IEEE TRANSACTIONS ON MEDICAL IMAGINGIEEE医学成像汇刊
IEEE TRANSACTIONS ON MEDICAL IMAGING(英文缩写 IEEE T MED IMAGING),ISSN 0278-0062,eISSN 1558-254X,中文译名:IEEE医学成像汇刊 是一本学术期刊。本页汇总该期刊的最新影响因子、分区信息以及最新收录于 PubMed 的文献,帮助您快速了解期刊全貌。
发文量统计区间：2025-11-01 至 2026-10-31，按本站收录文献的发表日期统计。
期刊介绍
历年影响因子趋势
| JCR 数据年份 | 影响因子 | JCR 分区 |
|---|---|---|
| 2021 | 11.037 | Q1 |
| 2022 | 10.600 | Q1 |
| 2023 | 8.900 | Q1 |
| 2024 | 9.800 | Q1 |
| 2025 | 12.400 | Q1 |
IEEE TRANSACTIONS ON MEDICAL IMAGING 最新收录文献
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1. EM-SliceCrafter: Distilling Lightweight 2D CNNs for Efficient 3D EM Neuron Segmentation.
1. EM-SliceCrafter：蒸馏轻量级二维CNN用于高效三维电子显微镜神经元分割PMID:日期:2026-10-02Accurate 3D neuron segmentation from large-scale electron microscopy (EM) volumes is a cornerstone of connectomics, yet it is hampered by a trade-off between accuracy and computational efficiency. While 3D Convolutional Neural Networks (CNNs) offer high accuracy by predicting 3D affinity maps, their high computational cost and limited input size impede their application to large-scale data. To overcome this, we introduce EM-SliceCrafter, a novel framework that employs a lightweight 2D CNN for both efficient and precise 3D neuron segmentation. Our method generates a 3D affinity map by computing distances between embedding maps of adjacent 2D slices. To enrich the 2D network's contextual understanding, we propose a dual-distillation strategy. First, Cross-Dimension Affinity Distillation (CAD) transfers inter-slice structural knowledge from a 3D teacher network to enhance 3D continuity. Complementarily, Internal-Dimension Affinity Distillation (IAD) leverages a powerful teacher built on the pre-trained Segment Anything Model 2 (SAM2) encoder, using a learnable adapter, to distill fine-grained intra-slice boundary details. Furthermore, a Feature Grafting Interaction (FGI) module deepens knowledge transfer by integrating embeddings across the student and both teacher networks. Extensive experiments on diverse EM datasets, featuring various imaging modalities and resolutions, show that EM-SliceCrafter improves segmentation accuracy over state-of-the-art methods while achieving a 33× speedup in GPU forward inference. The code is available at https://github.com/liuxy1103/EM-SliceCrafter.
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2. Message in a Bottleneck: Interpretable Deep Learning for Dynamic Effective Brain Connectivity.
PMID:日期:2026-09-22Deep learning (DL) approaches find increasingly more applications in medical imaging analysis, where they excel at predictive tasks such as diagnosis classification, but often offer little insight on the underlying mechanisms governing complex systems, a key goal of scientific inquiry. One way to bridge this gap is to design DL architectures for modeling the underlying system reflected in imaging data, which can give insights into the system through the analysis of fitted models. In this spirit, we present a novel approach for deep extraction of causally informed features via restricted architecture (DECIFRA), designed for multivariate time series modeling. DECIFRA introduces an interpretable bottleneck in its computation flow, where dynamically generated transition matrices exclusively model the directed influences between channels over time. These matrices function as both the model's internal computational mechanism and its primary, interpretable output that gives insights into the cross-channel interactions. To validate our method, we apply it to the challenging task of learning dynamic brain connectivity from functional magnetic resonance imaging (fMRI) data. We demonstrate that DECIFRA learns stable, reproducible connectivity patterns that are discriminative for group-level analysis. Moreover, these learned dynamics can be fine-tuned for specific classification tasks, improving predictive performance while retaining interpretability. Our findings show that DECIFRA captures functionally grounded brain connectivity patterns, with certain dynamics associated with age or schizophrenia classification tasks and others consistently preserved across tasks.
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3. Dictionary-based Pathology Mining with Hard-instance-assisted Classifier Debiasing for Genetic Biomarker Prediction from WSIs.
PMID:日期:2026-09-22Prediction of genetic biomarkers, e.g., microsatellite instability in colorectal cancer is crucial for clinical decision making. However, two primary challenges hamper accurate prediction: (1) It is difficult to construct a pathology-aware representation involving the complex interconnections among pathological components. (2) Whole slide images contain a large proportion of areas unrelated to genetic biomarkers, which make the model easily overfit simple but irrelevant instances. We hereby propose a dictionary-based hierarchical pathology mining with hard-instance-assisted classifier debiasing framework to address these challenges, dubbed as DBio. Our first module, dictionary-based hierarchical pathology mining, is able to mine diverse and very fine-grained pathological contextual interaction without the limit to the distances between patches. The second module, hard-instance-assisted classifier debiasing, learns a debiased classifier via focusing on hard but task-related features, without any additional annotations. Experimental results on five cohorts show the superiority of our method, with over 4% improvement in AUROC compared with the second best on the TCGA-CRC-MSI cohort. Our analysis further shows the clinical interpretability of DBio in genetic biomarker diagnosis and potential clinical utility in survival analysis. Code, patient-level dataset splits, patch coordinates, and preprocessing scripts will be available at https://github.com/DeepMed-Lab-ECNU/D2Bio.
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4. Mechanically Anisotropic Multi-modal Phantoms for Elastography Imaging.
4. 用于弹性成像的机械各向异性多模态体模PMID:日期:2026-09-21Mechanical anisotropy is a promising biomarker of tissue health because many tissues contain organized fibrous structures that can be disrupted by injury or disease. Elastographic imaging methods have recently emerged to noninvasively quantify anisotropic mechanical properties in soft fibrous tissues like brain white matter or muscle. Calibrated phantoms that reproduce anisotropic tissue behavior are necessary for clinical translation of these methods, but durable, validated, anisotropic, elastography phantoms are not currently available. To address this need, we fabricated mechanically anisotropic lattice composite phantoms by embedding directionally scaled, 3D-printed, polyethylene glycol diacrylate (PEGDA) lattices in polyacrylamide (PAA) gel matrix. Apparent shear moduli governing deformations parallel and perpendicular to the scaling direction in these phantoms were estimated using magnetic resonance elastography (MRE) and ultrasound shear wave elastography (SWE). The properties of the phantom composite materials were independently characterized by benchtop dynamic shear testing (DST). All measurements were repeated over a four week period to assess stability of the phantoms over time. Both elastography imaging methods (MRE and SWE) detected anisotropy in lattice composite phantoms and identified differences in properties between different phantoms. Some quantitative differences were also observed between estimates of shear moduli and anisotropy obtained by MRE, SWE and DST, attributable to different features of each method. These phantoms and their fabrication protocols are available at the National Medical Phantom Library (NMPL) [1], to serve as assessment and calibration tools for anisotropic elastography imaging methods.
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5. Comorbidity-Aware Radiology Report Generation.
5. 考虑合并症的放射学报告生成PMID:日期:2026-09-18Chest X-ray report generation systems are valuable for assisting disease diagnosis and improving healthcare efficiency. However, existing methods still face two key challenges. First, multiple diseases often co-occur, leading to a combinatorial explosion of label combinations and sparse supervision for learning a generalizable image-to-label mapping. Second, standard autoregressive training is primarily optimized for local linguistic fluency, but provides limited direct supervision for global clinical fidelity, often resulting in omissions or contradictions in complex multi-abnormality cases. To address these issues, we introduce a geometry-aware multimodal large-model framework. The framework leverages hyperbolic geometry as a hierarchy-aware inductive bias and applies hierarchy-consistent constraints at three levels: representation, loss, and reinforcement signals. Specifically, we build a hyperbolic contrastive language-image pre-training (CLIP) model to align image-text semantics; estimate class prototypes from single-label cases and perform prototype-driven hyperbolic contrastive learning to enforce radius ordering for multi-label samples; and incorporate a learnable hyperbolic soft reward into the group relative policy optimization (GRPO) framework to directly optimize clinical accuracy. On the MIMIC-CXR benchmark, HyCoRG obtains BLEU-4 of 0.136 as well as clinical F1 of 0.515, indicating improved disease-level clinical consistency. Ablation studies confirm that both the framework and the geometric constraints contribute independently and synergistically. Our approach improves both linguistic quality and diagnostic fidelity, and offers a transferable hierarchy-aware paradigm for multi-label medical text generation. The code is publicly available at https://github.com/hongzezhu/HyCoRG.
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6. SurgDepth: Test-Time Adaptation of Depth Foundation Models for Surgical Scene Understanding.
PMID:日期:2026-09-17Accurate depth estimation from endoscopic images is fundamental to surgical navigation, augmented reality, and robotic assistance, yet depth foundation models trained on natural images degrade severely on surgical endoscopy, and existing adaptation methods require surgical training data for each new camera and clinical environment. We propose SurgDepth, a test-time adaptation (TTA) framework that adapts pre-trained depth foundation models to surgical endoscopy without any labeled surgical data. SurgDepth combines two self-supervised signals-stereo photometric consistency for rectified stereo pairs, and flip equivariance requiring only single images-with selective decoder adaptation and two tuning-free reliability mechanisms: a progress-aware anchor regularization and a structural-trust safeguard for poorly-illuminated sequences. Through the first systematic study of TTA for dense depth regression (eight baselines), we show that existing TTA methods consistently fail in this setting: entropy-based methods degrade regardless of the number of adapted parameters, and temporal-photometric adaptation diverges on non-rigid tissue. SurgDepth reduces AbsRel by up to 63% with stereo pairs and 42% without, improves four of five cross-domain evaluation settings (three of four unseen datasets; Hamlyn is evaluated under two protocols), generalizes across seven foundation models, and attains the lowest AbsRel (0.128) on the complete standard Hamlyn benchmark among all reported methods. All results evaluate relative (median-scaled) depth; 20-step convergence (0.4 s) makes SurgDepth practical for relative-depth applications such as augmented-reality overlay and reconstruction initialization.
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7. SAMReg: SAM-enabled Image Registration with ROI-based Correspondence.
PMID:日期:2026-09-16This paper describes a new spatial correspondence representation based on paired regions-of-interest (ROIs), for medical image registration. The distinct properties of the proposed ROI-based correspondence are discussed, in the context of potential benefits in clinical applications following image registration, compared with alternative correspondence-representing approaches, such as those based on sampled displacements and spatial transformation functions. These benefits include a clear connection between learning-based image registration and segmentation, which in turn motivates two cases of image registration approaches using (pre-)trained segmentation networks. Based on the segment anything model (SAM), a vision foundation model for segmentation, we develop a new registration algorithm SAMReg, which does not require any training (or training data), gradient-based fine-tuning or prompt engineering. The proposed SAMReg models are evaluated across five real-world applications, including intra-subject registration tasks with cardiac MR and lung CT, challenging inter-subject registration scenarios with prostate MR and retinal imaging, and an additional evaluation with a non-clinical example with aerial image registration. The proposed methods outperform both intensity-based iterative algorithms and DDF-predicting learning-based networks across tested metrics including Dice and target registration errors on anatomical structures, and further demonstrates competitive performance compared to weakly-supervised registration approaches that rely on fully-segmented training data. Open source code and examples are available at SAMReg.
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8. Separation of Water Components from Multi-Echo MRI Data Using Deep Learning-Assisted Subspace Modeling.
8. 使用深度学习辅助子空间建模从多回波MRI数据中分离水成分PMID:日期:2026-09-16MRI data acquired using multi-echo spin-echo or gradient-echo sequences contain "encoded" water signals from various tissue compartments, i.e., myelin, axon, and extracellular space. Accurate separation of these components is desirable for tissue characterization in healthy and diseased states. However, this signal decomposition problem is very challenging because the encoding matrix is highly ill-conditioned and as a result, its solution is often sensitive to noise and practical data perturbations (e.g., B field inhomogeneity). This paper presents a new method to address this problem, which synergistically integrates physics-based spatiotemporal priors and machine learning to constrain the variations allowed for each water component while providing flexibility to capture subject- and experiment-dependent signal changes. Specifically, we introduced a probabilistic union-of-subspaces model with pre-learned spectral bases to enforce temporal constraints while incorporating spatial priors derived from deep learning-based image translation. To further accommodate subject- and experiment-specific signal variations, a generalized series model was employed to adapt the pre-learned subspace model to a specific dataset. With the proposed model and learned priors, water separation was done under the Bayesian statistical framework. Numerical simulations and experimental evaluations demonstrated that the proposed method significantly improved the accuracy of the estimated myelin, axonal, and extracellular water components. The new method will enhance the practical utility of multi-component water imaging for basic neuroscience and clinical applications and also provide a useful framework for separating overlapping spatiotemporal signals.
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9. Conjugate Bayesian Evidential Learning for Uncertainty-Aware Nasopharyngeal Carcinoma Segmentation.
9. 用于不确定性感知鼻咽癌分割的共轭贝叶斯证据学习PMID:日期:2026-09-15Accurate automatic segmentation of the gross tumor volume (GTV) in nasopharyngeal carcinoma (NPC) is critical for radiotherapy planning, yet conventional deterministic models do not adequately characterize the inherent uncertainty associated with ambiguous tumor boundaries on magnetic resonance imaging (MRI). Existing evidential deep learning (EDL) methods for uncertainty quantification typically rely on fixed priors and are theoretically prone to evidence saturation, thereby limiting the reliability of uncertainty estimation. To address these issues, we propose a conjugate Bayesian evidential segmentation (CoBESeg) framework for uncertainty-aware NPC GTV segmentation. To alleviate evidence saturation in conventional EDL, a hierarchical variance evidence network is introduced to explicitly integrate anatomical prior knowledge with image-driven evidence through conjugate Bayesian updating. To improve feature discrimination in ambiguous boundary regions, a fuzzy prototype contrastive learning strategy is proposed to enhance the discriminability of evidential representations. Furthermore, CoBESeg incorporates a feature distance-aware evidential calibration strategy to dynamically calibrate evidence strength at test time. Experiments on three multicenter NPC MRI datasets demonstrate that CoBESeg achieves superior segmentation accuracy and more reliable uncertainty estimation compared with state-of-the-art methods, while also supporting risk filtering under clinical shift. The code is available at https://github.com/zhangchi73/hvenfpcl.
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10. Bridging the Gap in Ophthalmic AI: MM-Retinal-Reason Dataset and OphthaReason Model toward Dynamic Multimodal Reasoning.
10. 弥合眼科人工智能的鸿沟：面向动态多模态推理的MM-Retinal-Reason数据集与OphthaReason模型PMID:日期:2026-09-15Multimodal large language models (MLLMs) have recently demonstrated remarkable reasoning abilities under reinforcement learning (RL) paradigm. However, most existing multimodal medical reasoning models focus on basic reasoning, which refers to shallow inference based on visual feature matching. In contrast, real-world clinical diagnosis extends beyond basic reasoning, demanding complex reasoning that integrates heterogeneous clinical information (such as chief complaints and medical history) with multimodal medical imaging data. To bridge this gap, we introduce MM-Retinal-Reason, an ophthalmic multimodal dataset covering the full spectrum of perception and reasoning. Specifically, it is the first dataset in ophthalmology to encompass both basic and complex reasoning tasks with Chain-of-Thought (CoT) trajectories, aiming to enhance visual-centric reasoning and emulate realistic clinical decision-making. Building upon MM-Retinal-Reason, we propose OphthaReason, the first RL-enhanced ophthalmic multimodal reasoning model with step-by-step reasoning traces. To enable flexible adaptation to both basic and complex reasoning tasks, we further introduce Uncertainty-Aware Dynamic Thinking (UADT), which estimates sample-level uncertainty via entropy and dynamically modulates exploration depth through a shaped advantage mechanism. Comprehensive experiments demonstrate the effectiveness of our model on both basic and complex reasoning tasks, outperforming general-purpose MLLMs, medical MLLMs, RL-based medical MLLMs, and ophthalmic MLLMs by at least 15.47%. Project Page: link.