GPAFormer: Graph-guided Patch Aggregation Transformer for Efficient 3D Medical Image Segmentation

• URL: http://arxiv.org/abs/2604.06658v1
• Date: Wed, 08 Apr 2026 04:17:58 GMT
• Title: GPAFormer: Graph-guided Patch Aggregation Transformer for Efficient 3D Medical Image Segmentation
• Abstract summary: GPAFormer is a lightweight network architecture specifically designed for 3D medical image segmentation.<n> GPAFormer incorporated two core modules: the multi-scale attention-guided stacked aggregation (MASA) and the mutual-aware patch graph aggregator (MPGA)
• Score: 2.209475514606477
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Deep learning has been widely applied to 3D medical image segmentation tasks. However, due to the diversity of imaging modalities, the high-dimensional nature of the data, and the heterogeneity of anatomical structures, achieving both segmentation accuracy and computational efficiency in multi-organ segmentation remains a challenge. This study proposed GPAFormer, a lightweight network architecture specifically designed for 3D medical image segmentation, emphasizing efficiency while keeping high accuracy. GPAFormer incorporated two core modules: the multi-scale attention-guided stacked aggregation (MASA) and the mutual-aware patch graph aggregator (MPGA). MASA utilized three parallel paths with different receptive fields, combined through planar aggregation, to enhance the network's capability in handling structures of varying sizes. MPGA employed a graph-guided approach to dynamically aggregate regions with similar feature distributions based on inter-patch feature similarity and spatial adjacency, thereby improving the discrimination of both internal and boundary structures of organs. Experiments were performed on public whole-body CT and MRI datasets including BTCV, Synapse, ACDC, and BraTS. Compared to the existed 3D segmentation networkd, GPAFormer using only 1.81 M parameters achieved overall highest DSC on BTCV (75.70%), Synapse (81.20%), ACDC (89.32%), and BraTS (82.74%). Using consumer level GPU, the inference time for one validation case of BTCV spent less than one second. The results demonstrated that GPAFormer balanced accuracy and efficiency in multi-organ, multi-modality 3D segmentation tasks across various clinical scenarios especially for resource-constrained and time-sensitive clinical environments.

Related papers

• A Unified Benchmark of Deep Learning Models for Multi-task 3D Brain Tumor Segmentation from Magnetic Resonance Imaging [0.0]
  This work presents a unified benchmark for comparing representative convolutional neural networks (CNNs) and Transformer-based models.<n>Five state-of-the-art three-dimensional segmentation models, including 3D U-Net, SegResNet, Swin UNETR, SegMamba, and SegMambaV2, are evaluated.<n>Performance is assessed using segmentation accuracy metrics together with computational cost indicators, including inference time and the size of each model.
  arXiv  Detail & Related papers  (2026-07-30T21:56:03Z)
• Augmentation is AUtO-Net: Augmentation-Driven Contrastive Multiview Learning for Medical Image Segmentation [3.1002416427168304]
  This thesis focuses on retinal blood vessel segmentation tasks. It provides an extensive literature review of deep learning-based medical image segmentation approaches. It proposes a novel efficient, simple multiview learning framework.
  arXiv  Detail & Related papers  (2023-11-02T06:31:08Z)
• 3DSAM-adapter: Holistic adaptation of SAM from 2D to 3D for promptable tumor segmentation [52.699139151447945]
  We propose a novel adaptation method for transferring the segment anything model (SAM) from 2D to 3D for promptable medical image segmentation. Our model can outperform domain state-of-the-art medical image segmentation models on 3 out of 4 tasks, specifically by 8.25%, 29.87%, and 10.11% for kidney tumor, pancreas tumor, colon cancer segmentation, and achieve similar performance for liver tumor segmentation.
  arXiv  Detail & Related papers  (2023-06-23T12:09:52Z)
• UNETR++: Delving into Efficient and Accurate 3D Medical Image Segmentation [93.88170217725805]
  We propose a 3D medical image segmentation approach, named UNETR++, that offers both high-quality segmentation masks as well as efficiency in terms of parameters, compute cost, and inference speed. The core of our design is the introduction of a novel efficient paired attention (EPA) block that efficiently learns spatial and channel-wise discriminative features. Our evaluations on five benchmarks, Synapse, BTCV, ACDC, BRaTs, and Decathlon-Lung, reveal the effectiveness of our contributions in terms of both efficiency and accuracy.
  arXiv  Detail & Related papers  (2022-12-08T18:59:57Z)
• UNesT: Local Spatial Representation Learning with Hierarchical Transformer for Efficient Medical Segmentation [29.287521185541298]
  We show that UNesT consistently achieves state-of-the-art performance and evaluate its generalizability and data efficiency. We show that UNesT consistently achieves state-of-the-art performance and evaluate its generalizability and data efficiency.
  arXiv  Detail & Related papers  (2022-09-28T19:14:38Z)
• Two-Stream Graph Convolutional Network for Intra-oral Scanner Image Segmentation [133.02190910009384]
  We propose a two-stream graph convolutional network (i.e., TSGCN) to handle inter-view confusion between different raw attributes. Our TSGCN significantly outperforms state-of-the-art methods in 3D tooth (surface) segmentation.
  arXiv  Detail & Related papers  (2022-04-19T10:41:09Z)
• Multi-organ Segmentation Network with Adversarial Performance Validator [10.775440368500416]
  This paper introduces an adversarial performance validation network into a 2D-to-3D segmentation framework. The proposed network converts the 2D-coarse result to 3D high-quality segmentation masks in a coarse-to-fine manner, allowing joint optimization to improve segmentation accuracy. Experiments on the NIH pancreas segmentation dataset demonstrate the proposed network achieves state-of-the-art accuracy on small organ segmentation and outperforms the previous best.
  arXiv  Detail & Related papers  (2022-04-16T18:00:29Z)
• Few-shot image segmentation for cross-institution male pelvic organs using registration-assisted prototypical learning [13.567073992605797]
  This work presents the first 3D few-shot interclass segmentation network for medical images. It uses a labelled multi-institution dataset from prostate cancer patients with eight regions of interest. A built-in registration mechanism can effectively utilise the prior knowledge of consistent anatomy between subjects.
  arXiv  Detail & Related papers  (2022-01-17T11:44:10Z)
• Automatic size and pose homogenization with spatial transformer network to improve and accelerate pediatric segmentation [51.916106055115755]
  We propose a new CNN architecture that is pose and scale invariant thanks to the use of Spatial Transformer Network (STN) Our architecture is composed of three sequential modules that are estimated together during training. We test the proposed method in kidney and renal tumor segmentation on abdominal pediatric CT scanners.
  arXiv  Detail & Related papers  (2021-07-06T14:50:03Z)
• Cross-Modality Brain Tumor Segmentation via Bidirectional Global-to-Local Unsupervised Domain Adaptation [61.01704175938995]
  In this paper, we propose a novel Bidirectional Global-to-Local (BiGL) adaptation framework under a UDA scheme. Specifically, a bidirectional image synthesis and segmentation module is proposed to segment the brain tumor. The proposed method outperforms several state-of-the-art unsupervised domain adaptation methods by a large margin.
  arXiv  Detail & Related papers  (2021-05-17T10:11:45Z)
• Unpaired Multi-modal Segmentation via Knowledge Distillation [77.39798870702174]
  We propose a novel learning scheme for unpaired cross-modality image segmentation. In our method, we heavily reuse network parameters, by sharing all convolutional kernels across CT and MRI. We have extensively validated our approach on two multi-class segmentation problems.
  arXiv  Detail & Related papers  (2020-01-06T20:03:17Z)

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.