arXiv subjects

Fubao Zhu

Publications and source records attributed to Fubao Zhu.

16 recordsLinked to original sources

arXiv2026

MVMGNN;Multi-View Masked Graph Neural Network for Alzheimer's Disease Diagnosis using Structural MRI

Alzheimer's disease (AD) is a common neurodegenerative disorder, and early diagnosis is of great significance for delaying disease progression and enabling timely intervention. Mild cognitive impairment (MCI), which represents an intermediate clinical stage between cognitively normal aging and AD. Structural magnetic resonance imaging (sMRI) provides detailed characterization of anatomical structures and plays an important role in AD-related brain analysis. However, existing sMRI-based brain network methods typically rely on a single graph construction strategy, limiting their ability to jointly capture spatial relationships and morphological similarities between brain regions. To address these issues, this paper proposes an sMRI-based multi-view masked graph neural network model (MVMGNN) for AD diagnosis. A joint node-edge masking mechanism is proposed to simultaneously select radiomics feature dimensions and structural connections, reducing redundancy during graph learning. Furthermore, a patient-level cross-view gated fusion mechanism is proposed to integrate multi-view representations. Experimental results on the ADNI dataset demonstrate that MVMGNN outperforms several competing approaches in AD classification. Interpretability analysis further demonstrates that MVMGNN is able to identify key brain regions associated with AD, providing useful insights into discriminative patterns in sMRI-based brain networks.Our implementation is publicly available at https://github.com/chenzhao2023/MVMGNN_AD

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arXiv2026

Point Cloud Registration for Fusion between SPECT MPI and CTA Images

Clinical fusion of Single Photon Emission Computed Tomography Myocardial Perfusion Imaging (SPECT MPI) and Computed Tomography Angiography (CTA) remains limited by cross-modality misregistration and reliance on manual landmarks, which can hinder accurate ischemia localization and lesion-level functional assessment. To address this issue, we propose a registration and fusion framework for SPECT MPI and CTA that integrates functional and structural information for comprehensive cardiac evaluation. The proposed pipeline performs U-Net-based segmentation on both modalities. On SPECT MPI, only the left ventricle (LV) is extracted, and anatomical landmarks are automatically derived from characteristic LV structures. On CTA, both ventricles are segmented, and their spatial relationship is used to automatically define landmarks at the interventricular septal junction. Scale-space consistency preprocessing and landmark-driven coarse registration are applied to mitigate initial misalignment. Based on this initialization, multiple fine registration methods are evaluated on LV epicardial surface point clouds, including ICP, SICP, CPD, CluReg, FFD, and BCPD-plus-plus. The resulting transformations are then propagated to voxel-level resampling for high-precision SPECT-CTA fusion. In a retrospective cohort of 60 patients, the proposed framework preserved sub-millimeter coronary detail from CTA while accurately overlaying quantitative SPECT perfusion. Among the evaluated methods, BCPD-plus-plus achieved the highest accuracy with a mean point cloud distance of 1.7 mm. By combining robust initialization, comparative fine registration, and voxel-level fusion, the proposed approach provides a practical solution for myocardial ischemia localization and functional evaluation of coronary lesions, while remaining independent of any specific fine registration algorithm.

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arXiv2026

ImmerIris: A Large-Scale Dataset and Benchmark for Off-Axis and Unconstrained Iris Recognition in Immersive Applications

Recently, iris recognition is regaining prominence in immersive applications such as extended reality as a means of seamless user identification. This application scenario introduces unique challenges compared to traditional iris recognition under controlled setups, as the ocular images are primarily captured off-axis and less constrained, causing perspective distortion, intra-subject variation, and quality degradation in iris textures. Datasets capturing these challenges remain limited. This paper fills this gap by presenting a large-scale iris dataset collected via head-mounted displays, termed ImmerIris. It contains 499,791 ocular images from 546 subjects, and is, to our knowledge, the largest public iris dataset to date and among the first dedicated to immersive applications. It is accompanied by a comprehensive set of evaluation protocols that benchmark recognition systems under various challenging conditions. This paper also draws attention to a shared obstacle of current recognition methods, the reliance on a pre-processing, normalization stage, which is fallible in off-axis and unconstrained setups. To this end, this paper further proposes a normalization-free paradigm that directly learns from minimally adjusted ocular images. Despite its simplicity, it outperforms normalization-based prior arts, indicating a promising direction for robust iris recognition.

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arXiv2025

UG-FedDA: Uncertainty-Guided Federated Domain Adaptation for Multi-Center Alzheimer's Disease Detection

Alzheimer's disease (AD) is an irreversible neurodegenerative disorder, and early diagnosis is critical for timely intervention. However, most existing classification frameworks face challenges in multicenter studies, as they often neglect inter-site heterogeneity and lack mechanisms to quantify uncertainty, which limits their robustness and clinical applicability. To address these issues, we proposed Uncertainty-Guided Federated Domain Adaptation (UG-FedDA), a novel multicenter AD classification framework that integrates uncertainty quantification (UQ) with federated domain adaptation to handle cross-site structure magnetic resonance imaging (MRI) heterogeneity under privacy constraints. Our approach extracts multi-template region-of-interest (RoI) features using a self-attention transformer, capturing both regional representations and their interactions. UQ is integrated to guide feature alignment, mitigating source-target distribution shifts by down-weighting uncertain samples. Experiments are conducted on three public datasets: the Alzheimer's Disease Neuroimaging Initiative (ADNI), the Australian Imaging, Biomarkers and Lifestyle study (AIBL), and the Open Access Series of Imaging Studies (OASIS). UG-FedDA achieved consistent cross-domain improvements in accuracy, sensitivity, and area under the ROC curve across three classification tasks: AD vs. normal controls (NC), mild cognitive impairment (MCI) vs. AD, and NC vs. MCI. For NC vs. AD, UG-FedDA achieves accuracies of 90.54%, 89.04%, and 77.78% on ADNI, AIBL and OASIS datasets, respectively. For MCI vs. AD, accuracies are 80.20% (ADNI), 71.91% (AIBL), and 79.73% (OASIS). For NC vs. MCI, results are 76.87% (ADNI), 73.91% (AIBL), and 83.73% (OASIS). These results demonstrate that the proposed framework not only adapts efficiently across multiple sites but also preserves strict privacy.

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arXiv2025

UPMAD-Net: A Brain Tumor Segmentation Network with Uncertainty Guidance and Adaptive Multimodal Feature Fusion

Background: Brain tumor segmentation has a significant impact on the diagnosis and treatment of brain tumors. Accurate brain tumor segmentation remains challenging due to their irregular shapes, vague boundaries, and high variability. Objective: We propose a brain tumor segmentation method that combines deep learning with prior knowledge derived from a region-growing algorithm. Methods: The proposed method utilizes a multi-scale feature fusion (MSFF) module and adaptive attention mechanisms (AAM) to extract multi-scale features and capture global contextual information. To enhance the model's robustness in low-confidence regions, the Monte Carlo Dropout (MC Dropout) strategy is employed for uncertainty estimation. Results: Extensive experiments demonstrate that the proposed method achieves superior performance on Brain Tumor Segmentation (BraTS) datasets, significantly outperforming various state-of-the-art methods. On the BraTS2021 dataset, the test Dice scores are 89.18% for Enhancing Tumor (ET) segmentation, 93.67% for Whole Tumor (WT) segmentation, and 91.23% for Tumor Core (TC) segmentation. On the BraTS2019 validation set, the validation Dice scores are 87.43%, 90.92%, and 90.40% for ET, WT, and TC segmentation, respectively. Ablation studies further confirmed the contribution of each module to segmentation accuracy, indicating that each component played a vital role in overall performance improvement. Conclusion: This study proposed a novel 3D brain tumor segmentation network based on the U-Net architecture. By incorporating the prior knowledge and employing the uncertainty estimation method, the robustness and performance were improved. The code for the proposed method is available at https://github.com/chenzhao2023/UPMAD_Net_BrainSeg.

cs.CV↗

arXiv2025

Myocardial Region-guided Feature Aggregation Net for Automatic Coronary artery Segmentation and Stenosis Assessment using Coronary Computed Tomography Angiography

Coronary artery disease (CAD) remains a leading cause of mortality worldwide, requiring accurate segmentation and stenosis detection using Coronary Computed Tomography angiography (CCTA). Existing methods struggle with challenges such as low contrast, morphological variability and small vessel segmentation. To address these limitations, we propose the Myocardial Region-guided Feature Aggregation Net, a novel U-shaped dual-encoder architecture that integrates anatomical prior knowledge to enhance robustness in coronary artery segmentation. Our framework incorporates three key innovations: (1) a Myocardial Region-guided Module that directs attention to coronary regions via myocardial contour expansion and multi-scale feature fusion, (2) a Residual Feature Extraction Encoding Module that combines parallel spatial channel attention with residual blocks to enhance local-global feature discrimination, and (3) a Multi-scale Feature Fusion Module for adaptive aggregation of hierarchical vascular features. Additionally, Monte Carlo dropout f quantifies prediction uncertainty, supporting clinical interpretability. For stenosis detection, a morphology-based centerline extraction algorithm separates the vascular tree into anatomical branches, enabling cross-sectional area quantification and stenosis grading. The superiority of MGFA-Net was demonstrated by achieving an Dice score of 85.04%, an accuracy of 84.24%, an HD95 of 6.1294 mm, and an improvement of 5.46% in true positive rate for stenosis detection compared to3D U-Net. The integrated segmentation-to-stenosis pipeline provides automated, clinically interpretable CAD assessment, bridging deep learning with anatomical prior knowledge for precision medicine. Our code is publicly available at http://github.com/chenzhao2023/MGFA_CCTA

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arXiv2025

Diagnosis of Pulmonary Hypertension by Integrating Multimodal Data with a Hybrid Graph Convolutional and Transformer Network

Early and accurate diagnosis of pulmonary hypertension (PH) is essential for optimal patient management. Differentiating between pre-capillary and post-capillary PH is critical for guiding treatment decisions. This study develops and validates a deep learning-based diagnostic model for PH, designed to classify patients as non-PH, pre-capillary PH, or post-capillary PH. This retrospective study analyzed data from 204 patients (112 with pre-capillary PH, 32 with post-capillary PH, and 60 non-PH controls) at the First Affiliated Hospital of Nanjing Medical University. Diagnoses were confirmed through right heart catheterization. We selected 6 samples from each category for the test set (18 samples, 10%), with the remaining 186 samples used for the training set. This process was repeated 35 times for testing. This paper proposes a deep learning model that combines Graph convolutional networks (GCN), Convolutional neural networks (CNN), and Transformers. The model was developed to process multimodal data, including short-axis (SAX) sequences, four-chamber (4CH) sequences, and clinical parameters. Our model achieved a performance of Area under the receiver operating characteristic curve (AUC) = 0.81 +- 0.06(standard deviation) and Accuracy (ACC) = 0.73 +- 0.06 on the test set. The discriminative abilities were as follows: non-PH subjects (AUC = 0.74 +- 0.11), pre-capillary PH (AUC = 0.86 +- 0.06), and post-capillary PH (AUC = 0.83 +- 0.10). It has the potential to support clinical decision-making by effectively integrating multimodal data to assist physicians in making accurate and timely diagnoses.

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arXiv2024

MsMorph: An Unsupervised pyramid learning network for brain image registration

In the field of medical image analysis, image registration is a crucial technique. Despite the numerous registration models that have been proposed, existing methods still fall short in terms of accuracy and interpretability. In this paper, we present MsMorph, a deep learning-based image registration framework aimed at mimicking the manual process of registering image pairs to achieve more similar deformations, where the registered image pairs exhibit consistency or similarity in features. By extracting the feature differences between image pairs across various as-pects using gradients, the framework decodes semantic information at different scales and continuously compen-sates for the predicted deformation field, driving the optimization of parameters to significantly improve registration accuracy. The proposed method simulates the manual approach to registration, focusing on different regions of the image pairs and their neighborhoods to predict the deformation field between the two images, which provides strong interpretability. We compared several existing registration methods on two public brain MRI datasets, including LPBA and Mindboggle. The experimental results show that our method consistently outperforms state of the art in terms of metrics such as Dice score, Hausdorff distance, average symmetric surface distance, and non-Jacobian. The source code is publicly available at https://github.com/GaodengFan/MsMorph

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arXiv2024

3D Lymphoma Segmentation on PET/CT Images via Multi-Scale Information Fusion with Cross-Attention

Background: Accurate segmentation of diffuse large B-cell lymphoma (DLBCL) lesions is challenging due to their complex patterns in medical imaging. Objective: This study aims to develop a precise segmentation method for DLBCL using 18F-Fluorodeoxyglucose (FDG) positron emission tomography (PET) and computed tomography (CT) images. Methods: We propose a 3D dual-branch encoder segmentation method using shifted window transformers and a Multi-Scale Information Fusion (MSIF) module. To enhance feature integration, the MSIF module performs multi-scale feature fusion using cross-attention mechanisms with a shifted window framework. A gated neural network within the MSIF module dynamically balances the contributions from each modality. The model was optimized using the Dice Similarity Coefficient (DSC) loss function. Additionally, we computed the total metabolic tumor volume (TMTV) and performed statistical analyses. Results: The model was trained and validated on a dataset of 165 DLBCL patients using 5-fold cross-validation, achieving a DSC of 0.7512. Statistical analysis showed a significant improvement over comparative methods (p < 0.05). Additionally, a Pearson correlation coefficient of 0.91 and an R^2 of 0.89 were observed when comparing manual annotations to segmentation results for TMTV measurement. Conclusion: This study presents an effective automatic segmentation method for DLBCL that leverages the complementary strengths of PET and CT imaging. Our method has the potential to improve diagnostic interpretations and assist in treatment planning for DLBCL patients.

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arXiv2023

Incremental Value and Interpretability of Radiomics Features of Both Lung and Epicardial Adipose Tissue for Detecting the Severity of COVID-19 Infection

Epicardial adipose tissue (EAT) is known for its pro-inflammatory properties and association with Coronavirus Disease 2019 (COVID-19) severity. However, current EAT segmentation methods do not consider positional information. Additionally, the detection of COVID-19 severity lacks consideration for EAT radiomics features, which limits interpretability. This study investigates the use of radiomics features from EAT and lungs to detect the severity of COVID-19 infections. A retrospective analysis of 515 patients with COVID-19 (Cohort1: 415, Cohort2: 100) was conducted using a proposed three-stage deep learning approach for EAT extraction. Lung segmentation was achieved using a published method. A hybrid model for detecting the severity of COVID-19 was built in a derivation cohort, and its performance and uncertainty were evaluated in internal (125, Cohort1) and external (100, Cohort2) validation cohorts. For EAT extraction, the Dice similarity coefficients (DSC) of the two centers were 0.972 (+-0.011) and 0.968 (+-0.005), respectively. For severity detection, the hybrid model with radiomics features of both lungs and EAT showed improvements in AUC, net reclassification improvement (NRI), and integrated discrimination improvement (IDI) compared to the model with only lung radiomics features. The hybrid model exhibited an increase of 0.1 (p<0.001), 19.3%, and 18.0% respectively, in the internal validation cohort and an increase of 0.09 (p<0.001), 18.0%, and 18.0%, respectively, in the external validation cohort while outperforming existing detection methods. Uncertainty quantification and radiomics features analysis confirmed the interpretability of case prediction after inclusion of EAT features.

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arXiv2023

A Robust Deep Learning Method with Uncertainty Estimation for the Pathological Classification of Renal Cell Carcinoma based on CT Images

Objectives To develop and validate a deep learning-based diagnostic model incorporating uncertainty estimation so as to facilitate radiologists in the preoperative differentiation of the pathological subtypes of renal cell carcinoma (RCC) based on CT images. Methods Data from 668 consecutive patients, pathologically proven RCC, were retrospectively collected from Center 1. By using five-fold cross-validation, a deep learning model incorporating uncertainty estimation was developed to classify RCC subtypes into clear cell RCC (ccRCC), papillary RCC (pRCC), and chromophobe RCC (chRCC). An external validation set of 78 patients from Center 2 further evaluated the model's performance. Results In the five-fold cross-validation, the model's area under the receiver operating characteristic curve (AUC) for the classification of ccRCC, pRCC, and chRCC was 0.868 (95% CI: 0.826-0.923), 0.846 (95% CI: 0.812-0.886), and 0.839 (95% CI: 0.802-0.88), respectively. In the external validation set, the AUCs were 0.856 (95% CI: 0.838-0.882), 0.787 (95% CI: 0.757-0.818), and 0.793 (95% CI: 0.758-0.831) for ccRCC, pRCC, and chRCC, respectively. Conclusions The developed deep learning model demonstrated robust performance in predicting the pathological subtypes of RCC, while the incorporated uncertainty emphasized the importance of understanding model confidence, which is crucial for assisting clinical decision-making for patients with renal tumors. Clinical relevance statement Our deep learning approach, integrated with uncertainty estimation, offers clinicians a dual advantage: accurate RCC subtype predictions complemented by diagnostic confidence references, promoting informed decision-making for patients with RCC.

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arXiv2023

MLA-BIN: Model-level Attention and Batch-instance Style Normalization for Domain Generalization of Federated Learning on Medical Image Segmentation

The privacy protection mechanism of federated learning (FL) offers an effective solution for cross-center medical collaboration and data sharing. In multi-site medical image segmentation, each medical site serves as a client of FL, and its data naturally forms a domain. FL supplies the possibility to improve the performance of seen domains model. However, there is a problem of domain generalization (DG) in the actual de-ployment, that is, the performance of the model trained by FL in unseen domains will decrease. Hence, MLA-BIN is proposed to solve the DG of FL in this study. Specifically, the model-level attention module (MLA) and batch-instance style normalization (BIN) block were designed. The MLA represents the unseen domain as a linear combination of seen domain models. The atten-tion mechanism is introduced for the weighting coefficient to obtain the optimal coefficient ac-cording to the similarity of inter-domain data features. MLA enables the global model to gen-eralize to unseen domain. In the BIN block, batch normalization (BN) and instance normalization (IN) are combined to perform the shallow layers of the segmentation network for style normali-zation, solving the influence of inter-domain image style differences on DG. The extensive experimental results of two medical image seg-mentation tasks demonstrate that the proposed MLA-BIN outperforms state-of-the-art methods.

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arXiv2022

Automatic reorientation by deep learning to generate short axis SPECT myocardial perfusion images

Single photon emission computed tomography (SPECT) myocardial perfusion images (MPI) can be displayed both in traditional short-axis (SA) cardiac planes and polar maps for interpretation and quantification. It is essential to reorient the reconstructed transaxial SPECT MPI into standard SA slices. This study is aimed to develop a deep-learning-based approach for automatic reorientation of MPI. Methods: A total of 254 patients were enrolled, including 228 stress SPECT MPIs and 248 rest SPECT MPIs. Five-fold cross-validation with 180 stress and 201 rest MPIs was used for training and internal validation; the remaining images were used for testing. The rigid transformation parameters (translation and rotation) from manual reorientation were annotated by an experienced operator and used as the ground truth. A convolutional neural network (CNN) was designed to predict the transformation parameters. Then, the derived transform was applied to the grid generator and sampler in spatial transformer network (STN) to generate the reoriented image. A loss function containing mean absolute errors for translation and mean square errors for rotation was employed. A three-stage optimization strategy was adopted for model optimization: 1) optimize the translation parameters while fixing the rotation parameters; 2) optimize rotation parameters while fixing the translation parameters; 3) optimize both translation and rotation parameters together.

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arXiv2022

A new method incorporating deep learning with shape priors for left ventricular segmentation in myocardial perfusion SPECT images

Background: The assessment of left ventricular (LV) function by myocardial perfusion SPECT (MPS) relies on accurate myocardial segmentation. The purpose of this paper is to develop and validate a new method incorporating deep learning with shape priors to accurately extract the LV myocardium for automatic measurement of LV functional parameters. Methods: A segmentation architecture that integrates a three-dimensional (3D) V-Net with a shape deformation module was developed. Using the shape priors generated by a dynamic programming (DP) algorithm, the model output was then constrained and guided during the model training for quick convergence and improved performance. A stratified 5-fold cross-validation was used to train and validate our models. Results: Results of our proposed method agree well with those from the ground truth. Our proposed model achieved a Dice similarity coefficient (DSC) of 0.9573(0.0244), 0.9821(0.0137), and 0.9903(0.0041), a Hausdorff distances (HD) of 6.7529(2.7334) mm, 7.2507(3.1952) mm, and 7.6121(3.0134) mm in extracting the endocardium, myocardium, and epicardium, respectively. Conclusion: Our proposed method achieved a high accuracy in extracting LV myocardial contours and assessing LV function.

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arXiv2021

A Deep Learning-based Method to Extract Lumen and Media-Adventitia in Intravascular Ultrasound Images

Intravascular ultrasound (IVUS) imaging allows direct visualization of the coronary vessel wall and is suitable for the assessment of atherosclerosis and the degree of stenosis. Accurate segmentation and measurements of lumen and median-adventitia (MA) from IVUS are essential for such a successful clinical evaluation. However, current segmentation relies on manual operations, which is time-consuming and user-dependent. In this paper, we aim to develop a deep learning-based method using an encoder-decoder deep architecture to automatically extract both lumen and MA border. Our method named IVUS-U-Net++ is an extension of the well-known U-Net++ model. More specifically, a feature pyramid network was added to the U-Net++ model, enabling the utilization of feature maps at different scales. As a result, the accuracy of the probability map and subsequent segmentation have been improved We collected 1746 IVUS images from 18 patients in this study. The whole dataset was split into a training dataset (1572 images) for the 10-fold cross-validation and a test dataset (174 images) for evaluating the performance of models. Our IVUS-U-Net++ segmentation model achieved a Jaccard measure (JM) of 0.9412, a Hausdorff distance (HD) of 0.0639 mm for the lumen border, and a JM of 0.9509, an HD of 0.0867 mm for the MA border, respectively. Moreover, the Pearson correlation and Bland-Altman analyses were performed to evaluate the correlations of 12 clinical parameters measured from our segmentation results and the ground truth, and automatic measurements agreed well with those from the ground truth (all Ps<0.01). In conclusion, our preliminary results demonstrate that the proposed IVUS-U-Net++ model has great promise for clinical use.

cs.CV↗

arXiv2020

Semantic Segmentation Using Deep Learning to Extract Total Extraocular Muscles and Optic Nerve from Orbital Computed Tomography Images

Objectives: Precise segmentation of total extraocular muscles (EOM) and optic nerve (ON) is essential to assess anatomical development and progression of thyroid-associated ophthalmopathy (TAO). We aim to develop a semantic segmentation method based on deep learning to extract the total EOM and ON from orbital CT images in patients with suspected TAO. Materials and Methods: A total of 7,879 images obtained from 97 subjects who underwent orbit CT scans due to suspected TAO were enrolled in this study. Eighty-eight patients were randomly selected into the training/validation dataset, and the rest were put into the test dataset. Contours of the total EOM and ON in all the patients were manually delineated by experienced radiologists as the ground truth. A three-dimensional (3D) end-to-end fully convolutional neural network called semantic V-net (SV-net) was developed for our segmentation task. Intersection over Union (IoU) was measured to evaluate the accuracy of the segmentation results, and Pearson correlation analysis was used to evaluate the volumes measured from our segmentation results against those from the ground truth. Results: Our model in the test dataset achieved an overall IoU of 0.8207; the IoU was 0.7599 for the superior rectus muscle, 0.8183 for the lateral rectus muscle, 0.8481 for the medial rectus muscle, 0.8436 for the inferior rectus muscle and 0.8337 for the optic nerve. The volumes measured from our segmentation results agreed well with those from the ground truth (all R>0.98, P<0.0001). Conclusion: The qualitative and quantitative evaluations demonstrate excellent performance of our method in automatically extracting the total EOM and ON and measuring their volumes in orbital CT images. There is a great promise for clinical application to assess these anatomical structures for the diagnosis and prognosis of TAO.

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