DALight-3D: A Lightweight 3D U-Net for Brain Tumor Segmentation from Multi-Modal MRI

• URL: http://arxiv.org/abs/2605.04518v1
• Date: Wed, 06 May 2026 05:54:44 GMT
• Title: DALight-3D: A Lightweight 3D U-Net for Brain Tumor Segmentation from Multi-Modal MRI
• Abstract summary: DALight-3D is a compact 3D U-Net variant that combines depthwise separable 3D convolutions, identifier-conditioned normalization, cross-slice attention, and adaptive skip fusion.<n>Results show DALight-3D offers a favorable accuracy-efficiency trade-off within the present benchmark setting.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Automatic brain tumor segmentation from multi-modal MRI remains challenging because volumetric models often incur substantial computational cost. This paper presents DALight-3D, a compact 3D U-Net variant that combines depthwise separable 3D convolutions, identifier-conditioned normalization, cross-slice attention, and adaptive skip fusion. The method is evaluated on the Medical Segmentation Decathlon Task01 BrainTumour benchmark under matched optimization settings against standard 3D U-Net, Attention U-Net, Residual 3D U-Net, and V-Net baselines. In the reported 50-epoch comparison, DALight-3D achieves a mean Dice of 0.727 with 2.22M parameters, compared with 0.710 Dice and 3.20M parameters for Residual 3D U-Net. Component-wise ablations show consistent performance degradation when SepConv, identifier-conditioned normalization, CSA, or SSFB is removed. These results indicate that DALight-3D offers a favorable accuracy-efficiency trade-off within the present benchmark setting.

Related papers

• DAMamba-UNet3D: A Parameter-Efficient Mamba State Space U-Net with Dynamic Adaptive Scan for 3D Medical Image Segmentation [1.9414157088543738]
  DAMamba-UNet3D is a hybrid encoder-decoder that integrates tri-plane 3D-DAS blocks at encoder stages E2-E4 while retaining convolutions elsewhere.<n>On BraTS 2020 five-fold cross-validation, DAMamba-UNet3D achieves mean Dice 0.815 +/-0.013 at 13x lower parameter cost than SegMamba.
  arXiv  Detail & Related papers  (2026-07-21T16:42:48Z)
• 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)
• Comparative Analysis of 3D Convolutional and 2.5D Slice-Conditioned U-Net Architectures for MRI Super-Resolution via Elucidated Diffusion Models [0.0]
  We investigate an elucidated diffusion model (EDM) framework for brain MRI SR.<n>We compare two U-Net backbone architectures: a full 3D convolutional U-Net that processes volumetric patches with 3D convolutions and multi-head self-attention, and a 2.5D slice-conditioned U-Net that super-resolves each slice independently while conditioning on an adjacent slice for inter-slice context.
  arXiv  Detail & Related papers  (2026-03-15T23:48:29Z)
• RefineFormer3D: Efficient 3D Medical Image Segmentation via Adaptive Multi-Scale Transformer with Cross Attention Fusion [6.372261626436676]
  RefineFormer3D is a lightweight hierarchical transformer architecture that balances segmentation accuracy and computational efficiency for medical imaging.<n>The model achieves fast inference (8.35 ms per volume on GPU) with low memory requirements, supporting deployment in resource-constrained clinical environments.
  arXiv  Detail & Related papers  (2026-02-18T09:58:59Z)
• LeAD-M3D: Leveraging Asymmetric Distillation for Real-time Monocular 3D Detection [72.97402509843484]
  LeAD-M3D is a state-of-the-art monocular 3D detector that achieves state-of-the-art accuracy and real-time inference without extra modalities.<n>Asymmetric Augmentation Denoising Distillation (A2D2) transfers geometric knowledge from a clean-image teacher to a mixup-noised student.<n>3D-aware Consistent Matching (CM3D) improves prediction-to-ground truth assignment.<n> Confidence-Gated 3D Inference (CGI3D) accelerates detection by restricting expensive 3D regression to top-confidence regions.
  arXiv  Detail & Related papers  (2025-12-05T12:08:18Z)
• Parameter-Efficient Fine-Tuning of 3D DDPM for MRI Image Generation Using Tensor Networks [23.30947697113457]
  We address the challenge of parameter-efficient fine-tuning (PEFT) for 3D U-Net-based denoising diffusion probabilistic models (DDPMs)<n>We propose TenVOO, a novel PEFT method specifically designed for fine-tuning DDPMs with 3D convolutional backbones.<n>Our results demonstrate that TenVOO achieves state-of-the-art performance in multi-scale similarity index measure (MS-SSIM)
  arXiv  Detail & Related papers  (2025-07-24T05:51:51Z)
• TriCLIP-3D: A Unified Parameter-Efficient Framework for Tri-Modal 3D Visual Grounding based on CLIP [52.79100775328595]
  3D visual grounding allows an embodied agent to understand visual information in real-world 3D environments based on human instructions.<n>Existing 3D visual grounding methods rely on separate encoders for different modalities.<n>We propose a unified 2D pre-trained multi-modal network to process all three modalities.
  arXiv  Detail & Related papers  (2025-07-20T10:28:06Z)
• DCFormer: Efficient 3D Vision-Language Modeling with Decomposed Convolutions [6.464464511743737]
  We introduce DCFormer, an efficient 3D image encoder that factorizes 3D convolutions into three parallel 1D convolutions along the depth, height, and width dimensions.<n>In zero-shot and fine-tuned detection of 18 pathologies, DCFormer consistently outperforms state-of-the-art 3D vision encoders.
  arXiv  Detail & Related papers  (2025-02-07T17:10:22Z)
• Efficient Slice Anomaly Detection Network for 3D Brain MRI Volume [2.3633885460047765]
  Current anomaly detection methods excel with benchmark industrial data but struggle with medical data due to varying definitions of 'normal' and 'abnormal' We propose a framework called Simple Slice-based Network (SimpleSliceNet), which utilizes a model pre-trained on ImageNet and fine-tuned on a separate MRI dataset as a 2D slice feature extractor to reduce computational cost.
  arXiv  Detail & Related papers  (2024-08-28T17:20:56Z)
• Homography Loss for Monocular 3D Object Detection [54.04870007473932]
  A differentiable loss function, termed as Homography Loss, is proposed to achieve the goal, which exploits both 2D and 3D information. Our method yields the best performance compared with the other state-of-the-arts by a large margin on KITTI 3D datasets.
  arXiv  Detail & Related papers  (2022-04-02T03:48:03Z)
• Asymmetric 3D Context Fusion for Universal Lesion Detection [55.61873234187917]
  3D networks are strong in 3D context yet lack supervised pretraining. Existing 3D context fusion operators are designed to be spatially symmetric, performing identical operations on each 2D slice like convolutions. We propose a novel asymmetric 3D context fusion operator (A3D), which uses different weights to fuse 3D context from different 2D slices.
  arXiv  Detail & Related papers  (2021-09-17T16:25:10Z)
• 3-Dimensional Deep Learning with Spatial Erasing for Unsupervised Anomaly Segmentation in Brain MRI [55.97060983868787]
  We investigate whether using increased spatial context by using MRI volumes combined with spatial erasing leads to improved unsupervised anomaly segmentation performance. We compare 2D variational autoencoder (VAE) to their 3D counterpart, propose 3D input erasing, and systemically study the impact of the data set size on the performance. Our best performing 3D VAE with input erasing leads to an average DICE score of 31.40% compared to 25.76% for the 2D VAE.
  arXiv  Detail & Related papers  (2021-09-14T09:17:27Z)
• Revisiting 3D Context Modeling with Supervised Pre-training for Universal Lesion Detection in CT Slices [48.85784310158493]
  We propose a Modified Pseudo-3D Feature Pyramid Network (MP3D FPN) to efficiently extract 3D context enhanced 2D features for universal lesion detection in CT slices. With the novel pre-training method, the proposed MP3D FPN achieves state-of-the-art detection performance on the DeepLesion dataset. The proposed 3D pre-trained weights can potentially be used to boost the performance of other 3D medical image analysis tasks.
  arXiv  Detail & Related papers  (2020-12-16T07:11:16Z)

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.