IEEE transactions on medical imaging最新文献_第6页

A Novel Spatio-Temporal Hub Identification in Brain Networks by Learning Dynamic Graph Embedding on Grassmannian Manifolds 通过学习格拉斯曼漫域上的动态图嵌入识别脑网络中的新型时空枢纽

IEEE transactions on medical imaging Pub Date : 2024-11-19 DOI: 10.1109/TMI.2024.3502545

Defu Yang;Hui Shen;Minghan Chen;Shuai Wang;Jiazhou Chen;Hongmin Cai;Xueli Chen;Guorong Wu;Wentao Zhu

{"title":"A Novel Spatio-Temporal Hub Identification in Brain Networks by Learning Dynamic Graph Embedding on Grassmannian Manifolds","authors":"Defu Yang;Hui Shen;Minghan Chen;Shuai Wang;Jiazhou Chen;Hongmin Cai;Xueli Chen;Guorong Wu;Wentao Zhu","doi":"10.1109/TMI.2024.3502545","DOIUrl":"10.1109/TMI.2024.3502545","url":null,"abstract":"Mounting evidence has revealed that functional brain networks are intrinsically dynamic, undergoing changes over time, even in the resting-state environment. Notably, recent studies have highlighted the existence of a small number of critical brain regions within each functional brain network that exhibit a flexible role in adapting the geometric pattern of brain connectivity over time, referred to as “temporal hub” regions. Therefore, the identification of these temporal hubs becomes pivotal for comprehending the mechanisms that underlie the dynamic evolution of brain connectivity. However, existing spatio-temporal hub identification methods rely on static network-based approaches, wherein each temporal hub region is independently inferred from individual time-segmented networks without considering their temporal consistency and consequently fails to align the evolution of hubs with the dynamic changes in brain states. To address this limitation, we propose a novel spatio-temporal hub identification method that fully leverages dynamic graph embedding to distinguish temporal hubs from peripheral nodes, in which dynamic graph embeddings are learned from both spatial and temporal dimensions. Specifically, to preserve the temporal consistency of evolving networks, we model the dynamic graph embedding as a physical model of time, where the network-to-network transition is mathematically expressed as a total variation of dynamic graph embedding with respect to time. Furthermore, a Grassmannian manifold optimization scheme is introduced to enhance graph embedding learning and capture the time-varying topology of brain networks. Experimental results on both synthetic and real fMRI data demonstrate superior temporal consistency in hub identification, surpassing conventional approaches.","PeriodicalId":94033,"journal":{"name":"IEEE transactions on medical imaging","volume":"44 3","pages":"1454-1467"},"PeriodicalIF":0.0,"publicationDate":"2024-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142673254","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Individual Graph Representation Learning for Pediatric Tooth Segmentation From Dental CBCT 从牙科 CBCT 进行小儿牙齿分割的个体图表示学习

IEEE transactions on medical imaging Pub Date : 2024-11-18 DOI: 10.1109/TMI.2024.3501365

Yusheng Liu;Shu Zhang;Xiyi Wu;Tao Yang;Yuchen Pei;Huayan Guo;Yuxian Jiang;Zhien Feng;Wen Xiao;Yu-Ping Wang;Lisheng Wang

{"title":"Individual Graph Representation Learning for Pediatric Tooth Segmentation From Dental CBCT","authors":"Yusheng Liu;Shu Zhang;Xiyi Wu;Tao Yang;Yuchen Pei;Huayan Guo;Yuxian Jiang;Zhien Feng;Wen Xiao;Yu-Ping Wang;Lisheng Wang","doi":"10.1109/TMI.2024.3501365","DOIUrl":"10.1109/TMI.2024.3501365","url":null,"abstract":"Pediatric teeth exhibit significant changes in type and spatial distribution across different age groups. This variation makes pediatric teeth segmentation from cone-beam computed tomography (CBCT) more challenging than that in adult teeth. Existing methods mainly focus on adult teeth segmentation, which however cannot be adapted to spatial distribution of pediatric teeth with individual changes (SDPTIC) in different children, resulting in limited accuracy for segmenting pediatric teeth. Therefore, we introduce a novel topology structure-guided graph convolutional network (TSG-GCN) to generate dynamic graph representation of SDPTIC for improved pediatric teeth segmentation. Specifically, this network combines a 3D GCN-based decoder for teeth segmentation and a 2D decoder for dynamic adjacency matrix learning (DAML) to capture SDPTIC information for individual graph representation. 3D teeth labels are transformed into specially-designed 2D projection labels, which is accomplished by first decoupling 3D teeth labels into class-wise volumes for different teeth via one-hot encoding and then projecting them to generate instance-wise 2D projections. With such 2D labels, DAML can be trained to adaptively describe SDPTIC from CBCT with dynamic adjacency matrix, which is then incorporated into GCN for improving segmentation. To ensure inter-task consistency at the adjacency matrix level between the two decoders, a novel loss function is designed. It can address the issue with inconsistent prediction and unstable TSG-GCN convergence due to two heterogeneous decoders. The TSG-GCN approach is finally validated with both public and multi-center datasets. Experimental results demonstrate its effectiveness for pediatric teeth segmentation, with significant improvement over seven state-of-the-art methods.","PeriodicalId":94033,"journal":{"name":"IEEE transactions on medical imaging","volume":"44 3","pages":"1432-1444"},"PeriodicalIF":0.0,"publicationDate":"2024-11-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142670505","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Pyramid Network With Quality-Aware Contrastive Loss for Retinal Image Quality Assessment 具有质量感知对比损失的金字塔网络用于视网膜图像质量评估

IEEE transactions on medical imaging Pub Date : 2024-11-18 DOI: 10.1109/TMI.2024.3501405

Guanghui Yue;Shaoping Zhang;Tianwei Zhou;Bin Jiang;Weide Liu;Tianfu Wang

{"title":"Pyramid Network With Quality-Aware Contrastive Loss for Retinal Image Quality Assessment","authors":"Guanghui Yue;Shaoping Zhang;Tianwei Zhou;Bin Jiang;Weide Liu;Tianfu Wang","doi":"10.1109/TMI.2024.3501405","DOIUrl":"10.1109/TMI.2024.3501405","url":null,"abstract":"Captured retinal images vary greatly in quality. Low-quality images increase the risk of misdiagnosis. This motivates to design effective retinal image quality assessment (RIQA) methods. Current deep learning-based methods usually classify the image into three levels of “Good”, “Usable”, and “Reject”, while ignoring the quantitative feedback for more detailed quality scores. This study proposes a unified RIQA framework, named QAC-Net, that can evaluate the quality of retinal images in both qualitative and quantitative manners. To improve the prediction accuracy, QAC-Net focuses on extracting discriminative features by using two strategies. On the one hand, it adopts a pyramid network structure that simultaneously inputs the scaled images to learn quality-aware features at different scales and purify the feature representation through a consistency loss. On the other hand, to improve feature representation, it utilizes a quality-aware contrastive (QAC) loss that considers quality relationships between different images. The QAC losses for qualitative and quantitative evaluation tasks have different forms in view of the task differences. Considering the shortage of datasets for the quantitative evaluation task, we construct a dataset with 2,300 authentically distorted retinal images, each of which is annotated with a numerical quality score through subjective experiments. Experimental results on public and our constructed datasets show that our QAC-Net is competent for the RIQA tasks with considerable performance.","PeriodicalId":94033,"journal":{"name":"IEEE transactions on medical imaging","volume":"44 3","pages":"1416-1431"},"PeriodicalIF":0.0,"publicationDate":"2024-11-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142670537","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Integration of Multi-Source Medical Data for Medical Diagnosis Question Answering 整合多源医疗数据用于医疗诊断问题解答

IEEE transactions on medical imaging Pub Date : 2024-11-13 DOI: 10.1109/TMI.2024.3496862

Qi Peng;Yi Cai;Jiankun Liu;Quan Zou;Xing Chen;Zheng Zhong;Zefeng Wang;Jiayuan Xie;Qing Li

{"title":"Integration of Multi-Source Medical Data for Medical Diagnosis Question Answering","authors":"Qi Peng;Yi Cai;Jiankun Liu;Quan Zou;Xing Chen;Zheng Zhong;Zefeng Wang;Jiayuan Xie;Qing Li","doi":"10.1109/TMI.2024.3496862","DOIUrl":"10.1109/TMI.2024.3496862","url":null,"abstract":"Medical question answering aims to enhance diagnostic support, improve patient education, and assist in clinical decision-making by automatically answering medical-related queries, which is an important foundation for realizing intelligent healthcare. Existing methods predominantly focus on extracting key information from a single data source, e.g., CT image, for answering. However, these methods are not enough to promote the development of intelligent healthcare, because they lack comprehensive medical diagnosis capabilities, which usually require the integration of multi-source data (e.g., laboratory tests, radiology images, pathology images, etc.) for processing. To address these limitations, our paper introduces the extended task of medical question answering, named medical diagnosis question answering MedDQA. MedDQA task aims to answer questions related to medical diagnosis based on multi-source data. Specifically, we introduce a corresponding dataset that incorporates multi-source diagnostic information from 250,917 patients in clinical data from hospital records, and utilize a large-scale model for constructing Q&A pairs. We propose a novel system based on large language models, named medical multi-agent (MMA) system, which includes a mechanism of multiple agents to handle different medical tasks. Each agent is specifically tailored to process various modalities of data and provide outputs in a uniform textual modality. Experimental results demonstrate that the MMA system’s architecture significantly enhances the handling of multi-source data, thereby improving medical diagnosis, establishing a robust baseline for future research.","PeriodicalId":94033,"journal":{"name":"IEEE transactions on medical imaging","volume":"44 3","pages":"1373-1385"},"PeriodicalIF":0.0,"publicationDate":"2024-11-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142610630","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

UC-NeRF: Uncertainty-Aware Conditional Neural Radiance Fields From Endoscopic Sparse Views UC-NeRF：来自内窥镜稀疏视图的不确定性感知条件神经辐射场

IEEE transactions on medical imaging Pub Date : 2024-11-12 DOI: 10.1109/TMI.2024.3496558

Jiaxin Guo;Jiangliu Wang;Ruofeng Wei;Di Kang;Qi Dou;Yun-Hui Liu

{"title":"UC-NeRF: Uncertainty-Aware Conditional Neural Radiance Fields From Endoscopic Sparse Views","authors":"Jiaxin Guo;Jiangliu Wang;Ruofeng Wei;Di Kang;Qi Dou;Yun-Hui Liu","doi":"10.1109/TMI.2024.3496558","DOIUrl":"10.1109/TMI.2024.3496558","url":null,"abstract":"Visualizing surgical scenes is crucial for revealing internal anatomical structures during minimally invasive procedures. Novel View Synthesis is a vital technique that offers geometry and appearance reconstruction, enhancing understanding, planning, and decision-making in surgical scenes. Despite the impressive achievements of Neural Radiance Field (NeRF), its direct application to surgical scenes produces unsatisfying results due to two challenges: endoscopic sparse views and significant photometric inconsistencies. In this paper, we propose uncertainty-aware conditional NeRF for novel view synthesis to tackle the severe shape-radiance ambiguity from sparse surgical views. The core of UC-NeRF is to incorporate the multi-view uncertainty estimation to condition the neural radiance field for modeling the severe photometric inconsistencies adaptively. Specifically, our UC-NeRF first builds a consistency learner in the form of multi-view stereo network, to establish the geometric correspondence from sparse views and generate uncertainty estimation and feature priors. In neural rendering, we design a base-adaptive NeRF network to exploit the uncertainty estimation for explicitly handling the photometric inconsistencies. Furthermore, an uncertainty-guided geometry distillation is employed to enhance geometry learning. Experiments on the SCARED and Hamlyn datasets demonstrate our superior performance in rendering appearance and geometry, consistently outperforming the current state-of-the-art approaches. Our code will be released at <uri>https://github.com/wrld/UC-NeRF</uri>.","PeriodicalId":94033,"journal":{"name":"IEEE transactions on medical imaging","volume":"44 3","pages":"1284-1296"},"PeriodicalIF":0.0,"publicationDate":"2024-11-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142601153","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Randomness-Restricted Diffusion Model for Ocular Surface Structure Segmentation 用于眼表结构分段的随机性受限扩散模型

IEEE transactions on medical imaging Pub Date : 2024-11-11 DOI: 10.1109/TMI.2024.3494762

Xinyu Guo;Han Wen;Huaying Hao;Yifan Zhao;Yanda Meng;Jiang Liu;Yalin Zheng;Wei Chen;Yitian Zhao

{"title":"Randomness-Restricted Diffusion Model for Ocular Surface Structure Segmentation","authors":"Xinyu Guo;Han Wen;Huaying Hao;Yifan Zhao;Yanda Meng;Jiang Liu;Yalin Zheng;Wei Chen;Yitian Zhao","doi":"10.1109/TMI.2024.3494762","DOIUrl":"10.1109/TMI.2024.3494762","url":null,"abstract":"Ocular surface diseases affect a significant portion of the population worldwide. Accurate segmentation and quantification of different ocular surface structures are crucial for the understanding of these diseases and clinical decision-making. However, the automated segmentation of the ocular surface structure is relatively unexplored and faces several challenges. Ocular surface structure boundaries are often inconspicuous and obscured by glare from reflections. In addition, the segmentation of different ocular structures always requires training of multiple individual models. Thus, developing a one-model-fits-all segmentation approach is desirable. In this paper, we introduce a randomness-restricted diffusion model for multiple ocular surface structure segmentation. First, a time-controlled fusion-attention module (TFM) is proposed to dynamically adjust the information flow within the diffusion model, based on the temporal relationships between the network’s input and time. TFM enables the network to effectively utilize image features to constrain the randomness of the generation process. We further propose a low-frequency consistency filter and a new loss to alleviate model uncertainty and error accumulation caused by the multi-step denoising process. Extensive experiments have shown that our approach can segment seven different ocular surface structures. Our method performs better than both dedicated ocular surface segmentation methods and general medical image segmentation methods. We further validated the proposed method over two clinical datasets, and the results demonstrated that it is beneficial to clinical applications, such as the meibomian gland dysfunction grading and aqueous deficient dry eye diagnosis.","PeriodicalId":94033,"journal":{"name":"IEEE transactions on medical imaging","volume":"44 3","pages":"1359-1372"},"PeriodicalIF":0.0,"publicationDate":"2024-11-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142599300","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

AASeg: Artery-Aware Global-to-Local Framework for Aneurysm Segmentation in Head and Neck CTA Images AASeg：用于头颈部 CTA 图像动脉瘤分割的动脉感知全局到局部框架

IEEE transactions on medical imaging Pub Date : 2024-11-11 DOI: 10.1109/TMI.2024.3496194

Linlin Yao;Dongdong Chen;Xiangyu Zhao;Manman Fei;Zhiyun Song;Zhong Xue;Yiqiang Zhan;Bin Song;Feng Shi;Qian Wang;Dinggang Shen

{"title":"AASeg: Artery-Aware Global-to-Local Framework for Aneurysm Segmentation in Head and Neck CTA Images","authors":"Linlin Yao;Dongdong Chen;Xiangyu Zhao;Manman Fei;Zhiyun Song;Zhong Xue;Yiqiang Zhan;Bin Song;Feng Shi;Qian Wang;Dinggang Shen","doi":"10.1109/TMI.2024.3496194","DOIUrl":"10.1109/TMI.2024.3496194","url":null,"abstract":"Aneurysm segmentation in computed tomography angiography (CTA) images is essential for medical intervention aimed at preventing subarachnoid hemorrhages. However, most existing studies tend to overlook the topological characteristics of arteries related to aneurysms, often resulting in suboptimal performance in aneurysm segmentation. To address this challenge, we propose an artery-aware global-to-local framework for aneurysm segmentation (AASeg) using CTA images of head and neck. This framework consists of two key components: 1) a centerline graph network (CG-Net) for aneurysm global localization, and 2) a point cloud network (PC-Net) for local aneurysm segmentation. The centerline graph is generated by extracting artery centerline structures from vessel masks obtained through a pre-trained model for head and neck vessel segmentation. This representation serves as a high-level representation of the artery structure, allowing for analysis of aneurysms along the entire arteries. It facilitates aneurysm localization via aneurysm-segment graph classification along the arteries. Then, local region of aneurysm segment can be sampled from the vessel mask according to the aneurysm-segment graph. Subsequently, aneurysm segmentation is performed on the point cloud constructed from the aneurysm segment through the PC-Net. Extensive experiments show that the proposed framework achieves state-of-the-art performance in aneurysm localization on a main dataset and an external testing dataset, with Recall of 84.1% and 80.7%, false positives per case of 1.72 and 1.69, and segmentation DSC of 66.1% and 60.2%, respectively.","PeriodicalId":94033,"journal":{"name":"IEEE transactions on medical imaging","volume":"44 3","pages":"1273-1283"},"PeriodicalIF":0.0,"publicationDate":"2024-11-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142599302","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Estimation of Stiffness Maps in Deforming Cells Through Optical Flow With Bounded Curvature 通过有界曲率的光流估算变形细胞中的刚度图

IEEE transactions on medical imaging Pub Date : 2024-11-08 DOI: 10.1109/TMI.2024.3494050

Yekta Kesenci;Aleix Boquet-Pujadas;Michael Unser;Jean-Christophe Olivo-Marin

{"title":"Estimation of Stiffness Maps in Deforming Cells Through Optical Flow With Bounded Curvature","authors":"Yekta Kesenci;Aleix Boquet-Pujadas;Michael Unser;Jean-Christophe Olivo-Marin","doi":"10.1109/TMI.2024.3494050","DOIUrl":"10.1109/TMI.2024.3494050","url":null,"abstract":"The stiffness of cells and of their nuclei is a biomarker of several pathological conditions. Current measurement methods rely on invasive physical probes that yield one or two stiffness values for the whole cell. However, the internal distribution of cells is heterogeneous. We propose a framework to estimate maps of intracellular and intranuclear stiffness inside deforming cells from fluorescent image sequences. Our scheme requires the resolution of two inverse problems. First, we use a novel optical-flow method that penalizes the nuclear norm of the Hessian to favor deformations that are continuous and piecewise linear, which we show to be compatible with elastic models. We then invert these deformations for the relative intracellular stiffness using a novel system of elliptic PDEs. Our method operates in quasi-static conditions and can still provide relative maps even in the absence of knowledge about the boundary conditions. We compare the accuracy of both methods to the state of the art on simulated data. The application of our method to real data of different cell strains allows us to distinguish different regions inside their nuclei.","PeriodicalId":94033,"journal":{"name":"IEEE transactions on medical imaging","volume":"44 3","pages":"1400-1415"},"PeriodicalIF":0.0,"publicationDate":"2024-11-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142596943","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A Learnable Prior Improves Inverse Tumor Growth Modeling 可学习的先验物改进了肿瘤逆生长建模

IEEE transactions on medical imaging Pub Date : 2024-11-08 DOI: 10.1109/TMI.2024.3494022

Jonas Weidner;Ivan Ezhov;Michal Balcerak;Marie-Christin Metz;Sergey Litvinov;Sebastian Kaltenbach;Leonhard Feiner;Laurin Lux;Florian Kofler;Jana Lipkova;Jonas Latz;Daniel Rueckert;Bjoern Menze;Benedikt Wiestler

{"title":"A Learnable Prior Improves Inverse Tumor Growth Modeling","authors":"Jonas Weidner;Ivan Ezhov;Michal Balcerak;Marie-Christin Metz;Sergey Litvinov;Sebastian Kaltenbach;Leonhard Feiner;Laurin Lux;Florian Kofler;Jana Lipkova;Jonas Latz;Daniel Rueckert;Bjoern Menze;Benedikt Wiestler","doi":"10.1109/TMI.2024.3494022","DOIUrl":"10.1109/TMI.2024.3494022","url":null,"abstract":"Biophysical modeling, particularly involving partial differential equations (PDEs), offers significant potential for tailoring disease treatment protocols to individual patients. However, the inverse problem-solving aspect of these models presents a substantial challenge, either due to the high computational requirements of model-based approaches or the limited robustness of deep learning (DL) methods. We propose a novel framework that leverages the unique strengths of both approaches in a synergistic manner. Our method incorporates a DL ensemble for initial parameter estimation, facilitating efficient downstream evolutionary sampling initialized with this DL-based prior. We showcase the effectiveness of integrating a rapid deep-learning algorithm with a high-precision evolution strategy in estimating brain tumor cell concentrations from magnetic resonance images. The DL-Prior plays a pivotal role, significantly constraining the effective sampling-parameter space. This reduction results in a fivefold convergence acceleration and a Dice-score of 95%.","PeriodicalId":94033,"journal":{"name":"IEEE transactions on medical imaging","volume":"44 3","pages":"1297-1307"},"PeriodicalIF":0.0,"publicationDate":"2024-11-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142596937","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

BronchoTrack: Airway Lumen Tracking for Branch-Level Bronchoscopic Localization BronchoTrack：用于支气管镜定位的气道管腔追踪技术

IEEE transactions on medical imaging Pub Date : 2024-11-07 DOI: 10.1109/TMI.2024.3493170

Qingyao Tian;Huai Liao;Xinyan Huang;Bingyu Yang;Jinlin Wu;Jian Chen;Lujie Li;Hongbin Liu

{"title":"BronchoTrack: Airway Lumen Tracking for Branch-Level Bronchoscopic Localization","authors":"Qingyao Tian;Huai Liao;Xinyan Huang;Bingyu Yang;Jinlin Wu;Jian Chen;Lujie Li;Hongbin Liu","doi":"10.1109/TMI.2024.3493170","DOIUrl":"10.1109/TMI.2024.3493170","url":null,"abstract":"Localizing the bronchoscope in real time is essential for ensuring intervention quality. However, most existing vision-based methods struggle to balance between speed and generalization. To address these challenges, we present BronchoTrack, an innovative real-time framework for accurate branch-level localization, encompassing lumen detection, tracking, and airway association. To achieve real-time performance, we employ benchmark light weight detector for efficient lumen detection. We firstly introduce multi-object tracking to bronchoscopic localization, mitigating temporal confusion in lumen identification caused by rapid bronchoscope movement and complex airway structures. To ensure generalization across patient cases, we propose a training-free detection-airway association method based on a semantic airway graph that encodes the hierarchy of bronchial tree structures. Experiments on 11 patient datasets demonstrate BronchoTrack’s localization accuracy of 81.72%, while accessing up to the 6th generation of airways. Furthermore, we tested BronchoTrack in an in-vivo animal study using a porcine model, where it localized the bronchoscope into the 8th generation airway successfully. Experimental evaluation underscores BronchoTrack’s real-time performance in both satisfying accuracy and generalization, demonstrating its potential for clinical applications.","PeriodicalId":94033,"journal":{"name":"IEEE transactions on medical imaging","volume":"44 3","pages":"1321-1333"},"PeriodicalIF":0.0,"publicationDate":"2024-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142596939","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0