NeuroVascU-Net: A Unified Multi-Scale and Cross-Domain Adaptive Feature Fusion U-Net for Precise 3D Segmentation of Brain Vessels in Contrast-Enhanced T1 MRI

• URL: http://arxiv.org/abs/2511.18422v1
• Date: Sun, 23 Nov 2025 12:23:20 GMT
• Title: NeuroVascU-Net: A Unified Multi-Scale and Cross-Domain Adaptive Feature Fusion U-Net for Precise 3D Segmentation of Brain Vessels in Contrast-Enhanced T1 MRI
• Abstract summary: We present NeuroVascU-Net, the first deep learning architecture specifically designed to segment cerebrovascular structures directly from T1CE MRI scans.<n>NeuroVascU-Net achieved a Dice score of 0.8609 and precision of 0.8841, accurately segmenting both major and fine vascular structures.
• Score: 2.3418151985141926
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Precise 3D segmentation of cerebral vasculature from T1-weighted contrast-enhanced (T1CE) MRI is crucial for safe neurosurgical planning. Manual delineation is time-consuming and prone to inter-observer variability, while current automated methods often trade accuracy for computational cost, limiting clinical use. We present NeuroVascU-Net, the first deep learning architecture specifically designed to segment cerebrovascular structures directly from clinically standard T1CE MRI in neuro-oncology patients, addressing a gap in prior work dominated by TOF-MRA-based approaches. NeuroVascU-Net builds on a dilated U-Net and integrates two specialized modules: a Multi-Scale Contextual Feature Fusion ($MSC^2F$) module at the bottleneck and a Cross-Domain Adaptive Feature Fusion ($CDA^2F$) module at deeper hierarchical layers. $MSC^2F$ captures both local and global information via multi-scale dilated convolutions, while $CDA^2F$ dynamically integrates domain-specific features, enhancing representation while keeping computation low. The model was trained and validated on a curated dataset of T1CE scans from 137 brain tumor biopsy patients, annotated by a board-certified functional neurosurgeon. NeuroVascU-Net achieved a Dice score of 0.8609 and precision of 0.8841, accurately segmenting both major and fine vascular structures. Notably, it requires only 12.4M parameters, significantly fewer than transformer-based models such as Swin U-NetR. This balance of accuracy and efficiency positions NeuroVascU-Net as a practical solution for computer-assisted neurosurgical planning.

Related papers

• NeuroTS-Net: Multi-Class Semantic Segmentation of Pediatric Brain Tumors in Multi-Modal MRI [0.8013259197554157]
  NeuroTS-Net is a three-dimensional encoder-decoder convolutional neural network architecture for semantic segmentation.<n>It incorporates a dual-scale raw-detail stream, adaptive low-resolution context selection, and detail-preserving multipath downsampling.<n>NeuroTS-Net was trained on the BraTS 2026 pediatric dataset without external data or pretrained weights.
  arXiv  Detail & Related papers  (2026-09-15T09:05:03Z)
• CoMNeT: A MedNeXt-CorrDiff Framework for Volumetric Brain Tumor Segmentation [1.2048008992808203]
  We show that combining a modern 3D convolutional segmentation model with corrective diffusion-based refinement and ensembling improves volumetric glioma segmentation.<n>We propose CoMNeT, a MedNeXt-CorrDiff framework that uses four MRI modalities as input and predicts ET, TC, and whole tumor.<n>Using five-fold cross-validation, CoMNeT achieved the highest Dice score for most tumor regions, with ET, TC, WT, and average Dice scores of 0.7543 +/- 0.0261, 0.6806 +/- 0.0166, 0.9049 +/- 0.0128, and 0.7798 +/- 0.0184
  arXiv  Detail & Related papers  (2026-06-13T13:51:10Z)
• D3Seg: Dependency-Aware Diffusion for Brain Tumor Segmentation with Missing Modalities [54.87947751720332]
  We propose a novel segmentation model D3Seg to maintain stable performance under missing-modality settings.<n>The proposed model achieves approximately 1.5-2.0% Dice improvement on enhancing tumor (ET) and around 1.0% on tumor core (TC) across multiple missing modality configurations.
  arXiv  Detail & Related papers  (2026-05-21T09:55:42Z)
• S2M-Net: Spectral-Spatial Mixing for Medical Image Segmentation with Morphology-Aware Adaptive Loss [0.0]
  Medical image segmentation requires balancing local precision for boundary-critical clinical applications, global context for anatomical coherence, and computational efficiency for deployment on limited data and hardware a trilemma that existing architectures fail to resolve.<n>We propose S2M-Net, a 4.7M- parameter architecture that achieves global context through two synergistic innovations: (i) Spectral-Selective Token Mixer (SSTM), which exploits the spectral concentration of medical images via truncated 2D FFT with learnable frequency filtering and content-gated spatial projection, avoiding quadratic attention cost while maintaining global receptive fields; and (ii) Morphology-Aware Adaptive Loss (MAS
  arXiv  Detail & Related papers  (2026-01-03T21:03:54Z)
• Rethinking Convergence in Deep Learning: The Predictive-Corrective Paradigm for Anatomy-Informed Brain MRI Segmentation [30.94379425064039]
  We introduce the Predictive-Corrective (PC) paradigm, a framework that decouples the modeling task to fundamentally accelerate learning.<n>PCambaNet is composed of two synergistic modules. First, the Predictive Prior Module (PPM) generates a coarse approximation at low computational cost.<n>Next, the Corrective Residual Network (CRN) learns to model the residual error, focusing the network's full capacity on refining these challenging regions.
  arXiv  Detail & Related papers  (2025-10-17T08:51:33Z)
• 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)
• Full-Head Segmentation of MRI with Abnormal Brain Anatomy: Model and Data Release [1.9492386975158917]
  We collected 98 MRIs with volumetric segmentation labels for a diverse set of human subjects.<n>We developed a MultiAxial network consisting of three 2D U-Net that operate independently in sagittal, axial and coronal planes.
  arXiv  Detail & Related papers  (2025-01-30T19:31:13Z)
• Self-calibrated convolution towards glioma segmentation [45.74830585715129]
  We evaluate self-calibrated convolutions in different parts of the nnU-Net network to demonstrate that self-calibrated modules in skip connections can significantly improve the enhanced-tumor and tumor-core segmentation accuracy.
  arXiv  Detail & Related papers  (2024-02-07T19:51:13Z)
• Leveraging Frequency Domain Learning in 3D Vessel Segmentation [50.54833091336862]
  In this study, we leverage Fourier domain learning as a substitute for multi-scale convolutional kernels in 3D hierarchical segmentation models. We show that our novel network achieves remarkable dice performance (84.37% on ASACA500 and 80.32% on ImageCAS) in tubular vessel segmentation tasks.
  arXiv  Detail & Related papers  (2024-01-11T19:07:58Z)
• Explainable Cost-Sensitive Deep Neural Networks for Brain Tumor Detection from Brain MRI Images considering Data Imbalance [0.0]
  An automated pipeline is proposed, which encompasses five models: CNN, ResNet50, InceptionV3, EfficientNetB0 and NASNetMobile. The performance of the proposed architecture is evaluated on a balanced dataset and found to yield an accuracy of 99.33% for fine-tuned InceptionV3 model. To further optimize the training process, a cost-sensitive neural network approach has been proposed in order to work with imbalanced datasets.
  arXiv  Detail & Related papers  (2023-08-01T15:35:06Z)
• Bayesian Neural Network Language Modeling for Speech Recognition [59.681758762712754]
  State-of-the-art neural network language models (NNLMs) represented by long short term memory recurrent neural networks (LSTM-RNNs) and Transformers are becoming highly complex. In this paper, an overarching full Bayesian learning framework is proposed to account for the underlying uncertainty in LSTM-RNN and Transformer LMs.
  arXiv  Detail & Related papers  (2022-08-28T17:50:19Z)
• A Neural Ordinary Differential Equation Model for Visualizing Deep Neural Network Behaviors in Multi-Parametric MRI based Glioma Segmentation [3.1435638364138105]
  We develop a neural ordinary differential equation (ODE) model for visualizing deep neural network (DNN) during multi-parametric MRI (mp-MRI) based glioma segmentation. All neural ODE models successfully illustrated image dynamics as expected.
  arXiv  Detail & Related papers  (2022-03-01T17:16:41Z)
• Hepatic vessel segmentation based on 3Dswin-transformer with inductive biased multi-head self-attention [46.46365941681487]
  We propose a robust end-to-end vessel segmentation network called Indu BIased Multi-Head Attention Vessel Net. We introduce the voxel-wise embedding rather than patch-wise embedding to locate precise liver vessel voxels. On the other hand, we propose inductive biased multi-head self-attention which learns inductive biased relative positional embedding from absolute position embedding.
  arXiv  Detail & Related papers  (2021-11-05T10:17:08Z)
• Brain Image Synthesis with Unsupervised Multivariate Canonical CSC$\ell_4$Net [122.8907826672382]
  We propose to learn dedicated features that cross both intre- and intra-modal variations using a novel CSC$ell_4$Net.
  arXiv  Detail & Related papers  (2021-03-22T05:19:40Z)
• Deep Learning Estimation of Multi-Tissue Constrained Spherical Deconvolution with Limited Single Shell DW-MRI [2.903217519429591]
  Deep learning can be used to estimate the information content captured by 8th order constrained spherical deconvolution (CSD) We examine two network architectures: Sequential network of fully connected dense layers with a residual block in the middle (ResDNN), and Patch based convolutional neural network with a residual block (ResCNN) The fiber orientation distribution function (fODF) can be recovered with high correlation as compared to the ground truth of MT-CST, which was derived from the multi-shell DW-MRI acquisitions.
  arXiv  Detail & Related papers  (2020-02-20T15:59:03Z)

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.