Medical image analysis最新文献

{"title":"Structure-guided MR-to-CT synthesis with spatial and semantic alignments for attenuation correction of whole-body PET/MR imaging","authors":"Jiaxu Zheng ,&nbsp;Zhenrong Shen ,&nbsp;Lichi Zhang ,&nbsp;Qun Chen","doi":"10.1016/j.media.2025.103622","DOIUrl":"10.1016/j.media.2025.103622","url":null,"abstract":"<div><div>Image synthesis from Magnetic Resonance (MR) to Computed Tomography (CT) can estimate the electron density of tissues, thereby facilitating Positron Emission Tomography (PET) attenuation correction in whole-body PET/MR imaging. Whole-body MR-to-CT synthesis faces several challenges including the spatial misalignment caused by tissue variety and respiratory movements, and the complex intensity mapping due to large intensity variations across the whole body. However, existing MR-to-CT synthesis methods mainly focus on body sub-regions, making them ineffective in addressing these challenges. Here we propose a novel whole-body MR-to-CT synthesis framework, which consists of three novel modules to tackle these challenges: (1) Structure-Guided Synthesis module leverages structure-guided attention gates to enhance synthetic image quality by diminishing unnecessary contours of soft tissues; (2) Spatial Alignment module yields precise registration between paired MR and CT images by taking into account the impacts of tissue volumes and respiratory movements, thus providing well-aligned ground-truth CT images during training; (3) Semantic Alignment module utilizes contrastive learning to constrain organ-related semantic information, thereby ensuring the semantic authenticity of synthetic CT images. Extensive experiments demonstrate that our method produces visually plausible and semantically accurate CT images, outperforming existing approaches in both synthetic image quality and PET attenuation correction accuracy.</div></div>","PeriodicalId":18328,"journal":{"name":"Medical image analysis","volume":"103 ","pages":"Article 103622"},"PeriodicalIF":10.7,"publicationDate":"2025-05-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144031343","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":"Next-generation surgical navigation: Marker-less multi-view 6DoF pose estimation of surgical instruments","authors":"Jonas Hein ,&nbsp;Nicola Cavalcanti ,&nbsp;Daniel Suter ,&nbsp;Lukas Zingg ,&nbsp;Fabio Carrillo ,&nbsp;Lilian Calvet ,&nbsp;Mazda Farshad ,&nbsp;Nassir Navab ,&nbsp;Marc Pollefeys ,&nbsp;Philipp Fürnstahl","doi":"10.1016/j.media.2025.103613","DOIUrl":"10.1016/j.media.2025.103613","url":null,"abstract":"<div><div>State-of-the-art research of traditional computer vision is increasingly leveraged in the surgical domain. A particular focus in computer-assisted surgery is to replace marker-based tracking systems for instrument localization with pure image-based 6DoF pose estimation using deep-learning methods. However, state-of-the-art single-view pose estimation methods do not yet meet the accuracy required for surgical navigation. In this context, we investigate the benefits of multi-view setups for highly accurate and occlusion-robust 6DoF pose estimation of surgical instruments and derive recommendations for an ideal camera system that addresses the challenges in the operating room. Our contributions are threefold. First, we present a multi-view RGB-D video dataset of ex-vivo spine surgeries, captured with static and head-mounted cameras and including rich annotations for surgeon, instruments, and patient anatomy. Second, we perform an extensive evaluation of three state-of-the-art single-view and multi-view pose estimation methods, analyzing the impact of camera quantities and positioning, limited real-world data, and static, hybrid, or fully mobile camera setups on the pose accuracy, occlusion robustness, and generalizability. Third, we design a multi-camera system for marker-less surgical instrument tracking, achieving an average position error of 1.01<!--> <!-->mm and orientation error of 0.89° for a surgical drill, and 2.79<!--> <!-->mm and 3.33° for a screwdriver under optimal conditions. Our results demonstrate that marker-less tracking of surgical instruments is becoming a feasible alternative to existing marker-based systems.</div></div>","PeriodicalId":18328,"journal":{"name":"Medical image analysis","volume":"103 ","pages":"Article 103613"},"PeriodicalIF":10.7,"publicationDate":"2025-05-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144069892","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":"Nested hierarchical group-wise registration with a graph-based subgrouping strategy for efficient template construction","authors":"Tongtong Che ,&nbsp;Lin Zhang ,&nbsp;Debin Zeng ,&nbsp;Yan Zhao ,&nbsp;Haoying Bai ,&nbsp;Jichang Zhang ,&nbsp;Xiuying Wang ,&nbsp;Shuyu Li","doi":"10.1016/j.media.2025.103624","DOIUrl":"10.1016/j.media.2025.103624","url":null,"abstract":"<div><div>Accurate and efficient group-wise registration for medical images is fundamentally important to construct a common template image for population-level analysis. However, current group-wise registration faces the challenges posed by the algorithm’s efficiency and capacity, and adaptability to large variations in the subject populations. This paper addresses these challenges with a novel Nested Hierarchical Group-wise Registration (NHGR) framework. Firstly, to alleviate the registration burden due to significant population variations, a new subgrouping strategy is proposed to serve as a “divide and conquer” mechanism that divides a large population into smaller subgroups. The subgroups with a hierarchical sequence are formed by gradually expanding the scale factors that relate to feature similarity and then conducting registration at the subgroup scale as the multi-scale conquer strategy. Secondly, the nested hierarchical group-wise registration is proposed to conquer the challenges due to the efficiency and capacity of the model from three perspectives. (1) Population level: the global group-wise registration is performed to generate age-related sub-templates from local subgroups progressively to the global population. (2) Subgroup level: the local group-wise registration is performed based on local image distributions to reduce registration error and achieve rapid optimization of sub-templates. (3) Image pair level: a deep multi-resolution registration network is employed for better registration efficiency. The proposed framework was evaluated on the brain datasets of adults and adolescents, respectively from 18 to 96 years and 5 to 21 years. Experimental results consistently demonstrated that our proposed group-wise registration method achieved better performance in terms of registration efficiency, template sharpness, and template centrality.</div></div>","PeriodicalId":18328,"journal":{"name":"Medical image analysis","volume":"103 ","pages":"Article 103624"},"PeriodicalIF":10.7,"publicationDate":"2025-05-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144069890","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":"Error correcting 2D–3D cascaded network for myocardial infarct scar segmentation on late gadolinium enhancement cardiac magnetic resonance images","authors":"Matthias Schwab ,&nbsp;Mathias Pamminger ,&nbsp;Christian Kremser ,&nbsp;Daniel Obmann ,&nbsp;Markus Haltmeier ,&nbsp;Agnes Mayr","doi":"10.1016/j.media.2025.103594","DOIUrl":"10.1016/j.media.2025.103594","url":null,"abstract":"<div><div>Late gadolinium enhancement (LGE) cardiac magnetic resonance (CMR) imaging is considered the in vivo reference standard for assessing infarct size (IS) and microvascular obstruction (MVO) in ST-elevation myocardial infarction (STEMI) patients. However, the exact quantification of those markers of myocardial infarct severity remains challenging and very time-consuming. As LGE distribution patterns can be quite complex and hard to delineate from the blood pool or epicardial fat, automatic segmentation of LGE CMR images is challenging. In this work, we propose a cascaded framework of two-dimensional and three-dimensional convolutional neural networks (CNNs) which enables to calculate the extent of myocardial infarction in a fully automated way. By artificially generating segmentation errors which are characteristic for 2D CNNs during training of the cascaded framework we are enforcing the detection and correction of 2D segmentation errors and hence improve the segmentation accuracy of the entire method. The proposed method was trained and evaluated on two publicly available datasets. We perform comparative experiments where we show that our framework outperforms state-of-the-art reference methods in segmentation of myocardial infarction. Furthermore, in extensive ablation studies we show the advantages that come with the proposed error correcting cascaded method. The code of this project is publicly available at <span><span>https://github.com/matthi99/EcorC.git</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":18328,"journal":{"name":"Medical image analysis","volume":"103 ","pages":"Article 103594"},"PeriodicalIF":10.7,"publicationDate":"2025-05-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144064025","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 dissection trajectories from expert surgical videos via imitation learning with equivariant diffusion","authors":"Hongyu Wang ,&nbsp;Yonghao Long ,&nbsp;Yueyao Chen ,&nbsp;Hon-Chi Yip ,&nbsp;Markus Scheppach ,&nbsp;Philip Wai-Yan Chiu ,&nbsp;Yeung Yam ,&nbsp;Helen Mei-Ling Meng ,&nbsp;Qi Dou","doi":"10.1016/j.media.2025.103599","DOIUrl":"10.1016/j.media.2025.103599","url":null,"abstract":"<div><div>Endoscopic Submucosal Dissection (ESD) constitutes a firmly well-established technique within endoscopic resection for the elimination of epithelial lesions. Dissection trajectory prediction in ESD videos has the potential to strengthen surgical skills training and simplify surgical skills training. However, this approach has been seldom explored in previous research. While imitation learning has proven effective in learning skills from expert demonstrations, it encounters difficulties in predicting uncertain future movements, learning geometric symmetries and generalizing to diverse surgical scenarios. This paper introduces imitation learning for the critical task of predicting dissection trajectories from expert video demonstrations. We propose a novel Implicit Diffusion Policy with Equivariant Representations for Imitation Learning (iDPOE) to address this variability. Our method implicitly models expert behaviors using a joint state–action distribution, capturing the inherent stochasticity of future dissection trajectories and enabling robust visual representation learning across various endoscopic views. By incorporating a diffusion model in policy learning, our approach facilitates efficient training and sampling, resulting in more accurate predictions and improved generalization. Additionally, we integrate equivariance into the learning process to enhance the model’s ability to generalize to geometric symmetries in trajectory prediction. To enable conditional sampling from the implicit policy, we develop a forward-process guided action inference strategy to correct state mismatches. We evaluated our method using a collected ESD video dataset comprising nearly 2000 clips. Experimental results demonstrate that our approach outperforms both explicit and implicit state-of-the-art methods in trajectory prediction. As far as we know, this is the first endeavor to utilize imitation learning-based techniques for surgical skill learning in terms of dissection trajectory prediction.</div></div>","PeriodicalId":18328,"journal":{"name":"Medical image analysis","volume":"103 ","pages":"Article 103599"},"PeriodicalIF":10.7,"publicationDate":"2025-05-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144069883","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":"Confidence intervals for performance estimates in brain MRI segmentation","authors":"Rosana El Jurdi ,&nbsp;Gaël Varoquaux ,&nbsp;Olivier Colliot","doi":"10.1016/j.media.2025.103565","DOIUrl":"10.1016/j.media.2025.103565","url":null,"abstract":"<div><div>Medical segmentation models are evaluated empirically. As such an evaluation is based on a limited set of example images, it is unavoidably noisy. Beyond a mean performance measure, reporting confidence intervals is thus crucial. However, this is rarely done in medical image segmentation. The width of the confidence interval depends on the test set size and on the spread of the performance measure (its standard-deviation across the test set). For classification, many test images are needed to avoid wide confidence intervals. Segmentation, however, has not been studied, and it differs by the amount of information brought by a given test image. In this paper, we study the typical confidence intervals in the context of segmentation in 3D brain magnetic resonance imaging (MRI). We carry experiments on using the standard nnU-net framework, two datasets from the Medical Decathlon challenge that concern brain MRI (hippocampus and brain tumor segmentation) and two performance measures: the Dice Similarity Coefficient and the Hausdorff distance. We show that the parametric confidence intervals are reasonable approximations of the bootstrap estimates for varying test set sizes and spread of the performance metric. Importantly, we show that the test size needed to achieve a given precision is often much lower than for classification tasks. Typically, a 1% wide confidence interval requires about 100–200 test samples when the spread is low (standard-deviation around 3%). More difficult segmentation tasks may lead to higher spreads and require over 1000 samples. The corresponding code and notebooks are available on GitHub at <span><span>https://github.com/rosanajurdi/SegVal_Repo</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":18328,"journal":{"name":"Medical image analysis","volume":"103 ","pages":"Article 103565"},"PeriodicalIF":10.7,"publicationDate":"2025-05-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144069985","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":"CausalMixNet: A mixed-attention framework for causal intervention in robust medical image diagnosis","authors":"Yajie Zhang ,&nbsp;Yu-An Huang ,&nbsp;Yao Hu ,&nbsp;Rui Liu ,&nbsp;Jibin Wu ,&nbsp;Zhi-An Huang ,&nbsp;Kay Chen Tan","doi":"10.1016/j.media.2025.103581","DOIUrl":"10.1016/j.media.2025.103581","url":null,"abstract":"<div><div>Confounding factors inherent in medical images can significantly impact the causal exploration capabilities of deep learning models, resulting in compromised accuracy and diminished generalization performance. In this paper, we present an innovative methodology named CausalMixNet that employs query-mixed intra-attention and key&amp;value-mixed inter-attention to probe causal relationships between input images and labels. For mitigating unobservable confounding factors, CausalMixNet integrates the non-local reasoning module (NLRM) and the key&amp;value-mixed inter-attention (KVMIA) to conduct a front-door adjustment strategy. Furthermore, CausalMixNet incorporates a patch-masked ranking module (PMRM) and query-mixed intra-attention (QMIA) to enhance mediator learning, thereby facilitating causal intervention. The patch mixing mechanism applied to query/(key&amp;value) features within QMIA and KVMIA specifically targets lesion-related feature enhancement and the inference of average causal effect inference. CausalMixNet consistently outperforms existing methods, achieving superior accuracy and F1-scores across in-domain and out-of-domain scenarios on multiple datasets, with an average improvement of 3% over the closest competitor. Demonstrating robustness against noise, gender bias, and attribute bias, CausalMixNet excels in handling unobservable confounders, maintaining stable performance even in challenging conditions.</div></div>","PeriodicalId":18328,"journal":{"name":"Medical image analysis","volume":"103 ","pages":"Article 103581"},"PeriodicalIF":10.7,"publicationDate":"2025-05-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144064023","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":"SegQC: a segmentation network-based framework for multi-metric segmentation quality control and segmentation error detection in volumetric medical images","authors":"Bella Specktor-Fadida ,&nbsp;Liat Ben-Sira ,&nbsp;Dafna Ben-Bashat ,&nbsp;Leo Joskowicz","doi":"10.1016/j.media.2025.103638","DOIUrl":"10.1016/j.media.2025.103638","url":null,"abstract":"<div><div>Quality control (QC) of structures segmentation in volumetric medical images is important for identifying segmentation errors in clinical practice and for facilitating model development by enhancing network performance in semi-supervised and active learning scenarios. This paper introduces SegQC, a novel framework for segmentation quality estimation and segmentation error detection. SegQC computes an estimate measure of the quality of a segmentation in volumetric scans and in their individual slices and identifies possible segmentation error regions within a slice. The key components of SegQC include: 1) SegQC<img>Net, a deep network that inputs a scan and its segmentation mask and outputs segmentation error probabilities for each voxel in the scan; 2) three new segmentation quality metrics computed from the segmentation error probabilities; 3) a new method for detecting possible segmentation errors in scan slices computed from the segmentation error probabilities. We introduce a novel evaluation scheme to measure segmentation error discrepancies based on an expert radiologist’s corrections of automatically produced segmentations that yields smaller observer variability and is closer to actual segmentation errors. We demonstrate SegQC on three fetal structures in 198 fetal MRI scans – fetal brain, fetal body and the placenta. To assess the benefits of SegQC, we compare it to the unsupervised Test Time Augmentation (TTA)-based QC and to supervised autoencoder (AE)-based QC. Our studies indicate that SegQC outperforms TTA-based quality estimation for whole scans and individual slices in terms of Pearson correlation and MAE for fetal body and fetal brain structures segmentation as well as for volumetric overlap metrics estimation of the placenta structure. Compared to both unsupervised TTA and supervised AE methods, SegQC achieves lower MAE for both 3D and 2D Dice estimates and higher Pearson correlation for volumetric Dice. Our segmentation error detection method achieved recall and precision rates of 0.77 and 0.48 for fetal body, and 0.74 and 0.55 for fetal brain segmentation error detection, respectively. Ranking derived from metrics estimation surpasses rankings based on entropy and sum for TTA and SegQC<img>Net estimations, respectively. SegQC provides high-quality metrics estimation for both 2D and 3D medical images as well as error localization within slices, offering important improvements to segmentation QC.</div></div>","PeriodicalId":18328,"journal":{"name":"Medical image analysis","volume":"103 ","pages":"Article 103638"},"PeriodicalIF":10.7,"publicationDate":"2025-05-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144069886","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":"PULASki: Learning inter-rater variability using statistical distances to improve probabilistic segmentation","authors":"Soumick Chatterjee ,&nbsp;Franziska Gaidzik ,&nbsp;Alessandro Sciarra ,&nbsp;Hendrik Mattern ,&nbsp;Gábor Janiga ,&nbsp;Oliver Speck ,&nbsp;Andreas Nürnberger ,&nbsp;Sahani Pathiraja","doi":"10.1016/j.media.2025.103623","DOIUrl":"10.1016/j.media.2025.103623","url":null,"abstract":"<div><div>In the domain of medical imaging, many supervised learning based methods for segmentation face several challenges such as high variability in annotations from multiple experts, paucity of labelled data and class imbalanced datasets. These issues may result in segmentations that lack the requisite precision for clinical analysis and can be misleadingly overconfident without associated uncertainty quantification. This work proposes the PULASki method as a computationally efficient generative tool for biomedical image segmentation that accurately captures variability in expert annotations, even in small datasets. This approach makes use of an improved loss function based on statistical distances in a conditional variational autoencoder structure (Probabilistic UNet) , which improves learning of the conditional decoder compared to the standard cross-entropy particularly in class imbalanced problems. The proposed method was analysed for two structurally different segmentation tasks (intracranial vessel and multiple sclerosis (MS) lesion) and compare our results to four well-established baselines in terms of quantitative metrics and qualitative output. These experiments involve class-imbalanced datasets characterised by challenging features, including suboptimal signal-to-noise ratios and high ambiguity. Empirical results demonstrate the PULASKi method outperforms all baselines at the 5% significance level. Our experiments are also of the first to present a comparative study of the computationally feasible segmentation of complex geometries using 3D patches and the traditional use of 2D slices. The generated segmentations are shown to be much more anatomically plausible than in the 2D case, particularly for the vessel task. Our method can also be applied to a wide range of multi-label segmentation tasks and is useful for downstream tasks such as hemodynamic modelling (computational fluid dynamics and data assimilation), clinical decision making, and treatment planning.</div></div>","PeriodicalId":18328,"journal":{"name":"Medical image analysis","volume":"103 ","pages":"Article 103623"},"PeriodicalIF":10.7,"publicationDate":"2025-05-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144069986","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":"Rethinking boundary detection in deep learning-based medical image segmentation","authors":"Yi Lin ,&nbsp;Dong Zhang ,&nbsp;Xiao Fang ,&nbsp;Yufan Chen ,&nbsp;Kwang-Ting Cheng ,&nbsp;Hao Chen","doi":"10.1016/j.media.2025.103615","DOIUrl":"10.1016/j.media.2025.103615","url":null,"abstract":"<div><div>Medical image segmentation is a pivotal task within the realms of medical image analysis and computer vision. While current methods have shown promise in accurately segmenting major regions of interest, the precise segmentation of boundary areas remains challenging. In this study, we propose a novel network architecture named CTO, which combines Convolutional Neural Networks (CNNs), Vision Transformer (ViT) models, and explicit edge detection operators to tackle this challenge. CTO surpasses existing methods in terms of segmentation accuracy and strikes a better balance between accuracy and efficiency, without the need for additional data inputs or label injections. Specifically, CTO adheres to the canonical encoder–decoder network paradigm, with a dual-stream encoder network comprising a mainstream CNN stream for capturing local features and an auxiliary StitchViT stream for integrating long-range dependencies. Furthermore, to enhance the model’s ability to learn boundary areas, we introduce a boundary-guided decoder network that employs binary boundary masks generated by dedicated edge detection operators to provide explicit guidance during the decoding process. We validate the performance of CTO through extensive experiments conducted on seven challenging medical image segmentation datasets, namely ISIC 2016, PH2, ISIC 2018, CoNIC, LiTS17, BraTS, and BTCV. Our experimental results unequivocally demonstrate that CTO achieves state-of-the-art accuracy on these datasets while maintaining competitive model complexity. The codes have been released at: <span><span>CTO</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":18328,"journal":{"name":"Medical image analysis","volume":"103 ","pages":"Article 103615"},"PeriodicalIF":10.7,"publicationDate":"2025-05-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143916897","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}