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Hongliang Ren

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11 papers

1 author row

JBHI Journal 2026 Journal Article

A Real-Time Scale-Robust Network for Glottis Segmentation in Nasal Transnasal Intubation

Yang Zhou
Chaoyong Zhang
Ruoyi Hao
Huilin Pan
Yang Zhang
Hongliang Ren

Nasotracheal intubation (NTI) is a critical clinical procedure for establishing and maintaining patient airway patency. Machine-assisted NTI has emerged as a pivotal approach for optimizing procedural efficiency and minimizing manual intervention. However, visual detection algorithms employed for NTI navigation encounter significant challenges, including complex anatomical environments and suboptimal illumination conditions surrounding the glottis. Additionally, the glottis presents considerable scale variability throughout the procedure, initially appearing as a small, difficult-to-capture structure before expanding to occupy nearly the entire field of view. Moreover, traditional visual detection methods often have high computational costs, making real-time, high-precision detection on portable devices challenging. To enhance NTI efficacy and address these challenges, this paper proposes a novel glottis segmentation framework optimized for vision-assisted NTI applications. First, we designed a lightweight, multi-receptive field feature extraction module to reduce intra-class differences, achieving robustness to scale variations of the glottis. This module was then stacked to form the backbone and neck of our network. Subsequently, we developed an advanced label assignment method and redefined the number of samples to further reduce intra-class differences and enhance accuracy in the complex NTI environment. Experiments on three distinct datasets demonstrate that our network surpasses state-of-the-art algorithms, achieving a segmentation mDice of 92. 9% with a compact model size of 19 MB and an inference speed exceeding 170 frames per second.

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AAAI Conference 2026 Conference Paper

Bridging Vision and Language for Robust Context-Aware Surgical Point Tracking: The VL-SurgPT Dataset and Benchmark

Rulin Zhou
Wenlong He
An Wang
Jianhang Zhang
Xuanhui Zeng
Xi Zhang
Chaowei Zhu
Haijun Hu

Accurate point tracking in surgical environments remains challenging due to complex visual conditions, including smoke occlusion, specular reflections, and tissue deformation. While existing surgical tracking datasets provide coordinate information, they lack the semantic context necessary to understand tracking failure mechanisms. We introduce VL-SurgPT, the first large-scale multimodal dataset that bridges visual tracking with textual descriptions of point status in surgical scenes. The dataset comprises 908 in vivo video clips, including 754 for tissue tracking (17,171 annotated points across five challenging scenarios) and 154 for instrument tracking (covering seven instrument types with detailed keypoint annotations). We establish comprehensive benchmarks using eight state-of-the-art tracking methods and propose TG-SurgPT, a text-guided tracking approach that leverages semantic descriptions to improve robustness in visually challenging conditions. Experimental results demonstrate that incorporating point status information significantly improves tracking accuracy and reliability, particularly in adverse visual scenarios where conventional vision-only methods struggle. By bridging visual and linguistic modalities, VL-SurgPT enables the development of context-aware tracking systems crucial for advancing computer-assisted surgery applications that can maintain performance even under challenging intraoperative conditions.

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AAAI Conference 2026 Conference Paper

Where It Moves, It Matters: Referring Surgical Instrument Segmentation via Motion

Meng Wei
Kun Yuan
Shi Li
Yue Zhou
Long Bai
Nassir Navab
Hongliang Ren
Hong Joo Lee

Enabling intuitive, language-driven interaction with surgical scenes is a critical step toward intelligent operating rooms and autonomous surgical robotic assistance. However, the task of referring segmentation, localizing surgical instruments based on natural language descriptions, remains underexplored in surgical videos, with existing approaches struggling to generalize due to reliance on static visual cues and predefined instrument names. In this work, we introduce SurgRef, a novel motion-guided framework that grounds free-form language expressions in instrument motion, capturing how tools move and interact across time, rather than what they look like. This allows models to understand and segment instruments even under occlusion, ambiguity, or unfamiliar terminology. To train and evaluate SurgRef, we present Ref-IMotion, a diverse, multi-institutional video dataset with dense spatiotemporal masks and rich motion-centric expressions. SurgRef achieves state-of-the-art accuracy and generalization across surgical procedures, setting a new benchmark for robust, language-driven surgical video segmentation.

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JBHI Journal 2025 Journal Article

Fine-Grained Classification Reveals Angiopathological Heterogeneity of Port Wine Stains Using OCT and OCTA Features

Xiaofeng Deng
Defu Chen
Bowen Liu
Xiwan Zhang
Haixia Qiu
Wu Yuan
Hongliang Ren

Accurate classification of port wine stains (PWS, vascular malformations present at birth), is critical for subsequent treatment planning. However, the current method of classifying PWS based on the external skin appearance rarely reflects the underlying angiopathological heterogeneity of PWS lesions, resulting in inconsistent outcomes with the common vascular-targeted photodynamic therapy (V-PDT) treatments. Conversely, optical coherence tomography angiography (OCTA) is an ideal tool for visualizing the vascular malformations of PWS. Previous studies have shown no significant correlation between OCTA quantitative metrics and the PWS subtypes determined by the current classification approach. In this study, we propose a novel fine-grained classification method for PWS that integrates OCT and OCTA imaging. Utilizing a machine learning-based approach, we subdivided PWS into five distinct subtypes by unearthing the heterogeneity of hypodermic histopathology and vessel structures. Six quantitative metrics, encompassing vascular morphology and depth information of PWS lesions, were designed and statistically analyzed to evaluate angiopathological differences among the subtypes. Our classification reveals significant distinctions across all metrics compared to conventional skin appearance-based subtypes, demonstrating its ability to accurately capture angiopathological heterogeneity. This research marks the first attempt to classify PWS based on angiopathology, potentially guiding more effective subtyping and treatment strategies for PWS.

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JBHI Journal 2024 Journal Article

FARN: Fetal Anatomy Reasoning Network for Detection With Global Context Semantic and Local Topology Relationship

Lei Zhao
Guanghua Tan
Qianghui Wu
Bin Pu
Hongliang Ren
Shengli Li
Kenli Li

Accurate recognition of fetal anatomical structure is a pivotal task in ultrasound (US) image analysis. Sonographers naturally apply anatomical knowledge and clinical expertise to recognizing key anatomical structures in complex US images. However, mainstream object detection approaches usually treat each structure recognition separately, overlooking anatomical correlations between different structures in fetal US planes. In this work, we propose a Fetal Anatomy Reasoning Network (FARN) that incorporates two kinds of relationship forms: a global context semantic block summarized with visual similarity and a local topology relationship block depicting structural pair constraints. Specifically, by designing the Adaptive Relation Graph Reasoning (ARGR) module, anatomical structures are treated as nodes, with two kinds of relationships between nodes modeled as edges. The flexibility of the model is enhanced by constructing the adaptive relationship graph in a data-driven way, enabling adaptation to various data samples without the need for predefined additional constraints. The feature representation is further refined by aggregating the outputs of the ARGR module. Comprehensive experimental results demonstrate that FARN achieves promising performance in detecting 37 anatomical structures across key US planes in tertiary obstetric screening. FARN effectively utilizes key relationships to improve detection performance, demonstrates robustness to small-scale, similar, and indistinct structures, and avoids some detection errors that deviate from anatomical norms. Overall, our study serves as a resource for developing efficient and concise approaches to model inter-anatomy relationships.

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JBHI Journal 2024 Journal Article

TransFSM: Fetal Anatomy Segmentation and Biometric Measurement in Ultrasound Images Using a Hybrid Transformer

Lei Zhao
Guanghua Tan
Bin Pu
Qianghui Wu
Hongliang Ren
Kenli Li

Biometric parameter measurements are powerful tools for evaluating a fetus's gestational age, growth pattern, and abnormalities in a 2D ultrasound. However, it is still challenging to measure fetal biometric parameters automatically due to the indiscriminate confusing factors, limited foreground-background contrast, variety of fetal anatomy shapes at different gestational ages, and blurry anatomical boundaries in ultrasound images. The performance of a standard CNN architecture is limited for these tasks due to the restricted receptive field. We propose a novel hybrid Transformer framework, TransFSM, to address fetal multi-anatomy segmentation and biometric measurement tasks. Unlike the vanilla Transformer based on a single-scale input, TransFSM has a deformable self-attention mechanism, so it can effectively process multi-scale information to segment fetal anatomy with irregular shapes and different sizes. We devised a boundary-aware decoder (BAD) to capture more intrinsic local details using boundary-wise prior knowledge, which compensates for the defects of the Transformer in extracting local features. In addition, a Transformer auxiliary segment head is designed to improve mask prediction by learning the semantic correspondence of the same pixel categories and feature discriminability among different pixel categories. Extensive experiments were conducted on clinical cases and benchmark datasets for anatomy segmentation and biometric measurement tasks. The experiment results indicate that our method achieves state-of-the-art performance in seven evaluation metrics compared with CNN-based, Transformer-based, and hybrid approaches. By knowledge distillation, the proposed TransFSM can create a more compact and efficient model with high deploying potential in resource-constrained scenarios. Our study serves as a unified framework for biometric estimation across multiple anatomical regions to monitor fetal growth in clinical practice.

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JBHI Journal 2023 Journal Article

Federated Semi-Supervised Learning for Medical Image Segmentation via Pseudo-Label Denoising

Liang Qiu
Jierong Cheng
Huxin Gao
Wei Xiong
Hongliang Ren

Distributed big data and digital healthcare technologies have great potential to promote medical services, but challenges arise when it comes to learning predictive model from diverse and complex e-health datasets. Federated Learning (FL), as a collaborative machine learning technique, aims to address the challenges by learning a joint predictive model across multi-site clients, especially for distributed medical institutions or hospitals. However, most existing FL methods assume that clients possess fully labeled data for training, which is often not the case in e-health datasets due to high labeling costs or expertise requirement. Therefore, this work proposes a novel and feasible approach to learn a Federated Semi-Supervised Learning (FSSL) model from distributed medical image domains, where a federated pseudo-labeling strategy for unlabeled clients is developed based on the embedded knowledge learned from labeled clients. This greatly mitigates the annotation deficiency at unlabeled clients and leads to a cost-effective and efficient medical image analysis tool. We demonstrated the effectiveness of our method by achieving significant improvements compared to the state-of-the-art in both fundus image and prostate MRI segmentation tasks, resulting in the highest Dice scores of 89. 23% and 91. 95% respectively even with only a few labeled clients participating in model training. This reveals the superiority of our method for practical deployment, ultimately facilitating the wider use of FL in healthcare and leading to better patient outcomes.

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AIIM Journal 2023 Journal Article

Two-stage contextual transformer-based convolutional neural network for airway extraction from CT images

Yanan Wu
Shuiqing Zhao
Shouliang Qi
Jie Feng
Haowen Pang
Runsheng Chang
Long Bai
Mengqi Li

Accurate airway segmentation from computed tomography (CT) images is critical for planning navigation bronchoscopy and realizing a quantitative assessment of airway-related chronic obstructive pulmonary disease (COPD). Existing methods face difficulty in airway segmentation, particularly for the small branches of the airway. These difficulties arise due to the constraints of limited labeling and failure to meet clinical use requirements in COPD. We propose a two-stage framework with a novel 3D contextual transformer for segmenting the overall airway and small airway branches using CT images. The method consists of two training stages sharing the same modified 3D U-Net network. The novel 3D contextual transformer block is integrated into both the encoder and decoder path of the network to effectively capture contextual and long-range information. In the first training stage, the proposed network segments the overall airway with the overall airway mask. To improve the performance of the segmentation result, we generate the intrapulmonary airway branch label, and train the network to focus on producing small airway branches in the second training stage. Extensive experiments were performed on in-house and multiple public datasets. Quantitative and qualitative analyses demonstrate that our proposed method extracts significantly more branches and longer lengths of the airway tree while accomplishing state-of-the-art airway segmentation performance. The code is available at https: //github. com/zhaozsq/airway_segmentation.

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JBHI Journal 2018 Journal Article

Three-Dimensional Intravascular Reconstruction Techniques Based on Intravascular Ultrasound: A Technical Review

Chaoyang Shi
Xiongbiao Luo
Jin Guo
Zoran Najdovski
Toshio Fukuda
Hongliang Ren

Intravascular ultrasound (IVUS) imaging provides two-dimensional (2-D) real-time luminal and transmural cross-sectional images of intravascular vessels with detailed pathological information. It has offered significant advantages in terms of diagnosis and guidance and has been increasingly introduced from coronary interventions into more generalized endovascular surgery. However, IVUS itself does not provide spatial pose information for its generated images, making it difficult to construct a 3-D intravascular visualization. To address this limitation, IVUS imaging-driven 3-D intravascular reconstruction techniques have been developed. These techniques enable accurate diagnosis and quantitative measurements of intravascular diseases to facilitate optimal treatment determination. Such reconstruction extends the IVUS imaging modality from pure diagnostic assistance to intraoperative navigation and guidance and supports both therapeutic options and interventional operations. This paper presents a comprehensive survey of technological advances and recent progress on IVUS imaging-based 3-D intravascular reconstruction and its state-of-the-art applications. Limitations of existing technologies and prospects of new technologies are also discussed.

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EAAI Journal 2018 Journal Article

Tracking control design of interval type-2 polynomial-fuzzy-model-based systems with time-varying delay

Bo Xiao
H.K. Lam
Xiaozhan Yang
Yan Yu
Hongliang Ren

In this paper, the tracking control design for the interval type-2 (IT2) polynomial-fuzzy-model-based (PFMB) control system subject to time-varying delay situation is investigated. The tracking control system is formed of the IT2 polynomial fuzzy model representing a nonlinear system with time-varying delay, the stable reference model and the IT2 polynomial fuzzy controller. The control objective is to design a proper IT2 polynomial fuzzy controller which is capable of driving the states of the polynomial fuzzy model to track those in the reference model and the tracking performance is evaluated and improved by the H ∞ performance index. Also, to handle the uncertainty in the membership functions, the property of IT2 fuzzy sets is utilized to enhance the fuzzy controller’s robustness against uncertainty. In addition, considering the effect of time-varying delay, the Lyapunov–Krasovskii functional based approach is adopted to facilitate the delay-dependent stability analysis. Stability conditions depending on the time-varying delay characteristic with the consideration of H ∞ performance are obtained in terms of sum-of-squares (SOS). Furthermore, the information of the IT2 membership functions is employed in the stability analysis to relax the stability conditions, both membership-function-independent (MFI) and membership-function-dependent (MFD) approaches are presented to develop the stability conditions. Simulation examples are presented to verify the effectiveness of the proposed tracking control approach.

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JBHI Journal 2014 Journal Article

Treatment Planning and Image Guidance for Radiofrequency Ablation of Large Tumors

Hongliang Ren
Enrique Campos-Nanez
Ziv Yaniv
Filip Banovac
Hernan Abeledo
Nobuhiko Hata
Kevin Cleary

This article addresses the two key challenges in computer-assisted percutaneous tumor ablation: planning multiple overlapping ablations for large tumors while avoiding critical structures, and executing the prescribed plan. Toward semiautomatic treatment planning for image-guided surgical interventions, we develop a systematic approach to the needle-based ablation placement task, ranging from preoperative planning algorithms to an intraoperative execution platform. The planning system incorporates clinical constraints on ablations and trajectories using a multiple objective optimization formulation, which consists of optimal path selection and ablation coverage optimization based on integer programming. The system implementation is presented and validated in both phantom and animal studies. The presented system can potentially be further extended for other ablation techniques such as cryotherapy.

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