A Semantic Segmentation Algorithm for Pleural Effusion Based on DBIF-AUNet

• URL: http://arxiv.org/abs/2508.06191v2
• Date: Mon, 22 Sep 2025 07:49:11 GMT
• Title: A Semantic Segmentation Algorithm for Pleural Effusion Based on DBIF-AUNet
• Authors: Ruixiang Tang, Mingda Zhang, Jianglong Qin, Yan Song, Yi Wu, Wei Wu,
• Abstract summary: Pleural effusion semantic segmentation can significantly enhance the accuracy and timeliness of clinical diagnosis and treatment.<n>Existing methods often struggle with diverse image variations and complex edges.<n>We propose the Dual-Branch Interactive Fusion Attention model (DBIF-AUNet) to address these challenges.
• Score: 22.657295396752023
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Pleural effusion semantic segmentation can significantly enhance the accuracy and timeliness of clinical diagnosis and treatment by precisely identifying disease severity and lesion areas. Currently, semantic segmentation of pleural effusion CT images faces multiple challenges. These include similar gray levels between effusion and surrounding tissues, blurred edges, and variable morphology. Existing methods often struggle with diverse image variations and complex edges, primarily because direct feature concatenation causes semantic gaps. To address these challenges, we propose the Dual-Branch Interactive Fusion Attention model (DBIF-AUNet). This model constructs a densely nested skip-connection network and innovatively refines the Dual-Domain Feature Disentanglement module (DDFD). The DDFD module orthogonally decouples the functions of dual-domain modules to achieve multi-scale feature complementarity and enhance characteristics at different levels. Concurrently, we design a Branch Interaction Attention Fusion module (BIAF) that works synergistically with the DDFD. This module dynamically weights and fuses global, local, and frequency band features, thereby improving segmentation robustness. Furthermore, we implement a nested deep supervision mechanism with hierarchical adaptive hybrid loss to effectively address class imbalance. Through validation on 1,622 pleural effusion CT images from Southwest Hospital, DBIF-AUNet achieved IoU and Dice scores of 80.1% and 89.0% respectively. These results outperform state-of-the-art medical image segmentation models U-Net++ and Swin-UNet by 5.7%/2.7% and 2.2%/1.5% respectively, demonstrating significant optimization in segmentation accuracy for complex pleural effusion CT images.

Related papers

• Dual-Encoder Transformer-Based Multimodal Learning for Ischemic Stroke Lesion Segmentation Using Diffusion MRI [5.332404648315838]
  We study ischemic stroke lesion segmentation using multimodal diffusion MRI from the ISLES 2022 dataset.<n>Several state-of-the-art convolutional and transformer-based architectures, including U-Net variants, Swin-UNet, and TransUNet, are benchmarked.<n>Results show that transformer-based models outperform convolutional baselines, and the proposed dual-encoder TransUNet achieves the best performance, reaching a Dice score of 85.4% on the test set.
  arXiv  Detail & Related papers  (2025-12-23T15:24:31Z)
• U-DFA: A Unified DINOv2-Unet with Dual Fusion Attention for Multi-Dataset Medical Segmentation [1.1724961392643483]
  We propose U-DFA, a unified DINOv2-Unet encoder-decoder architecture that integrates a novel Local-Global Fusion Adapter (LGFA) to enhance segmentation performance.<n>Our method achieves state-of-the-art performance on the Synapse and ACDC datasets with only 33% of the trainable model parameters.
  arXiv  Detail & Related papers  (2025-10-01T07:06:49Z)
• UniSegDiff: Boosting Unified Lesion Segmentation via a Staged Diffusion Model [53.34835793648352]
  We propose UniSegDiff, a novel diffusion model framework for lesion segmentation.<n>UniSegDiff addresses lesion segmentation in a unified manner across multiple modalities and organs.<n> Comprehensive experimental results demonstrate that UniSegDiff significantly outperforms previous state-of-the-art (SOTA) approaches.
  arXiv  Detail & Related papers  (2025-07-24T12:33:10Z)
• CFFormer: Cross CNN-Transformer Channel Attention and Spatial Feature Fusion for Improved Segmentation of Heterogeneous Medical Images [29.68616115427831]
  Medical image segmentation plays an important role in computer-aided diagnosis.<n>Due to limitations of medical imaging devices, medical images exhibit significant heterogeneity, posing challenges for segmentation.<n>We propose a hybrid CNN-Transformer model,called CFFormer, which leverages effective channel feature extraction.
  arXiv  Detail & Related papers  (2025-01-07T08:59:20Z)
• CriDiff: Criss-cross Injection Diffusion Framework via Generative Pre-train for Prostate Segmentation [60.61972883059688]
  CriDiff is a two-stage feature injecting framework with a Crisscross Injection Strategy (CIS) and a Generative Pre-train (GP) approach for prostate segmentation. To effectively learn multi-level of edge features and non-edge features, we proposed two parallel conditioners in the CIS. The GP approach eases the inconsistency between the images features and the diffusion model without adding additional parameters.
  arXiv  Detail & Related papers  (2024-06-20T10:46:50Z)
• CDSE-UNet: Enhancing COVID-19 CT Image Segmentation with Canny Edge Detection and Dual-Path SENet Feature Fusion [10.831487161893305]
  CDSE-UNet is a novel UNet-based segmentation model that integrates Canny operator edge detection and a dual-path SENet feature fusion mechanism. We have developed a Multiscale Convolution approach, replacing the standard Convolution in UNet, to adapt to the varied lesion sizes and shapes. Our evaluations on public datasets demonstrate CDSE-UNet's superior performance over other leading models.
  arXiv  Detail & Related papers  (2024-03-03T13:36:07Z)
• Dual-scale Enhanced and Cross-generative Consistency Learning for Semi-supervised Medical Image Segmentation [49.57907601086494]
  Medical image segmentation plays a crucial role in computer-aided diagnosis. We propose a novel Dual-scale Enhanced and Cross-generative consistency learning framework for semi-supervised medical image (DEC-Seg)
  arXiv  Detail & Related papers  (2023-12-26T12:56:31Z)
• MS-MT: Multi-Scale Mean Teacher with Contrastive Unpaired Translation for Cross-Modality Vestibular Schwannoma and Cochlea Segmentation [11.100048696665496]
  unsupervised domain adaptation (UDA) methods have achieved promising cross-modality segmentation performance. We propose a multi-scale self-ensembling based UDA framework for automatic segmentation of two key brain structures. Our method demonstrates promising segmentation performance with a mean Dice score of 83.8% and 81.4%.
  arXiv  Detail & Related papers  (2023-03-28T08:55:00Z)
• Reliable Joint Segmentation of Retinal Edema Lesions in OCT Images [55.83984261827332]
  In this paper, we propose a novel reliable multi-scale wavelet-enhanced transformer network. We develop a novel segmentation backbone that integrates a wavelet-enhanced feature extractor network and a multi-scale transformer module. Our proposed method achieves better segmentation accuracy with a high degree of reliability as compared to other state-of-the-art segmentation approaches.
  arXiv  Detail & Related papers  (2022-12-01T07:32:56Z)
• 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)
• Max-Fusion U-Net for Multi-Modal Pathology Segmentation with Attention and Dynamic Resampling [13.542898009730804]
  The performance of relevant algorithms is significantly affected by the proper fusion of the multi-modal information. We present the Max-Fusion U-Net that achieves improved pathology segmentation performance. We evaluate our methods using the Myocardial pathology segmentation (MyoPS) combining the multi-sequence CMR dataset.
  arXiv  Detail & Related papers  (2020-09-05T17:24:23Z)
• Unsupervised Bidirectional Cross-Modality Adaptation via Deeply Synergistic Image and Feature Alignment for Medical Image Segmentation [73.84166499988443]
  We present a novel unsupervised domain adaptation framework, named as Synergistic Image and Feature Alignment (SIFA) Our proposed SIFA conducts synergistic alignment of domains from both image and feature perspectives. Experimental results on two different tasks demonstrate that our SIFA method is effective in improving segmentation performance on unlabeled target images.
  arXiv  Detail & Related papers  (2020-02-06T13:49:47Z)

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.