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medical image segmentation assisted with clinical inputs via language encoder in a deep learning framework

MACHINE LEARNING-SCIENCE AND TECHNOLOGY 2025年第1期6卷

作者： Zhao, Hengrui Wang, Biling Mistry, Deepkumar Wang, Jing Dohopolski, Michael Yang, Daniel Lu, Weiguo Jiang, Steve Nguyen, Dan Univ Texas Southwestern Med Ctr Med Artificial Intelligence & Automat MAIA Lab Dallas TX 75390 USA Univ Texas Southwestern Med Ctr Dept Radiat Oncol Dallas TX 75390 USA

Introduction: Auto-segmentation of tumor volumes and organs at risk (OARs) is a critical step in cancer radiotherapy treatment planning, where rapid, precise adjustments to treatment plans are required to match the patient anatomy. Although auto-segmentation has been clinically accepted for most OARs, auto-segmentation of tumor volumes, particularly clinical target volumes (CTVs), remains a challenge. This difficulty arises because images alone are often insufficient to capture the necessary information for accurate delineation of microscopic tumor invasion invisible on the image itself. Methods: We propose a deep learning-based medical image segmentation framework designed to mimic the clinical process of delineating CTVs and OARs. At its core, the model performs precise segmentation of medical images while enhancing accuracy by integrating clinical information in text format. A transformer-based text encoder converts textual clinical data into vectors, which are incorporated into the segmentation process with image features. This integration bridges the gap between traditional automated segmentation methods and clinician-guided, context-rich delineations. The framework's effectiveness is demonstrated through a prostate segmentation example in the context of radiation therapy for localized prostate cancer, where incorporating clinical context significantly impacts the delineation process. Results: In our experiments, we included additional clinical information potentially influencing clinicians' prostate segmentation. The results show that our proposed method not only outperforms the baseline model, but also surpasses current state-of-the-art methods, with or without clinical contexts. Furthermore, our method demonstrates high performance even with limited data. Conclusion: This proposed segmentation framework has shown to significantly improve auto-segmentation, particularly for CTVs, in cancer radiotherapy.

关键词： medical image segmentation transformer CLIP deep learning language encoder

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medical image segmentation Network Based on Dual-Encoder Interactive Fusion

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APPLIED SCIENCES-BASEL 2025年第7期15卷 3785-3785页

作者： Yang, Hong Fan, Yong Yang, Ping Southwest Univ Sci & Technol Sch Comp Sci & Technol Mianyang 621010 Peoples R China

Hybrid CNN-Transformer networks seek to merge the local feature extraction capabilities of CNNs with the long-range dependency modeling abilities of Transformers, aiming to simultaneously address both local details and global contextual information. However, in many existing studies, CNNs and Transformers are often combined through the straightforward fusion of encoder features, which does not promote effective interaction between the two, thus limiting the potential benefits of each architecture. To overcome this shortfall, this study introduces a novel medical image segmentation (MIS) network, designated as DEFI-Net, which is based on dual-encoder interactive fusion. This network enhances segmentation performance by fostering interactive learning and feature fusion between the CNN and Transformer encoders. Specifically, during the encoding phase, DEFI-Net utilizes parallel encoding with both the CNN and Transformer to extract local and global features from the input images. The global-local interaction learning (GLIL) module then enables both the Transformer and CNN to assimilate global semantics and local details from each other, fully leveraging the strengths of the two encoders. In the feature fusion phase, the global-local feature fusion (GLFF) module integrates features from both encoders, using both global and local information to produce a more precise and comprehensive representation of features. Extensive experiments conducted on multiple public datasets, including multi-organ, cardiac, and colon polyp datasets, demonstrate that DEFI-Net surpasses several existing methods in terms of segmentation accuracy, thus highlighting its effectiveness and robustness in MIS tasks.

关键词： medical image segmentation image segmentation deep learning hybrid CNN-Transformer model

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medical image segmentation with hybrid neural networks

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PHYSICA SCRIPTA 2025年第3期100卷 036001-036001页

作者： Li, Chuangwei Li, Ling-Fang Wang, Xiaojun Luo, Ming-Xing Southwest Jiaotong Univ Sch Informat Sci & Technol Chengdu 610031 Peoples R China Dublin City Univ Sch Elect Engn Dublin Ireland

Recent advancements in CNN and Transformer-based models have significantly advanced medical image segmentation. CNN-based models are less effective at capturing global contexts than Transformer-based models and Transformers have structural disadvantages that limit their ability to capture detailed spatial information. In this paper, we introduce an Dual-Encoder Fusion Model that incorporates a novel Correlation Fusion Module (CFM) for medical image segmentation tasks. This model leverages the strengths of Convolutional Neural Networks (CNN) for local context modeling and Transformers for comprehending long-range dependencies in pixel interactions. Experimental results demonstrate a substantial improvement over existing models on the Synapse dataset, achieving enhancements of 2.28% and 3.47% on the dice metric for Aorta and Pancreas organs respectively. Additionally, our model attains the highest mean HD95 score of 9.05 on the Synapse dataset while utilizing fewer parameters. When evaluated on the MSD datasets, our model outperforms a fine-tuned nnUNet in three out of five tumor detection tasks and maintains competitive performance in three out of four organ boundary delineation tasks. Notably, on the MSD-Lung dataset, our model surpasses a fine-tuned nnUNet on the dice metric by 6.4%. These results underscore the effectiveness of the CFM module within the dual-encoder architecture.

关键词： medical image segmentation neural networks dual-encoder fusion model deep-supervision strategy

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TBConvL-Net: A hybrid deep learning architecture for robust medical image segmentation

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PATTERN RECOGNITION 2025年 158卷

作者： Iqbal, Shahzaib Khan, Tariq M. Naqvi, Syed S. Naveed, Asim Meijering, Erik Abasyn Univ Dept Elect Engn Islamabad Pakistan COMSATS Univ Islamabad Dept Elect & Comp Engn Islamabad Pakistan Univ New South Wales Sch Comp Sci & Engn Sydney Australia Univ Engn & Technol Lahore Dept Comp Sci & Engn Narowal Campus Lahore Pakistan

Deep learning has shown great potential for automated medical image segmentation to improve the precision and speed of disease diagnostics. However, the task presents significant difficulties due to variations in the scale, shape, texture, and contrast of the pathologies. Traditional convolutional neural network (CNN) models have certain limitations when it comes to effectively modelling multiscale context information and facilitating information interaction between skip connections across levels. To overcome these limitations, a novel deep learning architecture is introduced for medical image segmentation, which takes advantage of CNNs and vision transformers. Our proposed model, named TBConvL-Net, involves a hybrid network that combines the local features of a CNN encoder-decoder architecture with long-range and temporal dependencies using biconvolutional long-short-term memory (LSTM) networks and vision transformers (ViT). This enables the model to capture contextual channel relationships in the data and account for the uncertainty of the segmentation over time. Additionally, we introduce a novel composite loss function that considers both segmentation robustness and boundary agreement of the predicted output with the gold standard. Our proposed model shows consistent improvement over the state of the art on ten publicly available datasets of seven different medical imaging modalities.

关键词： medical image segmentation CNN LSTM Vision transformers

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Dilated dendritic learning of global-local feature representation for medical image segmentation

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EXPERT SYSTEMS WITH APPLICATIONS 2025年 264卷

作者： Liu, Zhipeng Song, Yaotong Yi, Junyan Zhang, Zhiming Omura, Masaaki Lei, Zhenyu Gao, Shangce Univ Toyama Fac Engn Toyama 9308555 Japan Beijing Univ Civil Engn & Architecture Dept Comp Sci & Technol Beijing 100044 Peoples R China

medical image segmentation serves as an important tool in the treatment of various medical diseases. However, achieving precise and efficient segmentation remains challenging due to the intricate structures and variations. Although neural network methods based on U-shaped structures have shown impressive results, they often lack effective representation of global-local features, leading to insufficient extraction of multi-scale and contextual information in medical image segmentation tasks. To tackle these challenges, we propose a novel approach: a dilated dendritic module with deep supervision, namely 3DL-Net. It integrates the flexible dilated convolution mechanism into the segmentation architecture, aiming to expand the model's receptive field and capture richer global features. Additionally, in contrast to other segmentation architectures, we innovatively introduce the processing of local feature of shallow structures through the dendritic neuron module in medical images into 3DL-Net. This is the first time dendritic learning has been employed at the channel level and represents a pioneering approach to the local feature process. During the training process, 3DL-Net incorporates a deep supervision mechanism that utilizes our designed loss function. Due to the intermediate supervision signals at various network stages, providing feedback at multiple levels, the model refines its predictions across various scales, contributing to further enhanced segmentation outcomes. To evaluate the effectiveness of our proposed method, we conducted extensive experiments on three medical image datasets to demonstrate significant improvements in segmentation accuracy compared to state-of-the-art models. Our mDice metrics on three datasets achieved 86.61%, 87.87%, and 85.06%, surpassing the second-best models by 3.85%, 1.54%, and 0.95%.

关键词： medical image segmentation Dendritic learning Dilated convolution global-local feature

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Dual stream fusion module integrating 2D and 3D for 3D semi-supervised medical image segmentation

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SIGNAL image AND VIDEO PROCESSING 2025年第4期19卷 1-11页

作者： He, Zhiquan Ouyang, Yating Wen, Yang Shenzhen Univ Guangdong Prov Key Lab Intelligent Informat Proc Nanhai Blvd Shenzhen 518060 Peoples R China

Semi-supervised learning has proven effective in the challenging field of 3D medical image segmentation. However, existing methods typically focus solely on utilizing 3D features, which limits their ability to capture local differences effectively. To address this limitation, we propose a Dual Stream Fusion Model (DSFM) that fuses both 3D and 2D features, enabling the model to capture both global and local information. The model consists of a Cross-Dimensional Attention Fusion Module (CDAFM) and a Self-Adaptive Fusion Module (SAFM). The CDAFM integrates 3D and 2D features using an attention mechanism, while the SAFM merges the segmentation results from both modalities through adaptive weighting. Together, these modules enhance the model's ability to focus on local differences while simultaneously learning global features. Specifically, the 3D decoder receives fused features from both the 3D and 2D encoders, and the final segmentation result is produced by combining the outputs of both the 3D and 2D decoders. Furthermore, we introduce an alignment loss to enforce consistency between the 3D and 2D segmentation sub-networks through regularization. Comprehensive experiments on four publicly available 3D medical datasets demonstrate the effectiveness of our approach. Notably, on the BRaTS19 dataset, which contains only 20% annotated images, we observe a 2.83% improvement in the Jaccard index.

关键词： Dual stream fusion Joint 3D and 2D learning Semi-supervised learning medical image segmentation

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Cross-Modal Conditioned Reconstruction for Language-Guided medical image segmentation

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IEEE TRANSACTIONS ON medical IMAGING 2025年第4期44卷 1821-1835页

作者： Huang, Xiaoshuang Li, Hongxiang Cao, Meng Chen, Long You, Chenyu An, Dong China Agr Univ Coll Informat & Elect Engn Beijing 100083 Peoples R China China Agr Univ Beijing Engn & Technol Res Ctr Internet Things Agr Beijing 100083 Peoples R China Peking Univ Sch Elect & Comp Engn Shenzhen 518055 Peoples R China Mohamed bin Zayed Univ Artificial Intelligence Dept Comp Vis Abu Dhabi U Arab Emirates Hong Kong Univ Sci & Technol Sch Engn Dept Comp Sci & Engn Hong Kong Peoples R China Yale Univ Dept Elect & Comp Engn New Haven CT 06520 USA

Recent developments underscore the potential of textual information in enhancing learning models for a deeper understanding of medical visual semantics. However, language-guided medical image segmentation still faces a challenging issue. Previous works employ implicit architectures to embed textual information. This leads to segmentation results that are inconsistent with the semantics represented by the language, sometimes even diverging significantly. To this end, we propose a novel cross-modal conditioned Reconstruction for Language-guided medical image segmentation (RecLMIS) to explicitly capture cross-modal interactions, which assumes that well-aligned medical visual features and medical notes can effectively reconstruct each other. We introduce conditioned interaction to adaptively predict patches and words of interest. Subsequently, they are utilized as conditioning factors for mutual reconstruction to align with regions described in the medical notes. Extensive experiments demonstrate the superiority of our RecLMIS, surpassing LViT by 3.74% mIoU on the MosMedData+ dataset and 1.89% mIoU on the QATA-CoV19 dataset. More importantly, we achieve a relative reduction of 20.2% in parameter count and a 55.5% decrease in computational load. The code will be available at https://***/ShawnHuang497/RecLMIS.

关键词： image segmentation Visualization image reconstruction Transformers medical diagnostic imaging Decoding Lungs Semantics Electronic mail Convolutional neural networks Language-guided segmentation medical image segmentation vision language

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Dynamic graph consistency and self-contrast learning for semi-supervised medical image segmentation

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NEURAL NETWORKS 2025年 184卷 107063页

作者： Li, Gang Xie, Jinjie Zhang, Ling Cheng, Guijuan Zhang, Kairu Bai, Mingqi Taiyuan Univ Technol Coll Software Taiyuan Peoples R China

Semi-supervised medical image segmentation endeavors to exploit a limited set of labeled data in conjunction with a substantial corpus of unlabeled data, with the aim of training models that can match or even exceed the efficacy of fully supervised segmentation models. Despite the potential of this approach, most existing semisupervised medical image segmentation techniques that employ consistency regularization predominantly focus on spatial consistency at the image level, often neglecting the crucial role of feature-level channel information. To address this limitation, we propose an innovative method that integrates graph convolutional networks with a consistency regularization framework to develop a dynamic graph consistency approach. This method imposes channel-level constraints across different decoders by leveraging high-level features within the network. Furthermore, we introduce a novel self-contrast learning strategy, which performs image-level comparison within the same batch and engages in pixel-level contrast learning based on pixel positions. This approach effectively overcomes traditional contrast learning challenges related to identifying positive and negative samples, reduces computational resource consumption, and significantly improves model performance. Our experimental evaluation on three distinct medical image segmentation datasets indicates that the proposed method demonstrates superior performance across a variety of test scenarios.

关键词： Semi-supervised learning Dynamic graph consistency Self-contrast learning medical image segmentation

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DAUNet: A deformable aggregation UNet for multi-organ 3D medical image segmentation

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PATTERN RECOGNITION LETTERS 2025年 191卷 58-65页

作者： Liu, Qinghao Liu, Min Zhu, Yuehao Liu, Licheng Zhang, Zhe Wang, Yaonan Hunan Univ Coll Elect & Informat Engn Natl Engn Res Ctr Robot Visual Percept & Control T Changsha 410082 Hunan Peoples R China Hunan Univ Int Sci & Technol Innovat Cooperat Base Biomed Ima Changsha 410082 Hunan Peoples R China

medical image segmentation is a critical component of medical image analysis. However, due to the limitations in the size and shape of the receptive field, neural networks often struggle to adapt to segmentation targets of different sizes in 3D medical images. Furthermore, they may not be able to adequately model the intra-slice and inter-slice relationships in 3D medical images. To overcome these challenges, we propose a Deformable Aggregation UNet (DAUNet) for multi-organ segmentation in medical images. We introduce two specially designed modules into the UNet structure, which is composed of residual blocks. Specifically, the Deformable Aggregation Module (DAM) incorporates deformable receptive fields and gate fusion methods. This enhances the fusion of information from various hierarchical levels, enabling DAUNet to adapt to the segmentation of multiple organs or structures at different scales within the abdominal region. Simultaneously, the Fourier Attention Module (FAM) leverages Fourier convolution to enhance long-range dependencies and relationships within the entire 3D volume of medical images, accommodating the detailed structural variations in different directions. The network was assessed on three publicly available datasets (ACDC, BTCV, and BraTSAfrica2024), as well as a private clinical dataset for the TME-CT segmentation task. Compared to existing state-of-the-art (SOTA) methods, DAUNet achieves superior performance, with improvements of 0.96%, 2.52%, 1.60%, and 4.57% in average Dice scores across the four datasets, respectively.

关键词： Deformable aggregation module Fourier attention module medical image segmentation Deep learning

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Exploring refined boundaries and accurate pseudo-labels for semi-supervised medical image segmentation

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APPLIED INTELLIGENCE 2025年第3期55卷 1-20页

作者： Ma, Xiaochen Li, Yanfeng Sun, Jia Chen, Houjin Ren, Yihan Chen, Ziwei Beijing Jiaotong Univ Sch Elect & Informat Engn Beijing 100044 Peoples R China

Due to the tedious and expensive nature of annotating data for medical image segmentation tasks, semi-supervised learning (SSL) methods utilizing a small amount of labeled data have gained widespread attention. However, most existing methods overlook the importance of boundary regions in multi-class tasks and are sensitive to incorrect pseudo-label. In this paper, a novel Dual-Cycled Boundary Refine Network (DCBR-Net) is presented, consisting of two simple yet slightly different segmentation networks and a Boundary Residual Refine (BRR) module. First, a new boundary refine method for the semi-supervised medical image segmentation field is designed, that is a BRR module with a residual architecture. This module is capable of enhancing the representation of boundaries while ensuring the quality of inner regions. Besides, a dual-cycled pseudo-label scheme is designed to train the unlabeled data. Through providing diverse outputs and achieving double supervision for the result, the issue of ineffective guidance caused by model consistency after multiple iterations is alleviated. Furthermore, a novel dynamic loss function is developed based on the prediction disagreement between different models, which can suppress the influence of incorrect pseudo-labels. Extensive experiments conducted on four public medical datasets demonstrate that our network can achieve competitive results, especially with higher reliability in boundary regions. On the ACDC, LA, and Fundus datasets, with only a 20% labeled ratio, our network achieves DSC scores of 90.53%, 90.40%, and 89.19%, respectively, which are comparable to fully supervised performance.

关键词： medical image segmentation Semi-supervised learning Pseudo-label Refined boundary

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