arXiv subjects

Xiaoran Zhang

Publications and source records attributed to Xiaoran Zhang.

At least 37 records · Page 2Linked to original sources

arXiv2025

Snap-and-tune: combining deep learning and test-time optimization for high-fidelity cardiovascular volumetric meshing

High-quality volumetric meshing from medical images is a key bottleneck for physics-based simulations in personalized medicine. For volumetric meshing of complex medical structures, recent studies have often utilized deep learning (DL)-based template deformation approaches to enable fast test-time generation with high spatial accuracy. However, these approaches still exhibit limitations, such as limited flexibility at high-curvature areas and unrealistic inter-part distances. In this study, we introduce a simple yet effective snap-and-tune strategy that sequentially applies DL and test-time optimization, which combines fast initial shape fitting with more detailed sample-specific mesh corrections. Our method provides significant improvements in both spatial accuracy and mesh quality, while being fully automated and requiring no additional training labels. Finally, we demonstrate the versatility and usefulness of our newly generated meshes via solid mechanics simulations in two different software platforms. Our code is available at https://github.com/danpak94/Deep-Cardiac-Volumetric-Mesh.

eess.IV↗

arXiv2025

DLW-CI: A Dynamic Likelihood-Weighted Cooperative Infotaxis Approach for Multi-Source Search in Urban Environments Using Consumer Drone Networks

Consumer-grade drones equipped with low-cost sensors have emerged as a cornerstone of Autonomous Intelligent Systems (AISs) for environmental monitoring and hazardous substance detection in urban environments. However, existing research primarily addresses single-source search problems, overlooking the complexities of real-world urban scenarios where both the location and quantity of hazardous sources remain unknown. To address this issue, we propose the Dynamic Likelihood-Weighted Cooperative Infotaxis (DLW-CI) approach for consumer drone networks. Our approach enhances multi-drone collaboration in AISs by combining infotaxis (a cognitive search strategy) with optimized source term estimation and an innovative cooperative mechanism. Specifically, we introduce a novel source term estimation method that utilizes multiple parallel particle filters, with each filter dedicated to estimating the parameters of a potentially unknown source within the search scene. Furthermore, we develop a cooperative mechanism based on dynamic likelihood weights to prevent multiple drones from simultaneously estimating and searching for the same source, thus optimizing the energy efficiency and search coverage of the consumer AIS. Experimental results demonstrate that the DLW-CI approach significantly outperforms baseline methods regarding success rate, accuracy, and root mean square error, particularly in scenarios with relatively few sources, regardless of the presence of obstacles. Also, the effectiveness of the proposed approach is verified in a diffusion scenario generated by the computational fluid dynamics (CFD) model. Research findings indicate that our approach could improve source estimation accuracy and search efficiency by consumer drone-based AISs, making a valuable contribution to environmental safety monitoring applications within smart city infrastructure.

cs.IT↗

arXiv2025

Adapting Vision Foundation Models for Real-time Ultrasound Image Segmentation

We propose a novel approach that adapts hierarchical vision foundation models for real-time ultrasound image segmentation. Existing ultrasound segmentation methods often struggle with adaptability to new tasks, relying on costly manual annotations, while real-time approaches generally fail to match state-of-the-art performance. To overcome these limitations, we introduce an adaptive framework that leverages the vision foundation model Hiera to extract multi-scale features, interleaved with DINOv2 representations to enhance visual expressiveness. These enriched features are then decoded to produce precise and robust segmentation. We conduct extensive evaluations on six public datasets and one in-house dataset, covering both cardiac and thyroid ultrasound segmentation. Experiments show that our approach outperforms state-of-the-art methods across multiple datasets and excels with limited supervision, surpassing nnUNet by over 20\% on average in the 1\% and 10\% data settings. Our method achieves $\sim$77 FPS inference speed with TensorRT on a single GPU, enabling real-time clinical applications.

cs.CV↗

arXiv2025

Progressive Test Time Energy Adaptation for Medical Image Segmentation

We propose a model-agnostic, progressive test-time energy adaptation approach for medical image segmentation. Maintaining model performance across diverse medical datasets is challenging, as distribution shifts arise from inconsistent imaging protocols and patient variations. Unlike domain adaptation methods that require multiple passes through target data - impractical in clinical settings - our approach adapts pretrained models progressively as they process test data. Our method leverages a shape energy model trained on source data, which assigns an energy score at the patch level to segmentation maps: low energy represents in-distribution (accurate) shapes, while high energy signals out-of-distribution (erroneous) predictions. By minimizing this energy score at test time, we refine the segmentation model to align with the target distribution. To validate the effectiveness and adaptability, we evaluated our framework on eight public MRI (bSSFP, T1- and T2-weighted) and X-ray datasets spanning cardiac, spinal cord, and lung segmentation. We consistently outperform baselines both quantitatively and qualitatively.

cs.CV↗

arXiv2024

Optically Coherent Nitrogen-Vacancy Centers in HPHT Treated Diamonds

As a point defect with unique spin and optical properties, nitrogen-vacancy (NV) center in diamond has attracted much attention in the fields of quantum sensing, quantum simulation, and quantum networks. The optical properties of an NV center are crucial for all these quantum applications. However, NV centers fabricated by destructive methods such as electron irradiation or ion implantation usually exhibit poor optical coherence. In this work, we demonstrate a non-destructive method to fabricate optically coherent NV centers. High-purity single crystal diamonds are annealed under high pressure and high temperature (1700 $^{\circ}$C, 5.5 GPa), and individually resolvable NV centers with narrow PLE linewidth (<100 MHz) are produced. The high-pressure condition prevents the conversion of diamond to graphite during high-temperature annealing, significantly expanding the parameter space for creating high-performance artificial defects for quantum information science. These findings deepen our understanding of NV center formation in diamond and have implications for the optimization of color centers in solids, including silicon carbide and hexagonal boron nitride.

quant-ph↗

arXiv2024

A high-order implicit time integration method for linear and nonlinear dynamics with efficient computation of accelerations

An algorithm for a family of self-starting high-order implicit time integration schemes with controllable numerical dissipation is proposed for both linear and nonlinear transient problems. This work builds on the previous works of the authors on elastodynamics by presenting a new algorithm that eliminates the need for factorization of the mass matrix providing benefit for the solution of nonlinear problems. The improved algorithm directly obtains the acceleration at the same order of accuracy of the displacement and velocity using vector operations (without additional equation solutions). The nonlinearity is handled by numerical integration within a time step to achieve the desired order of accuracy. The new algorithm fully retains the desirable features of the previous works: 1. The order of accuracy is not affected by the presence of external forces and physical damping; 2. numerical dissipation in the algorithm is controlled by a user-specified parameter, leading to schemes ranging from perfectly nondissipative A-stable to L-stable; 3. The effective stiffness matrix is a linear combination of the mass, damping, and stiffness matrices as in the trapezoidal rule. The proposed algorithm is shown to replicate the numerical results demonstrated on linear problems in previous works. Additional numerical examples of linear and nonlinear vibration and wave propagation are presented herein. Notably, the proposed algorithms show the same convergence rates for nonlinear problems as linear problems, and very high accuracy. Second-order time integration methods commonly used in commercial software produce significantly polluted acceleration responses for a common class of wave propagation problems. The high-order time integration schemes presented here perform noticably better at suppressing spurious high-frequency oscillations and producing reliable and useable acceleration responses.

math.NA↗

arXiv2024

Heteroscedastic Uncertainty Estimation Framework for Unsupervised Registration

Deep learning methods for unsupervised registration often rely on objectives that assume a uniform noise level across the spatial domain (e.g. mean-squared error loss), but noise distributions are often heteroscedastic and input-dependent in real-world medical images. Thus, this assumption often leads to degradation in registration performance, mainly due to the undesired influence of noise-induced outliers. To mitigate this, we propose a framework for heteroscedastic image uncertainty estimation that can adaptively reduce the influence of regions with high uncertainty during unsupervised registration. The framework consists of a collaborative training strategy for the displacement and variance estimators, and a novel image fidelity weighting scheme utilizing signal-to-noise ratios. Our approach prevents the model from being driven away by spurious gradients caused by the simplified homoscedastic assumption, leading to more accurate displacement estimation. To illustrate its versatility and effectiveness, we tested our framework on two representative registration architectures across three medical image datasets. Our method consistently outperforms baselines and produces sensible uncertainty estimates. The code is publicly available at \url{https://voldemort108x.github.io/hetero_uncertainty/}.

eess.IV↗

arXiv2024

Adaptive Correspondence Scoring for Unsupervised Medical Image Registration

We propose an adaptive training scheme for unsupervised medical image registration. Existing methods rely on image reconstruction as the primary supervision signal. However, nuisance variables (e.g. noise and covisibility), violation of the Lambertian assumption in physical waves (e.g. ultrasound), and inconsistent image acquisition can all cause a loss of correspondence between medical images. As the unsupervised learning scheme relies on intensity constancy between images to establish correspondence for reconstruction, this introduces spurious error residuals that are not modeled by the typical training objective. To mitigate this, we propose an adaptive framework that re-weights the error residuals with a correspondence scoring map during training, preventing the parametric displacement estimator from drifting away due to noisy gradients, which leads to performance degradation. To illustrate the versatility and effectiveness of our method, we tested our framework on three representative registration architectures across three medical image datasets along with other baselines. Our adaptive framework consistently outperforms other methods both quantitatively and qualitatively. Paired t-tests show that our improvements are statistically significant. Code available at: \url{https://voldemort108x.github.io/AdaCS/}.

eess.IV↗

arXiv2023

The Critical Conjecture of Wave Equations with Logarithmic Nonlinearity on $\mathbb{H}^2$

In this paper, we verified the critical conjecture in our previous work \cite{wang2023wave} on two-dimensional hyperbolic space, that is, concerning nonlinear wave equations with logarithmic nonlinearity, which behaves like $\left(\ln {1}/{|u|}\right)^{1-p}|u|$ near $u=0$, on hyperbolic spaces, we demonstrate that the critical power is $p_c(2)=3$, by proving global existence for $p>3$, as well as blow up for $p\in (1,3)$.

math.AP↗

arXiv2023

Observation of non-superconducting phase changes in LuH$_{2\pm\text{x}}$N$_y$

The recent report of near-ambient superconductivity in nitrogen doped lutetium hydride has triggered a worldwide fanaticism and raised major questions about the latest claims. An intriguing phenomenon of color changes in pressurized samples from blue to pink to red was observed and correlated with the claimed superconducting transition, but the origin and underlying physics of these color changes have yet to be elucidated. Here we report synthesis and characterization of high-purity nitrogen doped lutetium hydride LuH$_{2\pm\text{x}}$N$_y$ with the same structure and composition as in the main phase of near-ambient superconductor1. We find a new purple phase of LuH$_{2\pm\text{x}}$N$_y$ between blue and pink phase, and reveal that the sample color changes likely stem from pressure-driven redistribution of nitrogen and its interaction with the LuH$_2$ framework. No superconducting transition is found in all blue, purple, pink and red phases at temperatures 1.8-300 K and pressures 0-30 GPa. Instead, we identify a notable temperature-induced resistance anomaly of structural and/or electronic origin in LuH$_{2\pm\text{x}}$N$_y$, which is most pronounced in the pink phase and may have been erroneously interpreted as a sign of superconducting transition. This work establishes key benchmarks for nitrogen doped lutetium hydrides, allowing an in-depth understanding of the novel pressure-induced phase changes.

cond-mat.supr-con↗

arXiv2023

Wave equations with logarithmic nonlinearity on hyperbolic spaces

In light of the exponential decay of solutions of linear wave equations on hyperbolic spaces $\mathbb{H}^n$, to illustrate the critical nature, we investigate nonlinear wave equations with logarithmic nonlinearity, which behaves like $\left(\ln {1}/{|u|}\right)^{1-p}|u|$ near $u=0$, on hyperbolic spaces. Concerning the global existence vs blow up with small data, we expect that the problem admits a critical power $p_c(n)>1$. When $n=3$, we prove that the critical power is $3$, by proving global existence for $p>3$, as well as generically blow up for $p\in (1,3)$.

math.AP↗

arXiv2022

High-order implicit time integration scheme with controllable numerical dissipation based on mixed-order Padé expansions

A single-step high-order implicit time integration scheme with controllable numerical dissipation at high frequencies is presented for the transient analysis of structural dynamic problems. The amount of numerical dissipation is controlled by a user-specified value of the spectral radius $ρ_\infty$ in the high frequency limit. Using this user-specified parameter as a weight factor, a Padé expansion of the matrix exponential solution of the equation of motion is constructed by mixing the diagonal and sub-diagonal expansions. An efficient timestepping scheme is designed where systems of equations, similar in complexity to the standard Newmark method, are solved recursively. It is shown that the proposed high-order scheme achieves high-frequency dissipation, while minimizing low-frequency dissipation and period errors. The effectiveness of the provided dissipation control and the efficiency of the scheme are demonstrated by numerical examples. A simple guideline for the choice of the controlling parameter and time step size is provided. The source codes written in MATLAB and FORTRAN are available for download at: https://github.com/ChongminSong/HighOrderTimeIntegration.

math.NA↗

arXiv2022

Learning correspondences of cardiac motion from images using biomechanics-informed modeling

Learning spatial-temporal correspondences in cardiac motion from images is important for understanding the underlying dynamics of cardiac anatomical structures. Many methods explicitly impose smoothness constraints such as the $\mathcal{L}_2$ norm on the displacement vector field (DVF), while usually ignoring biomechanical feasibility in the transformation. Other geometric constraints either regularize specific regions of interest such as imposing incompressibility on the myocardium or introduce additional steps such as training a separate network-based regularizer on physically simulated datasets. In this work, we propose an explicit biomechanics-informed prior as regularization on the predicted DVF in modeling a more generic biomechanically plausible transformation within all cardiac structures without introducing additional training complexity. We validate our methods on two publicly available datasets in the context of 2D MRI data and perform extensive experiments to illustrate the effectiveness and robustness of our proposed methods compared to other competing regularization schemes. Our proposed methods better preserve biomechanical properties by visual assessment and show advantages in segmentation performance using quantitative evaluation metrics. The code is publicly available at \url{https://github.com/Voldemort108X/bioinformed_reg}.

eess.IV↗

arXiv2022

Incremental Learning Meets Transfer Learning: Application to Multi-site Prostate MRI Segmentation

Many medical datasets have recently been created for medical image segmentation tasks, and it is natural to question whether we can use them to sequentially train a single model that (1) performs better on all these datasets, and (2) generalizes well and transfers better to the unknown target site domain. Prior works have achieved this goal by jointly training one model on multi-site datasets, which achieve competitive performance on average but such methods rely on the assumption about the availability of all training data, thus limiting its effectiveness in practical deployment. In this paper, we propose a novel multi-site segmentation framework called incremental-transfer learning (ITL), which learns a model from multi-site datasets in an end-to-end sequential fashion. Specifically, "incremental" refers to training sequentially constructed datasets, and "transfer" is achieved by leveraging useful information from the linear combination of embedding features on each dataset. In addition, we introduce our ITL framework, where we train the network including a site-agnostic encoder with pre-trained weights and at most two segmentation decoder heads. We also design a novel site-level incremental loss in order to generalize well on the target domain. Second, we show for the first time that leveraging our ITL training scheme is able to alleviate challenging catastrophic forgetting problems in incremental learning. We conduct experiments using five challenging benchmark datasets to validate the effectiveness of our incremental-transfer learning approach. Our approach makes minimal assumptions on computation resources and domain-specific expertise, and hence constitutes a strong starting point in multi-site medical image segmentation.

cs.CV↗

arXiv2022

An alternative proof of Tataru's dispersive estimates

The aim of this article is to give an alternative proof of Tataru's dispersive estimates for wave equations posed on the hyperbolic space. Based on the formula for the wave kernel on hyperbolic spaces, in \cite{MR2743652}, we give the proof from the perspective of Bessel potentials, by exploiting various facts about Gamma functions, modified Bessel functions, and Bessel potentials. This leads to our proof being more self-contained than that in Tataru \cite{MR1804518}.

math.AP↗

arXiv2022

MyoPS: A Benchmark of Myocardial Pathology Segmentation Combining Three-Sequence Cardiac Magnetic Resonance Images

Assessment of myocardial viability is essential in diagnosis and treatment management of patients suffering from myocardial infarction, and classification of pathology on myocardium is the key to this assessment. This work defines a new task of medical image analysis, i.e., to perform myocardial pathology segmentation (MyoPS) combining three-sequence cardiac magnetic resonance (CMR) images, which was first proposed in the MyoPS challenge, in conjunction with MICCAI 2020. The challenge provided 45 paired and pre-aligned CMR images, allowing algorithms to combine the complementary information from the three CMR sequences for pathology segmentation. In this article, we provide details of the challenge, survey the works from fifteen participants and interpret their methods according to five aspects, i.e., preprocessing, data augmentation, learning strategy, model architecture and post-processing. In addition, we analyze the results with respect to different factors, in order to examine the key obstacles and explore potential of solutions, as well as to provide a benchmark for future research. We conclude that while promising results have been reported, the research is still in the early stage, and more in-depth exploration is needed before a successful application to the clinics. Note that MyoPS data and evaluation tool continue to be publicly available upon registration via its homepage (www.sdspeople.fudan.edu.cn/zhuangxiahai/0/myops20/).

eess.IV↗

arXiv2021

Label-free virtual HER2 immunohistochemical staining of breast tissue using deep learning

The immunohistochemical (IHC) staining of the human epidermal growth factor receptor 2 (HER2) biomarker is widely practiced in breast tissue analysis, preclinical studies and diagnostic decisions, guiding cancer treatment and investigation of pathogenesis. HER2 staining demands laborious tissue treatment and chemical processing performed by a histotechnologist, which typically takes one day to prepare in a laboratory, increasing analysis time and associated costs. Here, we describe a deep learning-based virtual HER2 IHC staining method using a conditional generative adversarial network that is trained to rapidly transform autofluorescence microscopic images of unlabeled/label-free breast tissue sections into bright-field equivalent microscopic images, matching the standard HER2 IHC staining that is chemically performed on the same tissue sections. The efficacy of this virtual HER2 staining framework was demonstrated by quantitative analysis, in which three board-certified breast pathologists blindly graded the HER2 scores of virtually stained and immunohistochemically stained HER2 whole slide images (WSIs) to reveal that the HER2 scores determined by inspecting virtual IHC images are as accurate as their immunohistochemically stained counterparts. A second quantitative blinded study performed by the same diagnosticians further revealed that the virtually stained HER2 images exhibit a comparable staining quality in the level of nuclear detail, membrane clearness, and absence of staining artifacts with respect to their immunohistochemically stained counterparts. This virtual HER2 staining framework bypasses the costly, laborious, and time-consuming IHC staining procedures in laboratory, and can be extended to other types of biomarkers to accelerate the IHC tissue staining used in life sciences and biomedical workflow.

q-bio.QM↗

arXiv2020

Fully Automated Left Atrium Segmentation from Anatomical Cine Long-axis MRI Sequences using Deep Convolutional Neural Network with Unscented Kalman Filter

This study proposes a fully automated approach for the left atrial segmentation from routine cine long-axis cardiac magnetic resonance image sequences using deep convolutional neural networks and Bayesian filtering. The proposed approach consists of a classification network that automatically detects the type of long-axis sequence and three different convolutional neural network models followed by unscented Kalman filtering (UKF) that delineates the left atrium. Instead of training and predicting all long-axis sequence types together, the proposed approach first identifies the image sequence type as to 2, 3 and 4 chamber views, and then performs prediction based on neural nets trained for that particular sequence type. The datasets were acquired retrospectively and ground truth manual segmentation was provided by an expert radiologist. In addition to neural net based classification and segmentation, another neural net is trained and utilized to select image sequences for further processing using UKF to impose temporal consistency over cardiac cycle. A cyclic dynamic model with time-varying angular frequency is introduced in UKF to characterize the variations in cardiac motion during image scanning. The proposed approach was trained and evaluated separately with varying amount of training data with images acquired from 20, 40, 60 and 80 patients. Evaluations over 1515 images with equal number of images from each chamber group acquired from an additional 20 patients demonstrated that the proposed model outperformed state-of-the-art and yielded a mean Dice coefficient value of 94.1%, 93.7% and 90.1% for 2, 3 and 4-chamber sequences, respectively, when trained with datasets from 80 patients.

cs.CV↗