State-of-the-Art Stroke Lesion Segmentation at 1/1000th of Parameters

• URL: http://arxiv.org/abs/2503.05531v1
• Date: Fri, 07 Mar 2025 15:58:36 GMT
• Title: State-of-the-Art Stroke Lesion Segmentation at 1/1000th of Parameters
• Authors: Alex Fedorov, Yutong Bu, Xiao Hu, Chris Rorden, Sergey Plis,
• Abstract summary: We introduce a novel multi-scale dilation pattern with an encoder-decoder structure.<n>We operate directly on whole-brain $2563$ MRI volumes.<n>Our results validate MeshNet's strong balance of efficiency and performance.
• Score: 1.581945821289601
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Efficient and accurate whole-brain lesion segmentation remains a challenge in medical image analysis. In this work, we revisit MeshNet, a parameter-efficient segmentation model, and introduce a novel multi-scale dilation pattern with an encoder-decoder structure. This innovation enables capturing broad contextual information and fine-grained details without traditional downsampling, upsampling, or skip-connections. Unlike previous approaches processing subvolumes or slices, we operate directly on whole-brain $256^3$ MRI volumes. Evaluations on the Aphasia Recovery Cohort (ARC) dataset demonstrate that MeshNet achieves superior or comparable DICE scores to state-of-the-art architectures such as MedNeXt and U-MAMBA at 1/1000th of parameters. Our results validate MeshNet's strong balance of efficiency and performance, making it particularly suitable for resource-limited environments such as web-based applications and opening new possibilities for the widespread deployment of advanced medical image analysis tools.

Related papers

• PathSegDiff: Pathology Segmentation using Diffusion model representations [63.20694440934692]
  We propose PathSegDiff, a novel approach for histopathology image segmentation that leverages Latent Diffusion Models (LDMs) as pre-trained featured extractors. Our method utilizes a pathology-specific LDM, guided by a self-supervised encoder, to extract rich semantic information from H&E stained histopathology images. Our experiments demonstrate significant improvements over traditional methods on the BCSS and GlaS datasets.
  arXiv  Detail & Related papers  (2025-04-09T14:58:21Z)
• Performance Analysis of Deep Learning Models for Femur Segmentation in MRI Scan [5.5193366921929155]
  We evaluate and compare the performance of three CNN-based models, i.e., U-Net, Attention U-Net, and U-KAN, and one transformer-based model, SAM 2. The dataset comprises 11,164 MRI scans with detailed annotations of femoral regions. Attention U-Net achieves the highest overall scores, while U-KAN demonstrated superior performance in anatomical regions with a smaller region of interest.
  arXiv  Detail & Related papers  (2025-04-05T05:47:56Z)
• Multi-encoder nnU-Net outperforms Transformer models with self-supervised pretraining [0.0]
  This study addresses the essential task of medical image segmentation, which involves the automatic identification and delineation of anatomical structures and pathological regions in medical images. We propose a novel self-supervised learning Multi-encoder nnU-Net architecture designed to process multiple MRI modalities independently through separate encoders. Our Multi-encoder nnU-Net demonstrates exceptional performance, achieving a Dice Similarity Coefficient (DSC) of 93.72%, which surpasses that of other models such as vanilla nnU-Net, SegResNet, and Swin UNETR.
  arXiv  Detail & Related papers  (2025-04-04T14:31:06Z)
• MedFuncta: Modality-Agnostic Representations Based on Efficient Neural Fields [1.931185411277237]
  We introduce MedFuncta, a modality-agnostic continuous data representation based on neural fields.<n>We demonstrate how to scale neural fields from single instances to large datasets by exploiting redundancy in medical signals.<n>We release a large-scale dataset of > 550k annotated neural fields to promote research in this direction.
  arXiv  Detail & Related papers  (2025-02-20T09:38:13Z)
• ContextMRI: Enhancing Compressed Sensing MRI through Metadata Conditioning [51.26601171361753]
  We propose ContextMRI, a text-conditioned diffusion model for MRI that integrates granular metadata into the reconstruction process.<n>We show that increasing the fidelity of metadata, ranging from slice location and contrast to patient age, sex, and pathology, systematically boosts reconstruction performance.
  arXiv  Detail & Related papers  (2025-01-08T05:15:43Z)
• MRGen: Diffusion-based Controllable Data Engine for MRI Segmentation towards Unannotated Modalities [59.61465292965639]
  This paper investigates a new paradigm for leveraging generative models in medical applications.<n>We propose a diffusion-based data engine, termed MRGen, which enables generation conditioned on text prompts and masks.
  arXiv  Detail & Related papers  (2024-12-04T16:34:22Z)
• LHU-Net: A Light Hybrid U-Net for Cost-Efficient, High-Performance Volumetric Medical Image Segmentation [4.168081528698768]
  We introduce LHU-Net, a streamlined Hybrid U-Net for medical image segmentation. Tested on five benchmark datasets, LHU-Net demonstrated superior efficiency and accuracy.
  arXiv  Detail & Related papers  (2024-04-07T22:58:18Z)
• LiteNeXt: A Novel Lightweight ConvMixer-based Model with Self-embedding Representation Parallel for Medical Image Segmentation [2.0901574458380403]
  We propose a new lightweight but efficient model, namely LiteNeXt, for medical image segmentation. The model is trained from scratch with small amount of parameters (0.71M) and Giga Floating Point Operations Per Second (0.42). Experiments on public datasets including Data Science Bowls, GlaS, ISIC2018, PH2, Sunnybrook, and Lung X-ray data show promising results.
  arXiv  Detail & Related papers  (2024-04-04T01:59:19Z)
• PMFSNet: Polarized Multi-scale Feature Self-attention Network For Lightweight Medical Image Segmentation [6.134314911212846]
  Current state-of-the-art medical image segmentation methods prioritize accuracy but often at the expense of increased computational demands and larger model sizes. We propose PMFSNet, a novel medical imaging segmentation model that balances global local feature processing while avoiding computational redundancy. It incorporates a plug-and-play PMFS block, a multi-scale feature enhancement module based on attention mechanisms, to capture long-term dependencies.
  arXiv  Detail & Related papers  (2024-01-15T10:26:47Z)
• Self-Supervised Neuron Segmentation with Multi-Agent Reinforcement Learning [53.00683059396803]
  Mask image model (MIM) has been widely used due to its simplicity and effectiveness in recovering original information from masked images. We propose a decision-based MIM that utilizes reinforcement learning (RL) to automatically search for optimal image masking ratio and masking strategy. Our approach has a significant advantage over alternative self-supervised methods on the task of neuron segmentation.
  arXiv  Detail & Related papers  (2023-10-06T10:40:46Z)
• Learnable Weight Initialization for Volumetric Medical Image Segmentation [66.3030435676252]
  We propose a learnable weight-based hybrid medical image segmentation approach. Our approach is easy to integrate into any hybrid model and requires no external training data. Experiments on multi-organ and lung cancer segmentation tasks demonstrate the effectiveness of our approach.
  arXiv  Detail & Related papers  (2023-06-15T17:55:05Z)
• Negligible effect of brain MRI data preprocessing for tumor segmentation [36.89606202543839]
  We conduct experiments on three publicly available datasets and evaluate the effect of different preprocessing steps in deep neural networks. Our results demonstrate that most popular standardization steps add no value to the network performance. We suggest that image intensity normalization approaches do not contribute to model accuracy because of the reduction of signal variance with image standardization.
  arXiv  Detail & Related papers  (2022-04-11T17:29:36Z)
• End-to-end Neuron Instance Segmentation based on Weakly Supervised Efficient UNet and Morphological Post-processing [0.0]
  We present an end-to-end weakly-supervised framework to automatically detect and segment NeuN stained neuronal cells on histological images. We integrate the state-of-the-art network, EfficientNet, into our U-Net-like architecture.
  arXiv  Detail & Related papers  (2022-02-17T14:35:45Z)
• InDuDoNet+: A Model-Driven Interpretable Dual Domain Network for Metal Artifact Reduction in CT Images [53.4351366246531]
  We construct a novel interpretable dual domain network, termed InDuDoNet+, into which CT imaging process is finely embedded. We analyze the CT values among different tissues, and merge the prior observations into a prior network for our InDuDoNet+, which significantly improve its generalization performance.
  arXiv  Detail & Related papers  (2021-12-23T15:52:37Z)

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.