Medical image analysis最新文献

{"title":"Recurrent inference machine for medical image registration","authors":"Yi Zhang ,&nbsp;Yidong Zhao ,&nbsp;Hui Xue ,&nbsp;Peter Kellman ,&nbsp;Stefan Klein ,&nbsp;Qian Tao","doi":"10.1016/j.media.2025.103748","DOIUrl":"10.1016/j.media.2025.103748","url":null,"abstract":"<div><div>Image registration is essential for medical image applications where alignment of voxels across multiple images is needed for qualitative or quantitative analysis. With recent advances in deep neural networks and parallel computing, deep learning-based medical image registration methods become competitive with their flexible modeling and fast inference capabilities. However, compared to traditional optimization-based registration methods, the speed advantage may come at the cost of registration performance at inference time. Besides, deep neural networks ideally demand large training datasets while optimization-based methods are training-free. To improve registration accuracy and data efficiency, we propose a novel image registration method, termed Recurrent Inference Image Registration (RIIR) network. RIIR is formulated as a meta-learning solver for the registration problem in an iterative manner. RIIR addresses the accuracy and data efficiency issues, by learning the update rule of optimization, with implicit regularization combined with explicit gradient input.</div><div>We extensively evaluated RIIR on brain MRI, lung CT, and quantitative cardiac MRI datasets, in terms of both registration accuracy and training data efficiency. Our experiments showed that RIIR outperformed a range of deep learning-based methods, even with only 5% of the training data, demonstrating high data efficiency. Key findings from our ablation studies highlighted the important added value of the hidden states introduced in the recurrent inference framework for meta-learning. Our proposed RIIR offers a highly data-efficient framework for deep learning-based medical image registration.</div></div>","PeriodicalId":18328,"journal":{"name":"Medical image analysis","volume":"107 ","pages":"Article 103748"},"PeriodicalIF":11.8,"publicationDate":"2025-08-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144804418","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Contrastive learning and prior knowledge-induced feature extraction network for prediction of high-risk recurrence areas in Gliomas","authors":"Boya Wu ,&nbsp;Jianyun Cao ,&nbsp;Wei Xiong ,&nbsp;Yanchun Lv ,&nbsp;Guohua Zhao ,&nbsp;Xiaoyue Ma ,&nbsp;Ying Zhang ,&nbsp;Jiawei Zhang ,&nbsp;Junguo Bu ,&nbsp;Tao Xie ,&nbsp;Qianjin Feng ,&nbsp;Meiyan Huang","doi":"10.1016/j.media.2025.103740","DOIUrl":"10.1016/j.media.2025.103740","url":null,"abstract":"<div><div>Gliomas can easily recur even after standard treatments, and their recurrence may be related to insufficient radiation doses received by high-risk recurrence areas (HRA). Therefore, HRA prediction can help clinical experts in formulating effective radiotherapy plans. However, research on HRA prediction using early postoperative conventional MRI images with total resection is lacking. This gap is due to multifold challenges, including visually minimal differences between HRA and non-HRA and small dataset size caused by missing follow-up data. A contrastive learning and prior knowledge-induced feature extraction network (CLPKnet) to explore HRA-related features and achieve HRA prediction was proposed in this paper. First, a contrastive and multisequence learning-based encoder was proposed to effectively extract diverse features across multiple MRI sequences around the operative cavity. Specifically, a contrastive learning method was employed to pretrain the encoder, which enabled it to capture subtle differences between HRA and non-HRA regions while mitigating the challenges posed by the limited dataset size. Second, clinical prior knowledge was incorporated into the CLPKnet to guide the model in learning the patterns of glioma growth and improve its discriminative capability for identifying HRA regions. Third, a dual-focus fusion module was utilized to explore important sequential features and spatial regions and effectively fused multisequence features to provide complementary information associated with glioma recurrence. Fourth, to balance clinical needs and task difficulty, we used a patch-based prediction method to predict the recurrent probability. The CLPKnet was validated on a multicenter dataset from four hospitals, and a remarkable performance was achieved. Moreover, the interpretability and robustness of our method were evaluated to illustrate its effectiveness and credibility. Therefore, the CLPKnet displays a great application potential for HRA prediction. The codes will be available at <span><span>https://github.com/Meiyan88/CLPKnet</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":18328,"journal":{"name":"Medical image analysis","volume":"107 ","pages":"Article 103740"},"PeriodicalIF":11.8,"publicationDate":"2025-08-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144780232","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Learning from certain regions of interest in medical images via probabilistic positive-unlabeled networks","authors":"Le Yi,&nbsp;Lei Zhang,&nbsp;Kefu Zhao,&nbsp;Xiuyuan Xu","doi":"10.1016/j.media.2025.103745","DOIUrl":"10.1016/j.media.2025.103745","url":null,"abstract":"<div><div>The laborious annotation process and inherent image ambiguity exacerbate difficulties of data acquisition for medical image segmentation, leading to suboptimal performance in practice. This paper proposes a workaround against these challenges aiming to learn unbiased models solely from certainties. Concretely, during the labeling stage, only regions of interest confidently discerned by annotators are required to be labeled, not only increasing label quantity but also improving label quality. This approach formulates the positive-unlabeled (PU) segmentation problem and motivates to capture uncertainty in ambiguous regions. We thus delve into data-generating assumptions in the PU segmentation context and propose Probabilistic PU Segmentation Networks (ProPU-Nets) to tackle problems abovementioned. This framework employs the expectation–maximization algorithm to gradually estimate true masks, and more importantly, by encoding plausible segmentation variants in a latent space, uncertainty estimation can be naturally embedded into the PU segmentation framework. Benefitting from the framework’s unbiasedness, a semi-supervised PU segmentation method is also proposed, which can further excavate performance gains from unlabeled data. We conduct extensive experiments on LIDC, RIGA, and LA datasets, and comprehensively compared with state-of-the-art methods in label-efficient medical image segmentation. The results justify the method’s effectiveness and practical prospect.</div></div>","PeriodicalId":18328,"journal":{"name":"Medical image analysis","volume":"107 ","pages":"Article 103745"},"PeriodicalIF":11.8,"publicationDate":"2025-08-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144804416","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Interpretable multi-scale deep learning to detect malignancy in cell blocks and cytological smears of pleural effusion and identify aggressive endometrial cancer","authors":"Ching-Wei Wang ,&nbsp;Hikam Muzakky ,&nbsp;Yu-Pang Chung ,&nbsp;Po-Jen Lai ,&nbsp;Tai-Kuang Chao","doi":"10.1016/j.media.2025.103742","DOIUrl":"10.1016/j.media.2025.103742","url":null,"abstract":"<div><div>The pleura is a serous membrane that surrounds the surface of the lungs. The visceral surface secretes fluid into the serous cavity, while the parietal surface ensures that the fluid is properly absorbed. However, when this balance is disrupted, it leads to the formation of pleural Effusion. The most common malignant pleural effusion (MPE) caused by lung cancer or breast cancer, and benign pleural effusions (BPE) caused by Mycobacterium tuberculosis infection, heart failure, or infections related to pneumonia. Today, with the rapid advancement of treatment protocols, accurately diagnosing MPE has become increasingly important. Although cytology smears and cell blocks examinations of pleural effusion are the clinical gold standards for diagnosing MPE, the diagnostic accuracy of these tools can be affected by certain limitations, such as low sensitivity, diagnostic variability across different regions and significant inter-observer variability, leading to a certain proportion of misdiagnoses. This study presents a deep learning (DL) framework, namely Interpretable Multi-scale Attention DL with Self-Supervised Learning Feature Encoder (IMA-SSL), to identifyMPE or BPE using 194 Cytological smears whole-slide images (WSIs) and 188 cell blocks WSIs. The use of DL on WSIs of pleural effusion allows for preliminary results to be obtained in a short time, giving patients the opportunity for earlier diagnosis and treatment. The experimental results show that the proposed IMA-SSL consistently obtained superior performance and outperformed five state-of-the-art (SOTA) methods in malignancy prediction on both cell block and cytological smear datasets and also in identification of aggressive endometrial cancer (EC) using a public TCGA dataset. Fisher’s exact test confirmed a highly significant correlation between the outputs of the proposed model and the slide status in the EC and pleural effusion datasets (<span><math><mi>p &lt; 0.001</mi></math></span>), substantiating the model’s predictive reliability. The proposed method has the potential for practical clinical application in the foreseeable future. It can directly detect the presence of malignant tumor cells from cost-effective cell blocks and pleural effusion cytology smears and facilitate personalized cancer treatment decisions.</div></div>","PeriodicalId":18328,"journal":{"name":"Medical image analysis","volume":"107 ","pages":"Article 103742"},"PeriodicalIF":11.8,"publicationDate":"2025-08-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144804415","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Self-consistent recursive diffusion bridge for medical image translation","authors":"Fuat Arslan ,&nbsp;Bilal Kabas ,&nbsp;Onat Dalmaz ,&nbsp;Muzaffer Ozbey ,&nbsp;Tolga Çukur","doi":"10.1016/j.media.2025.103747","DOIUrl":"10.1016/j.media.2025.103747","url":null,"abstract":"<div><div>Denoising diffusion models (DDM) have gained recent traction in medical image translation given their high training stability and image fidelity. DDMs learn a multi-step denoising transformation that progressively maps random Gaussian-noise images provided as input onto target-modality images as output, while receiving indirect guidance from source-modality images via a separate static channel. This denoising transformation diverges significantly from the task-relevant source-to-target modality transformation, as source images are governed by a non-noise distribution. In turn, DDMs can suffer from suboptimal source-modality guidance and performance losses in medical image translation. Here, we propose a novel self-consistent recursive diffusion bridge (SelfRDB) that leverages direct source-modality guidance within its diffusion process for improved performance in medical image translation. Unlike DDMs, SelfRDB devises a novel forward process with the start-point taken as the target image, and the end-point defined based on the source image. Intermediate image samples across the process are expressed via a normal distribution whose mean is taken as a convex combination of start-end points, and whose variance is controlled by additive noise. Unlike regular diffusion bridges that prescribe zero noise variance at start-end points and high noise variance at mid-point of the process, we propose a novel noise scheduling with monotonically increasing variance towards the end-point in order to facilitate information transfer between the two modalities and boost robustness against measurement noise. To further enhance sampling accuracy in each reverse step, we propose a novel sampling procedure where the network recursively generates a transient-estimate of the target image until convergence onto a self-consistent solution. Comprehensive experiments in multi-contrast MRI and MRI-CT translation indicate that SelfRDB achieves state-of-the-art results in terms of image quality.</div></div>","PeriodicalId":18328,"journal":{"name":"Medical image analysis","volume":"107 ","pages":"Article 103747"},"PeriodicalIF":11.8,"publicationDate":"2025-08-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144780370","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"MedCLIP-SAMv2: Towards universal text-driven medical image segmentation","authors":"Taha Koleilat ,&nbsp;Hojat Asgariandehkordi ,&nbsp;Hassan Rivaz ,&nbsp;Yiming Xiao","doi":"10.1016/j.media.2025.103749","DOIUrl":"10.1016/j.media.2025.103749","url":null,"abstract":"<div><div>Segmentation of anatomical structures and pathologies in medical images is essential for modern disease diagnosis, clinical research, and treatment planning. While significant advancements have been made in deep learning-based segmentation techniques, many of these methods still suffer from limitations in data efficiency, generalizability, and interactivity. As a result, developing robust segmentation methods that require fewer labeled datasets remains a critical challenge in medical image analysis. Recently, the introduction of foundation models like CLIP and Segment-Anything-Model (SAM), with robust cross-domain representations, has paved the way for interactive and universal image segmentation. However, further exploration of these models for data-efficient segmentation in medical imaging is an active field of research. In this paper, we introduce MedCLIP-SAMv2, a novel framework that integrates the CLIP and SAM models to perform segmentation on clinical scans using text prompts, in both zero-shot and weakly supervised settings. Our approach includes fine-tuning the BiomedCLIP model with a new Decoupled Hard Negative Noise Contrastive Estimation (DHN-NCE) loss, and leveraging the Multi-modal Information Bottleneck (M2IB) to create visual prompts for generating segmentation masks with SAM in the zero-shot setting. We also investigate using zero-shot segmentation labels in a weakly supervised paradigm to enhance segmentation quality further. Extensive validation across four diverse segmentation tasks and medical imaging modalities (breast tumor ultrasound, brain tumor MRI, lung X-ray, and lung CT) demonstrates the high accuracy of our proposed framework. Our code is available at <span><span>https://github.com/HealthX-Lab/MedCLIP-SAMv2</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":18328,"journal":{"name":"Medical image analysis","volume":"107 ","pages":"Article 103749"},"PeriodicalIF":11.8,"publicationDate":"2025-08-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144804417","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Brain Latent Progression: Individual-based spatiotemporal disease progression on 3D Brain MRIs via latent diffusion","authors":"Lemuel Puglisi ,&nbsp;Daniel C. Alexander ,&nbsp;Alzheimer’s Disease Neuroimaging Initiative ,&nbsp;Australian Imaging Biomarkers and Lifestyle flagship study of aging ,&nbsp;Daniele Ravì","doi":"10.1016/j.media.2025.103734","DOIUrl":"10.1016/j.media.2025.103734","url":null,"abstract":"<div><div>The growing availability of longitudinal Magnetic Resonance Imaging (MRI) datasets has facilitated Artificial Intelligence (AI)-driven modeling of disease progression, making it possible to predict future medical scans for individual patients. However, despite significant advancements in AI, current methods continue to face challenges including achieving patient-specific individualization, ensuring spatiotemporal consistency, efficiently utilizing longitudinal data, and managing the substantial memory demands of 3D scans. To address these challenges, we propose Brain Latent Progression (BrLP), a novel spatiotemporal model designed to predict individual-level disease progression in 3D brain MRIs. The key contributions in BrLP are fourfold: (i) it operates in a small latent space, mitigating the computational challenges posed by high-dimensional imaging data; (ii) it explicitly integrates subject metadata to enhance the individualization of predictions; (iii) it incorporates prior knowledge of disease dynamics through an auxiliary model, facilitating the integration of longitudinal data; and (iv) it introduces the Latent Average Stabilization (LAS) algorithm, which (a) enforces spatiotemporal consistency in the predicted progression at inference time and (b) allows us to derive a measure of the uncertainty for the prediction at the global and voxel level. We train and evaluate BrLP on 11,730 T1-weighted (T1w) brain MRIs from 2,805 subjects and validate its generalizability on an external test set comprising 2,257 MRIs from 962 subjects. Our experiments compare BrLP-generated MRI scans with real follow-up MRIs, demonstrating state-of-the-art accuracy compared to existing methods. The code is publicly available at: <span><span>https://github.com/LemuelPuglisi/BrLP</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":18328,"journal":{"name":"Medical image analysis","volume":"107 ","pages":"Article 103734"},"PeriodicalIF":11.8,"publicationDate":"2025-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144780233","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A generalizable diffusion framework for 3D low-dose and few-view cardiac SPECT imaging","authors":"Huidong Xie ,&nbsp;Weijie Gan ,&nbsp;Wei Ji ,&nbsp;Xiongchao Chen ,&nbsp;Alaa Alashi ,&nbsp;Stephanie L. Thorn ,&nbsp;Bo Zhou ,&nbsp;Qiong Liu ,&nbsp;Menghua Xia ,&nbsp;Xueqi Guo ,&nbsp;Yi-Hwa Liu ,&nbsp;Hongyu An ,&nbsp;Ulugbek S. Kamilov ,&nbsp;Ge Wang ,&nbsp;Albert J. Sinusas ,&nbsp;Chi Liu","doi":"10.1016/j.media.2025.103729","DOIUrl":"10.1016/j.media.2025.103729","url":null,"abstract":"<div><div>Myocardial perfusion imaging using SPECT is widely utilized to diagnose coronary artery diseases, but image quality can be negatively affected in low-dose and few-view acquisition settings. Although various deep learning methods have been introduced to improve image quality from low-dose or few-view SPECT data, previous approaches often fail to generalize across different acquisition settings, limiting realistic applicability. This work introduced DiffSPECT-3D, a diffusion framework for 3D cardiac SPECT imaging that effectively adapts to different acquisition settings without requiring further network re-training or fine-tuning. Using both image and projection data, a consistency strategy is proposed to ensure that diffusion sampling at each step aligns with the low-dose/few-view projection measurements, the image data, and the scanner geometry, thus enabling generalization to different low-dose/few-view settings. Incorporating anatomical spatial information from CT and total variation constraint, we proposed a 2.5D conditional strategy to allow DiffSPECT-3D to observe 3D contextual information from the entire image volume, addressing the 3D memory/computational issues in diffusion model. We extensively evaluated the proposed method on 1,325 clinical <span><math><msup><mrow></mrow><mrow><mtext>99m</mtext></mrow></msup></math></span>Tc tetrofosmin stress/rest studies from 795 patients. Each study was reconstructed into 5 different low-count levels and 5 different projection few-view levels for model evaluations, ranging from 1% to 50% and from 1 view to 9 view, respectively. Validated against cardiac catheterization results and diagnostic review from nuclear cardiologists, the presented results show the potential to achieve low-dose and few-view SPECT imaging without compromising clinical performance. Additionally, DiffSPECT-3D could be directly applied to full-dose SPECT images to further improve image quality, especially in a low-dose stress-first cardiac SPECT imaging protocol.</div></div>","PeriodicalId":18328,"journal":{"name":"Medical image analysis","volume":"107 ","pages":"Article 103729"},"PeriodicalIF":11.8,"publicationDate":"2025-07-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144768766","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"CXR-LT 2024: A MICCAI challenge on long-tailed, multi-label, and zero-shot disease classification from chest X-ray","authors":"Mingquan Lin ,&nbsp;Gregory Holste ,&nbsp;Song Wang ,&nbsp;Yiliang Zhou ,&nbsp;Yishu Wei ,&nbsp;Imon Banerjee ,&nbsp;Pengyi Chen ,&nbsp;Tianjie Dai ,&nbsp;Yuexi Du ,&nbsp;Nicha C. Dvornek ,&nbsp;Yuyan Ge ,&nbsp;Zuwei Guo ,&nbsp;Shouhei Hanaoka ,&nbsp;Dongkyun Kim ,&nbsp;Pablo Messina ,&nbsp;Yang Lu ,&nbsp;Denis Parra ,&nbsp;Donghyun Son ,&nbsp;Álvaro Soto ,&nbsp;Aisha Urooj ,&nbsp;Yifan Peng","doi":"10.1016/j.media.2025.103739","DOIUrl":"10.1016/j.media.2025.103739","url":null,"abstract":"<div><div>The CXR-LT series is a community-driven initiative designed to enhance lung disease classification using chest X-rays (CXR). It tackles challenges in open long-tailed lung disease classification and enhances the measurability of state-of-the-art techniques. The first event, CXR-LT 2023, aimed to achieve these goals by providing high-quality benchmark CXR data for model development and conducting comprehensive evaluations to identify ongoing issues impacting lung disease classification performance. Building on the success of CXR-LT 2023, the <strong>CXR-LT 2024</strong> expands the dataset to 377,110 chest X-rays (CXRs) and 45 disease labels, including 19 new rare disease findings. It also introduces a new focus on zero-shot learning to address limitations identified in the previous event. Specifically, CXR-LT 2024 features three tasks: (i) long-tailed classification on a large, noisy test set, (ii) long-tailed classification on a manually annotated “gold standard” subset, and (iii) zero-shot generalization to five previously unseen disease findings. This paper provides an overview of CXR-LT 2024, detailing the data curation process and consolidating state-of-the-art solutions, including the use of multimodal models for rare disease detection, advanced generative approaches to handle noisy labels, and zero-shot learning strategies for unseen diseases. Additionally, the expanded dataset enhances disease coverage to better represent real-world clinical settings, offering a valuable resource for future research. By synthesizing the insights and innovations of participating teams, we aim to advance the development of clinically realistic and generalizable diagnostic models for chest radiography.</div></div>","PeriodicalId":18328,"journal":{"name":"Medical image analysis","volume":"107 ","pages":"Article 103739"},"PeriodicalIF":11.8,"publicationDate":"2025-07-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144809816","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Attend-and-Refine: Interactive keypoint estimation and quantitative cervical vertebrae analysis for bone age assessment","authors":"Jinhee Kim ,&nbsp;Taesung Kim ,&nbsp;Taewoo Kim ,&nbsp;Dong-Wook Kim ,&nbsp;Byungduk Ahn ,&nbsp;Yoon-Ji Kim ,&nbsp;In-Seok Song ,&nbsp;Jaegul Choo","doi":"10.1016/j.media.2025.103715","DOIUrl":"10.1016/j.media.2025.103715","url":null,"abstract":"<div><div>In pediatric orthodontics, accurate estimation of growth potential is essential for developing effective treatment strategies. Our research aims to predict this potential by identifying the growth peak and analyzing cervical vertebra morphology solely through lateral cephalometric radiographs. We accomplish this by comprehensively analyzing cervical vertebral maturation (CVM) features from these radiographs. This methodology provides clinicians with a reliable and efficient tool to determine the optimal timings for orthodontic interventions, ultimately enhancing patient outcomes. A crucial aspect of this approach is the meticulous annotation of keypoints on the cervical vertebrae, a task often challenged by its labor-intensive nature. To mitigate this, we introduce Attend-and-Refine Network (ARNet), a user-interactive, deep learning-based model designed to streamline the annotation process. ARNet features Interaction-guided recalibration network, which adaptively recalibrates image features in response to user feedback, coupled with a morphology-aware loss function that preserves the structural consistency of keypoints. This novel approach substantially reduces manual effort in keypoint identification, thereby enhancing the efficiency and accuracy of the process. Extensively validated across various datasets, ARNet demonstrates remarkable performance and exhibits wide-ranging applicability in medical imaging. In conclusion, our research offers an effective AI-assisted diagnostic tool for assessing growth potential in pediatric orthodontics, marking a significant advancement in the field.</div></div>","PeriodicalId":18328,"journal":{"name":"Medical image analysis","volume":"107 ","pages":"Article 103715"},"PeriodicalIF":11.8,"publicationDate":"2025-07-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144780371","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}