D MRI and Clinical Narratives [0.7742420215649948]
  We present NeuroMosaic, a 3D multimodal language model that converts multi-sequence brain MRI into anatomy-indexed regional tokens.<n>The architecture combines a multi-resolution volumetric tokenizer, a neuroanatomical graph router, a molecular concept memory, and selective risk control.
  arXiv  Detail & Related papers  (2026-08-04T06:26:50Z)
• AutoIQ: An Ensemble Framework for Automatic Assessment of Geometric Distortion in Prostate Diffusion-Weighted Imaging [39.60379134041974]
  We propose AutoIQ, an ensemble machine learning framework for automatic and quantification classification of geometric distortion severity.<n>A total of 140 retrospective MRI examinations were analyzed, including 33 scans with severe distortion requiring repeat acquisition.<n>AutoIQ can provide automated, quantitative quality assessment for prostate DWI and may help identify scans that require repeat acquisition.
  arXiv  Detail & Related papers  (2026-05-29T22:19:56Z)
• DRBD-Mamba for Robust and Efficient Brain Tumor Segmentation with Analytical Insights [54.87947751720332]
  Accurate brain tumor segmentation is significant for clinical diagnosis and treatment.<n>Mamba-based State Space Models have demonstrated promising performance.<n>We propose a dual-resolution bi-directional Mamba that captures multi-scale long-range dependencies with minimal computational overhead.
  arXiv  Detail & Related papers  (2025-10-16T07:31:21Z)
• Explainable Anatomy-Guided AI for Prostate MRI: Foundation Models and In Silico Clinical Trials for Virtual Biopsy-based Risk Assessment [3.5408411348831232]
  We present a fully automated, anatomically guided deep learning pipeline for prostate cancer (PCa) risk stratification using routine MRI.<n>The pipeline integrates three key components: an nnU-Net module for segmenting the prostate gland and its zones on axial T2-weighted MRI; a classification module based on the DiceedPT Swin Transformer foundation model, fine-tuned on 3D patches with optional anatomical priors and clinical data; and a VAE-GAN framework for generating counterfactual heatmaps that localize decision-driving image regions.
  arXiv  Detail & Related papers  (2025-05-23T14:40:09Z)
• A weakly-supervised deep learning model for fast localisation and delineation of the skeleton, internal organs, and spinal canal on Whole-Body Diffusion-Weighted MRI (WB-DWI) [0.0]
  We developed an automated deep-learning pipeline based on a 3D patch-based Residual U-Net architecture.<n>We employed a multi-centre WB-DWI dataset comprising 532 scans from patients with Advanced Prostate Cancer (APC) or Multiple Myeloma (MM)<n>Relative median ADC differences between automated and manual full-body delineations were below 10%.<n>The model was 12x faster than the atlas-based registration algorithm.
  arXiv  Detail & Related papers  (2025-03-26T17:03:46Z)
• AXIAL: Attention-based eXplainability for Interpretable Alzheimer's Localized Diagnosis using 2D CNNs on 3D MRI brain scans [43.06293430764841]
  This study presents an innovative method for Alzheimer's disease diagnosis using 3D MRI designed to enhance the explainability of model decisions. Our approach adopts a soft attention mechanism, enabling 2D CNNs to extract volumetric representations. With voxel-level precision, our method identified which specific areas are being paid attention to, identifying these predominant brain regions.
  arXiv  Detail & Related papers  (2024-07-02T16:44:00Z)
• Analysis of the BraTS 2023 Intracranial Meningioma Segmentation Challenge [44.76736949127792]
  We describe the design and results from the BraTS 2023 Intracranial Meningioma Challenge.<n>The BraTS Meningioma Challenge differed from prior BraTS Glioma challenges in that it focused on meningiomas.<n>The top ranked team had a lesion-wise median dice similarity coefficient (DSC) of 0.976, 0.976, and 0.964 for enhancing tumor, tumor core, and whole tumor.
  arXiv  Detail & Related papers  (2024-05-16T03:23:57Z)
• Estimation of mitral valve hinge point coordinates -- deep neural net for echocardiogram segmentation [0.0]
  We propose a fully automatic detection method for mitral valve hinge points. The method extracts the mitral valve hinge points from the resulting segmentations in a second step. Results measured with this automatic detection method were compared to reference coordinate values.
  arXiv  Detail & Related papers  (2023-01-20T19:46:16Z)
• A Global Benchmark of Algorithms for Segmenting Late Gadolinium-Enhanced Cardiac Magnetic Resonance Imaging [90.29017019187282]
  " 2018 Left Atrium Challenge" using 154 3D LGE-MRIs, currently the world's largest cardiac LGE-MRI dataset. Analyse of the submitted algorithms using technical and biological metrics was performed. Results show the top method achieved a dice score of 93.2% and a mean surface to a surface distance of 0.7 mm.
  arXiv  Detail & Related papers  (2020-04-26T08:49:17Z)
• Lung Infection Quantification of COVID-19 in CT Images with Deep Learning [41.35413216175024]
  Deep learning system developed to automatically quantify infection regions of interest. Human-in-the-loop strategy adopted to assist radiologists for infection region segmentation.
  arXiv  Detail & Related papers  (2020-03-10T11:58:40Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.