HES-UNet: A U-Net for Hepatic Echinococcosis Lesion Segmentation

• URL: http://arxiv.org/abs/2412.06530v1
• Date: Mon, 09 Dec 2024 14:33:55 GMT
• Title: HES-UNet: A U-Net for Hepatic Echinococcosis Lesion Segmentation
• Authors: Jiayan Chen, Kai Li, Zhanjin Wang, Zhan Wang, Jianqiang Huang,
• Abstract summary: HES-UNet is an efficient and accurate model for HE lesion segmentation.<n>Model combines convolutional layers and attention modules to capture local and global features.<n>Experiments show that HES-UNet achieves state-of-the-art performance on our dataset.
• Score: 31.698319244945793
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Hepatic echinococcosis (HE) is a prevalent disease in economically underdeveloped pastoral areas, where adequate medical resources are usually lacking. Existing methods often ignore multi-scale feature fusion or focus only on feature fusion between adjacent levels, which may lead to insufficient feature fusion. To address these issues, we propose HES-UNet, an efficient and accurate model for HE lesion segmentation. This model combines convolutional layers and attention modules to capture local and global features. During downsampling, the multi-directional downsampling block (MDB) is employed to integrate high-frequency and low-frequency features, effectively extracting image details. The multi-scale aggregation block (MAB) aggregates multi-scale feature information. In contrast, the multi-scale upsampling Block (MUB) learns highly abstract features and supplies this information to the skip connection module to fuse multi-scale features. Due to the distinct regional characteristics of HE, there is currently no publicly available high-quality dataset for training our model. We collected CT slice data from 268 patients at a certain hospital to train and evaluate the model. The experimental results show that HES-UNet achieves state-of-the-art performance on our dataset, achieving an overall Dice Similarity Coefficient (DSC) of 89.21%, which is 1.09% higher than that of TransUNet. The project page is available at https://chenjiayan-qhu.github.io/HES-UNet-page.

Related papers

• EAGLE: An Efficient Global Attention Lesion Segmentation Model for Hepatic Echinococcosis [31.698319244945793]
  We propose a U-shaped network composed of a Progressive Visual State Space (PVSS) encoder and a Hybrid Visual State Space (HVSS) decoder.<n>The network achieves state-of-the-art performance with a Dice Similarity Coefficient (DSC) of 89.76%, surpassing MSVM-UNet by 1.61%.
  arXiv  Detail & Related papers  (2025-06-25T11:42:05Z)
• CLIP Meets Diffusion: A Synergistic Approach to Anomaly Detection [54.85000884785013]
  Anomaly detection is a complex problem due to the ambiguity in defining anomalies, the diversity of anomaly types, and the scarcity of training data.<n>We propose CLIPfusion, a method that leverages both discriminative and generative foundation models.<n>We believe that our method underscores the effectiveness of multi-modal and multi-model fusion in tackling the multifaceted challenges of anomaly detection.
  arXiv  Detail & Related papers  (2025-06-13T13:30:15Z)
• An Arbitrary-Modal Fusion Network for Volumetric Cranial Nerves Tract Segmentation [21.228897192093573]
  We propose a novel arbitrary-modal fusion network for volumetric cranial nerves (CNs) tract segmentation, called CNTSeg-v2.<n>Our model encompasses an Arbitrary-Modal Collaboration Module (ACM) designed to effectively extract informative features from other auxiliary modalities.<n>Our CNTSeg-v2 achieves state-of-the-art segmentation performance, outperforming all competing methods.
  arXiv  Detail & Related papers  (2025-05-05T06:00:41Z)
• FORESEE: Multimodal and Multi-view Representation Learning for Robust Prediction of Cancer Survival [3.4686401890974197]
  We propose a new end-to-end framework, FORESEE, for robustly predicting patient survival by mining multimodal information. Cross-fusion transformer effectively utilizes features at the cellular level, tissue level, and tumor heterogeneity level to correlate prognosis. The hybrid attention encoder (HAE) uses the denoising contextual attention module to obtain the contextual relationship features. We also propose an asymmetrically masked triplet masked autoencoder to reconstruct lost information within modalities.
  arXiv  Detail & Related papers  (2024-05-13T12:39:08Z)
• 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)
• 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)
• 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)
• 3D Medical Image Segmentation based on multi-scale MPU-Net [5.393743755706745]
  This paper proposes a tumor segmentation model MPU-Net for patient volume CT images. It is inspired by Transformer with a global attention mechanism. Compared with the benchmark model U-Net, MPU-Net shows excellent segmentation results.
  arXiv  Detail & Related papers  (2023-07-11T20:46:19Z)
• 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)
• Diff-UNet: A Diffusion Embedded Network for Volumetric Segmentation [41.608617301275935]
  We propose a novel end-to-end framework, called Diff-UNet, for medical volumetric segmentation. Our approach integrates the diffusion model into a standard U-shaped architecture to extract semantic information from the input volume effectively. We evaluate our method on three datasets, including multimodal brain tumors in MRI, liver tumors, and multi-organ CT volumes.
  arXiv  Detail & Related papers  (2023-03-18T04:06:18Z)
• Superficial White Matter Analysis: An Efficient Point-cloud-based Deep Learning Framework with Supervised Contrastive Learning for Consistent Tractography Parcellation across Populations and dMRI Acquisitions [68.41088365582831]
  White matter parcellation classifies tractography streamlines into clusters or anatomically meaningful tracts. Most parcellation methods focus on the deep white matter (DWM), whereas fewer methods address the superficial white matter (SWM) due to its complexity. We propose a novel two-stage deep-learning-based framework, Superficial White Matter Analysis (SupWMA), that performs an efficient parcellation of 198 SWM clusters from whole-brain tractography.
  arXiv  Detail & Related papers  (2022-07-18T23:07:53Z)
• Decoupled Pyramid Correlation Network for Liver Tumor Segmentation from CT images [22.128902125820193]
  We propose a Decoupled Pyramid Correlation Network (DPC-Net) It exploits attention mechanisms to fully leverage both low and high-level features embedded in FCN to segment liver tumor. It achieves a competitive results with a DSC of 96.2% and an ASSD of 1.636 mm for liver segmentation.
  arXiv  Detail & Related papers  (2022-05-26T07:31:29Z)
• Federated Learning Enables Big Data for Rare Cancer Boundary Detection [98.5549882883963]
  We present findings from the largest Federated ML study to-date, involving data from 71 healthcare institutions across 6 continents. We generate an automatic tumor boundary detector for the rare disease of glioblastoma. We demonstrate a 33% improvement over a publicly trained model to delineate the surgically targetable tumor, and 23% improvement over the tumor's entire extent.
  arXiv  Detail & Related papers  (2022-04-22T17:27:00Z)
• Modeling Shared Responses in Neuroimaging Studies through MultiView ICA [94.31804763196116]
  Group studies involving large cohorts of subjects are important to draw general conclusions about brain functional organization. We propose a novel MultiView Independent Component Analysis model for group studies, where data from each subject are modeled as a linear combination of shared independent sources plus noise. We demonstrate the usefulness of our approach first on fMRI data, where our model demonstrates improved sensitivity in identifying common sources among subjects.
  arXiv  Detail & Related papers  (2020-06-11T17:29:53Z)

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.