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Reflecting topology consistency and abnormality via learnable attentions for airway labeling

International Journal of Computer Assisted Radiology and Surgery 2025年 1-9页

作者： Li, Chenyu Zhang, Minghui Zhang, Chuyan Gu, Yun Institute of Medical Robotics Shanghai Jiao Tong University Shanghai China Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University Shanghai China Shanghai Key Laboratory of Flexible Medical Robotics Tongren Hospital Shanghai Jiao Tong University Shanghai China

Purpose: Accurate airway anatomical labeling is crucial for clinicians to identify and navigate complex bronchial structures during bronchoscopy. Automatic airway labeling is challenging due to significant anatomical variations. Previous methods are prone to generate inconsistent predictions, hindering preoperative planning and intraoperative navigation. This paper aims to enhance topological consistency and improve the detection of abnormal airway branches. Methods: We propose a transformer-based framework incorporating two modules: the soft subtree consistency (SSC) and the abnormal branch saliency (ABS). The SSC module constructs a soft subtree to capture clinically relevant topological relationships, allowing for flexible feature aggregation within and across subtrees. The ABS module facilitates interaction between node features and prototypes to distinguish abnormal branches, preventing the erroneous features aggregation between normal and abnormal nodes. Results: Evaluated on a challenging dataset characterized by severe airway deformities, our method achieves superior performance compared to state-of-the-art approaches. Specifically, it attains an 83.7% subsegmental accuracy, along with a 3.1% increase in segmental subtree consistency, a 45.2% increase in abnormal branch recall. Notably, the method demonstrates robust performance in cases with airway deformities, ensuring consistent and accurate labeling. Conclusion: The enhanced topological consistency and robust identification of abnormal branches provided by our method offer an accurate and robust solution for airway labeling, with potential to improve the precision and safety of bronchoscopy procedures. © CARS 2025.

关键词： Airway anatomical labeling Anomaly detection Structural prior Transformer

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Humanoid Robot 3-D Motion Simulation for Hardware Realization

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Journal of Donghua University(English Edition) 2007年第6期24卷 713-717,722页

作者：曹曦赵群飞马培荪 Research Institute of Robotics Shanghai Jiaotong University Institute of Image Processing & Pattern Recognition Shanghai Jiaotong University

In this paper,three dimensions kinematics and kinetics simulation are discussed for hardware realization of a physical biped walking-chair *** direct and inverse close-form kinematics solution of the biped walking-chair robot is *** gaits are realized with the kinematics solution,including walking straight on level floor,going up stair,squatting down and standing *** Moment Point(ZMP)equation is analyzed considering the movement of the *** simulated biped walking-chair robot is used for mechanical design,gaits development and validation before they are tested on real robot.

关键词： Humanoid robot walking chair robot gait design 3D simulation ZMP

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MambaMIM: Pre-training Mamba with state space token interpolation and its application to medical image segmentation

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medical image Analysis 2025年 103卷 103606页

作者： Tang, Fenghe Nian, Bingkun Li, Yingtai Jiang, Zihang Yang, Jie Liu, Wei Zhou, S. Kevin School of Biomedical Engineering Division of Life Sciences and Medicine University of Science and Technology of China Anhui Hefei230026 China Suzhou Institute for Advanced Research University of Science and Technology of China Jiangsu Suzhou215123 China Suzhou Institute for Advanced Research USTC Jiangsu Suzhou215123 China Department of Automation Shanghai Jiao Tong University Shanghai China Institute of Image Processing and Pattern Recognition Shanghai China Institute of Medical Robotics Shanghai Jiao Tong University Shanghai China Jiangsu Provincial Key Laboratory of Multimodal Digital Twin Technology Jiangsu Suzhou215123 China State Key Laboratory of Precision and Intelligent Chemistry University of Science and Technology of China Anhui Hefei230026 China

Recently, the state space model Mamba has demonstrated efficient long-sequence modeling capabilities, particularly for addressing long-sequence visual tasks in 3D medical imaging. However, existing generative self-supervised learning methods have not yet fully unleashed Mamba's potential for handling long-range dependencies because they overlook the inherent causal properties of state space sequences in masked modeling. To address this challenge, we propose a general-purpose pre-training framework called MambaMIM, a masked image modeling method based on a novel TOKen-Interpolation strategy (TOKI) for the selective structure state space sequence, which learns causal relationships of state space within the masked sequence. Further, MambaMIM introduces a bottom-up 3D hybrid masking strategy to maintain a masking consistency across different architectures and can be used on any single or hybrid Mamba architecture to enhance its multi-scale and long-range representation capability. We pre-train MambaMIM on a large-scale dataset of 6.8K CT scans and evaluate its performance across eight public medical segmentation benchmarks. Extensive downstream experiments reveal the feasibility and advancement of using Mamba for medical image pre-training. In particular, when we apply the MambaMIM to a customized architecture that hybridizes MedNeXt and Vision Mamba, we consistently obtain the state-of-the-art segmentation performance. The code is available at: https://***/FengheTan9/MambaMIM. © 2025

关键词： image segmentation

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Follicular Unit Registration Based on Binocular Stereo Vision for Hair Transplantation Surgery Robot 6

Follicular Unit Registration Based on Binocular Stereo Visio...

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6th International Conference on Data Science and Information Technology, DSIT 2023

作者： Wang, Weiduo Liu, Jinfeng Yang, Jie Shanghai Jiao Tong University Institute of Image Processing and Pattern Recognition Department of Automation China Shanghai Jiao Tong University Institute of Medical Robotics China

ISBN: (纸本)9798350304442

In this paper, we focus on the follicular unit registration problem for hair transplantation surgery robots based on binocular stereo vision system. The follicular units in both images are detected with YOLO V5 network. Because of the dull and similar pattern of the follicular units, previous regular methods which are pixel-based are reported with misalignment and poor accuracy. To solve this problem, we propose the context shape to describe the feature of the follicular unit. The distribution of units surrounding the target unit is estimated as the context shape. Then, Otsu's method is used to segment hairs and Hungarian algorithm is used to match the hairs in the matched follicular unit. Then, the hairs are projected to 3D coordinate system and reconstructed. The experiments on the generated hair dataset demonstrate that the proposed method achieves both high accuracy and speed. © 2023 IEEE.

关键词： Stereo vision

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Anatomy recognition in CT images of head and neck region via precision atlases

Anatomy recognition in CT images of head and neck region via...

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medical Imaging 2021: image processing

作者： Li, Jieyu Udupa, Jayaram K. Tong, Yubing Odhner, Dewey Torigian, Drew A. Institute of Image Processing and Pattern Recognition Department of Automation Shanghai Jiao Tong University China Medical Image Processing Group Department of Radiology University of Pennsylvania United States

ISBN: (纸本)9781510640214

Multi-atlas segmentation methods will benefit from atlases covering the complete spectrum of population patterns, while the difficulties in generating such large enough datasets and the computation burden required in the segmentation procedure reduce its practicality in clinical application. In this work, we start from a viewpoint that different parts of the target object can be recognized by different atlases and propose a precision atlas selection strategy. By comparing regional similarity between target image and atlases, precision atlases are ranked and selected by the frequency of regional best match, which have no need to be globally similar to the target subject at either image-level or object-level, largely increasing the implicit patterns contained in the atlas set. In the proposed anatomy recognition method, atlas building is first achieved by all-totemplate registration, where the minimum spanning tree (MST) strategy is used to select a registration template from a subset of radiologically near-normal images. Then, a two-stage recognition process is conducted: in rough recognition, sub-image level similarity is calculated between the test image and each image of the whole atlas set, and only the atlas with the highest similarity contributes to the recognition map regionally;in refined recognition, the atlases with the highest frequencies of best match are selected as the precision atlases and are utilized to further increase the accuracy of boundary matching. The proposed method is demonstrated on 298 computed tomography (CT) images and 9 organs in the Head and Neck (HN) body region. Experimental results illustrate that our method is effective for organs with different segmentation challenge and samples with different image quality, where remarkable improvement in boundary interpretation is made by refined recognition and most objects achieve a localization error within 2 voxels. © 2021 SPIE.

关键词： image enhancement

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A new fuzzy ART neural network based on dual competition and resonance technique

A new fuzzy ART neural network based on dual competition and...

引用

3rd International Symposium on Neural Networks, ISNN 2006 - Advances in Neural Networks

作者： Zhang, Lei Wang, Guoyou Wang, Wentao Lab. for Biometric and Medical Image Processing Institute for Pattern Recognition and Artificial Intelligence Huazhong University of Science and Technology Wuhan 430074 China

ISBN: (纸本)354034439X

A new Fuzzy ART neural network model based on dual competition and resonance technique is proposed. This new model takes the competition and resonance method of the input nodes into the output nodes, putting the input nodes and output nodes competition and resonance together, solves the over-segmentation of the FART algorithm due to the vigilance's increase in the application of the image segmentation. Experimental results have shown that compared with the original FART, this algorithm has a better segmentation and antinoise performance. © Springer-Verlag Berlin Heidelberg 2006.

关键词： Neural networks

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Deep Learning Based Neovascularization Detection in Color Fundus Photography image 23

Deep Learning Based Neovascularization Detection in Color Fu...

引用

9th International Conference on Computing and Artificial Intelligence, ICCAI 2023

作者： Guo, Tianjiao Yang, Jie Yu, Qi Institute of Medical Robotics Institute of Image Processing and Pattern Recognition Department of Automation School of Biomedical Engineering Shanghai Jiao Tong University Shanghai China Institute of Image Processing and Pattern Recognition Department of Automation Shanghai Jiao Tong University Shanghai China Department of Ophthalmology Shanghai General Hospital Shanghai Jiao Tong University School of Medicine Shanghai China National Clinical Research Center for Eye Diseases Shanghai China

ISBN: (纸本)9781450399029

Diabetic retinopathy (DR) has already been one of the leading causes of vision loss. A large number of researches about deep learning-based DR screening using color retinal photography images have been proposed in recent years. However, existing works mainly concentrated on non-proliferative diabetic retinopathy (NPDR) lesions. The exploration of proliferative diabetic retinopathy (PDR) lesion, which is more serious, is still insufficient. In this paper, we explored into one typical PDR lesion named neovascularization (NV). We collected and annotated retinal images from public and private datasets to construct and release a new dataset for NV detection. Three tasks including segmentation, detection, and grading were conducted on our dataset by using a set of state-of-the-art deep learning models. The results show that introducing NV detection can benefit DR grading. However, the segmentation and detection of NV are still challenging and have considerable room for improvement. © 2023 Copyright held by the owner/author(s).

关键词： Color

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ChromTR:chromosome detection in raw metaphase cell images via deformable transformers

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Frontiers of Medicine 2024年第6期18卷 1100-1114页

作者： Chao Xia Jiyue Wang Xin You Yaling Fan Bing Chen Saijuan Chen Jie Yang Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong UniversityShanghai200240China Shanghai Institute of Hematology State Key Laboratory of Medical GenomicsNational Research Center for Translational Medicine at ShanghaiRuijin Hospital Affiliated to Shanghai Jiao Tong University School of MedicineShanghai200025China

Chromosome karyotyping is a critical way to diagnose various hematological malignancies and genetic diseases,of which chromosome detection in raw metaphase cell images is the most critical and challenging *** this work,focusing on the joint optimization of chromosome localization and classification,we propose ChromTR to accurately detect and classify 24 classes of chromosomes in raw metaphase cell *** incorporates semantic feature learning and class distribution learning into a unified DETR-based detection ***,we first propose a Semantic Feature Learning Network(SFLN)for semantic feature extraction and chromosome foreground region segmentation with object-wise ***,we construct a Semantic-Aware Transformer(SAT)with two parallel encoders and a Semantic-Aware decoder to integrate global visual and semantic *** provide a prediction with a precise chromosome number and category distribution,a Category Distribution Reasoning Module(CDRM)is built for foreground-background objects and chromosome class distribution *** evaluate ChromTR on 1404 newly collected R-band metaphase images and the public G-band dataset *** proposed ChromTR outperforms all previous chromosome detection methods with an average precision of 92.56%in R-band chromosome detection,surpassing the baseline method by 3.02%.In a clinical test,ChromTR is also confident in tackling normal and numerically abnormal *** extended to the chromosome enumeration task,ChromTR also demonstrates state-of-the-art performances on R-band and G-band two metaphase image *** these superior performances to other methods,our proposed method has been applied to assist clinical karyotype diagnosis.

关键词： chromosome detection deformable transformer metaphase cell image distribution reasoning

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Iteratively Refine the Segmentation of Head and Neck Tumor in FDG-PET and CT images 1

引用

1st 3D Head and Neck Tumor Segmentation in PET/CT Challenge, HECKTOR 2020, which was held in conjunction with 23rd International Conference on medical image Computing and Computer-Assisted Intervention, MICCAI 2020

作者： Chen, Huai Chen, Haibin Wang, Lisheng Department of Automation Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University Shanghai200240 China Perception Vision Medical Technology Guangzhou China

ISBN: (数字)9783030671945

ISBN: (纸本)9783030671938

The automatic segmentation of head and neck (H&N) tumor from FDG-PET and CT images is urgently needed for radiomics. In this paper, we propose a framework to segment H&N tumor automatically by fusing information of PET and CT. In this framework, multiple 3D-Unets are trained one-by-one. The predictions and features of upstream models will be captured as additional information for the next one to further refine the segmentation. Experiments show that iterative refinements can improve the performance. We evaluated our framework on the dataset of HECKTOR2020 (The challenge of head and neck tumor segmentation) and won the 5th place with average DSC, precision and recall of 0.7241, 0.8479, 0.6701 respectively. © 2021, Springer Nature Switzerland AG.

关键词： Computerized tomography

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Adversarial attack type I: Cheat classifiers by significant changes

arXiv

引用

arXiv 2018年

作者： Tang, Sanli Huang, Xiaolin Chen, Mingjian Sun, Chengjin Yang, Jie Institute of Image Processing and Pattern Recognition Institute of Medical Robotics Shanghai Jiao Tong University Shanghai China

Despite the great success of deep neural networks, the adversarial attack can cheat some well-trained classifiers by small permutations. In this paper, we propose another type of adversarial attack that can cheat classifiers by significant changes. For example, we can significantly change a face but well-trained neural networks still recognize the adversarial and the original example as the same person. Statistically, the existing adversarial attack increases Type II error and the proposed one aims at Type I error, which are hence named as Type II and Type I adversarial attack, respectively. The two types of attack are equally important but are essentially different, which are intuitively explained and numerically evaluated. To implement the proposed attack, a supervised variation autoencoder is designed and then the classifier is attacked by updating the latent variables using gradient information. Besides, with pretrained generative models, Type I attack on latent spaces is investigated as well. Experimental results show that our method is practical and effective to generate Type I adversarial examples on large-scale image datasets. Most of these generated examples can pass detectors designed for defending Type II attack and the strengthening strategy is only efficient with a specific type attack, both implying that the underlying reasons for Type I and Type II attack are different. Copyright © 2018, The Authors. All rights reserved.

关键词： Deep neural networks

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