COMPUTERIZED MEDICAL IMAGING AND GRAPHICS计算机化医学成像与图形学
COMPUTERIZED MEDICAL IMAGING AND GRAPHICS(英文缩写 COMPUT MED IMAG GRAP),ISSN 0895-6111,eISSN 1879-0771,中文译名:计算机化医学成像与图形学 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 | 7.422 | Q1 |
| 2022 | 5.700 | Q1 |
| 2023 | 5.400 | Q1 |
| 2024 | 4.900 | Q1 |
| 2025 | 5.500 | Q1 |
COMPUTERIZED MEDICAL IMAGING AND GRAPHICS 最新收录文献
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1. RC-EndoDepth: Two-stage self-supervised depth and motion estimation with improved scale consistency in endoscopic videos.
1. RC-EndoDepth：在内窥镜视频中具有改进尺度一致性的两阶段自监督深度和运动估计PMID:期刊:日期:2026-09-23Maintaining a stable depth scale across endoscopic video frames remains challenging, which limits downstream applications such as surgical navigation and 3D reconstruction. Existing self-supervised methods often produce accurate relative depth for individual frames but still suffer from scale drift across a video. We present RC-EndoDepth, a two-stage self-supervised framework for endoscopic depth and motion estimation. Stage One learns scale-ambiguous relative depth, pose, and intrinsics from a large-scale endoscopic dataset. Stage Two performs sequence-level refinement by enforcing multi-frame depth consistency through pose-based projection, with the goal of improving within-sequence scale stability. Applied on top of two other self-supervised backbones, Stage Two improves most of their depth metrics as well, whereas its effect on pose depends on the pose head of the backbone. We also employ a Vision Transformer (ViT) based intrinsics network to obtain more stable camera parameter estimates when calibration is unavailable. Experiments show strong relative-depth performance on SCARED, SERV-CT, and C3VD, and improved sequence-level scale consistency on SCARED sequences, without ground-truth depth supervision; the sequence-level gains are largest within the temporal span each refinement adapted on. These results suggest that the proposed training strategy is a practical option for self-supervised 3D perception in endoscopic videos. Code, partial model checkpoints, and the project page are publicly available at https://han12580.github.io/RC-EndoDepth/index.html.
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2. Dynamic adaptation and boundary-aware learning for semi-supervised medical image segmentation.
2. 面向半监督医学图像分割的动态自适应与边界感知学习PMID:期刊:日期:2026-09-16Limited annotated data remains a major challenge in medical image segmentation. Existing semi-supervised methods are often sensitive to appearance mismatch between labeled and unlabeled images, especially near ambiguous boundaries. To address this problem, we propose Dynamic Adaptation and Boundary-Aware Learning for Semi-supervised Medical Image Segmentation (DABAL), a semi-supervised framework designed to improve both supervision reliability and contour localization. Specifically, we introduce a Dynamic-static Domain Adaptive Adapter (DDAA) into the Segment Anything Model (SAM) encoder to preserve stable structural priors while providing input-dependent feature compensation for knowledge distillation. This adaptation alleviates the effect of appearance variation between labeled and unlabeled samples and makes feature transfer more stable. We further develop Boundary-Aware Supervision (BAS), which imposes explicit constraints on uncertain boundary regions and helps reduce error accumulation during semi-supervised training. Experiments show that DABAL achieves the best performance on ACDC and the best overall performance across the five colonoscopy datasets. Compared with KnowSAM, on ACDC with 10% labeled data, the proposed method improves Dice by 0.95% and IoU by 1.32%, while reducing HD95 by 0.23 and ASD by 0.14. Across the five colonoscopy benchmarks, it improves mean Dice by 3.28% and mean IoU by 3.60%, while reducing mean HD95 by 0.19.
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3. T-FDR: Trust-guided frequency-aware diffusion refinement for reliable cardiac MRI segmentation under limited annotations.
3. T-FDR：面向有限标注下可靠心脏MRI分割的信任引导频率感知扩散细化PMID:期刊:日期:2026-09-12Semi-supervised image segmentation is often limited by noisy pseudo-label propagation and insufficient modeling of boundary-sensitive features and latent semantic distributions, especially in cardiac magnetic resonance imaging (MRI) with low contrast and ambiguous structures. To address these challenges, we propose a trust-guided frequency-aware diffusion refinement (T-FDR) framework that formulates segmentation as a progressive refinement process. Specifically, the framework consists of three sequential stages: suppressing unreliable supervision, recovering boundary-sensitive features, and aligning latent semantic distributions. A Trust-guided Feature Structuring (TFS) module reduces noisy supervision by estimating pseudo-label reliability, followed by a Frequency-aware Residual Refinement (FRRF) module that enhances high-frequency boundary details in uncertain regions. Finally, a Bidirectional Diffusion Correction Module (BDCM) progressively refines latent semantic representations to improve consistency between labeled and unlabeled data. Experiments on three public cardiac MRI benchmarks demonstrate that T-FDR consistently outperforms state-of-the-art methods in both overlap and boundary metrics, indicating its effectiveness for robust semi-supervised segmentation under limited annotations.
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4. End-to-end framework integrating generative AI and deep reinforcement learning for autonomous ultrasound scanning.
4. 融合生成式人工智能与深度强化学习的端到端自主超声扫描框架PMID:期刊:日期:2026-09-07Cardiac ultrasound (US) is among the most widely used diagnostic tools in cardiology for assessing heart health, but its effectiveness is limited by operator dependence, time constraints, and human error. The shortage of trained professionals, especially in remote areas, further restricts access. These issues underscore the need for automated solutions that can ensure consistent, accessible cardiac imaging regardless of operator skill or location. Recent progress in artificial intelligence (AI), especially in deep reinforcement learning (DRL), has gained attention for enabling autonomous decision-making. However, existing DRL-based approaches to cardiac US scanning lack reproducibility, rely on proprietary data, and use simplified models. Motivated by these gaps, we present the first end-to-end framework that integrates generative AI and DRL to enable autonomous and reproducible cardiac US scanning. The framework comprises two components: (i) a conditional generative simulator combining Generative Adversarial Networks (GANs) with Variational Autoencoders (VAEs), that models the cardiac US environment producing realistic action-conditioned images; and (ii) a DRL module that leverages this simulator to learn autonomous, accurate scanning policies. The proposed framework delivers AI-driven guidance through expert-validated models that classify image type and assess quality, supports conditional generation of realistic US images, and establishes a reproducible foundation extendable to other organs. To ensure reproducibility, a publicly available dataset of real cardiac US scans is released. The solution is validated through several experiments. The VAE-GAN is benchmarked against existing GAN variants, with performance assessed using qualitative and quantitative approaches, while the DRL-based scanning system is evaluated under varying configurations to demonstrate effectiveness.
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5. DARTS-searched feedback-augmented state space model for 3D pulmonary nodule classification.
5. 用于三维肺结节分类的DARTS搜索反馈增强状态空间模型PMID:期刊:日期:2026-09-03The automatic classification of pulmonary nodules is important for early cancer diagnosis. In this work, we aim to find the optimal state space model (SSM) for pulmonary nodule classification by leveraging Differentiable Architecture Search (DARTS). To achieve DARTS, we design the Differentiable State Space Supergraph (DSSS) to search for the optimal SSM. DSSS removes convolution, dimensionality expansion, and spatial attention around the SSM of Mamba while incorporating sampling-based multi-scale feature fusion. Additionally, anatomical scan provides multiple visual sequences of three-dimensional (3D) pulmonary nodules. During the supergraph search process, a knockout (KO) strategy is introduced to progressively remove low-probability candidate operations, thereby reducing the discrepancy between the continuous supergraph and the final discrete architecture while improving search efficiency. The discrete architecture obtained by DSSS is subsequently augmented with output feedback. By stacking the feedback-augmented cells, the final model, termed the Feedback-Augmented State Space Model (FASSM), is constructed. We conducted extensive experiments on the Lung Nodule Analysis 2016 (LUNA16) dataset, and the results show that the FASSM delivers outstanding performance. The architecture search was completed in less than six hours. The resulting FASSM achieved a mean classification accuracy of 93.22% across folds 5-9 with 8.59 M parameters. FASSM achieves a favorable balance between classification performance and model scale, with 8.59 M parameters and a computational cost of 20.41 GFLOPs. Related code and results have been released at: https://github.com/GuYuIMUST/DSSS-FASSM.
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6. TeF-SAM: Prototype memory for medical lesion segmentation with text-free inference.
6. TeF-SAM：用于医学病灶分割的原型记忆与无文本推理PMID:期刊:日期:2026-08-31Medical lesion segmentation often depends on large, high-quality pixel-level annotations, which are costly to obtain because they require expert delineation. Recent multimodal methods use clinical text to improve visual representation learning, but they usually require paired image-report data during training and text input during inference. This dependency limits their use when paired reports are scarce or when segmentation is performed before report generation. To address this limitation, we propose TeF-SAM, a Segment Anything Model (SAM)-based framework for limited-text training and text-free medical lesion segmentation. TeF-SAM builds a Persistent Region-Informed Semantic Memory (PRISM) from the paired reports available during training. PRISM distils segmentation-relevant textual knowledge into a region-aware prototype space, allowing image features to retrieve semantic cues at inference without online text input. We further introduce Prototype-Driven Regional Lesion Detection (PRLD), which combines prototype-level semantic responses with multi-scale visual features to generate SAM-compatible prompts, including coarse masks, points, and semantic embeddings. We evaluate TeF-SAM on three lesion segmentation benchmarks spanning different anatomies and imaging modalities: QaTa-COV19, MosMedData+, and BUSI. Experimental results show that TeF-SAM achieves competitive segmentation performance using only 1%-15% of the available paired reports and requires no text input during inference. Our code will be released at: https://github.com/Jinxinya2/TeF-SAM.
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7. 3D-FuseNet: An innovative multimodal fusion network for enhancing survival prediction.
7. 3D-FuseNet：一种增强生存预测的创新多模态融合网络PMID:期刊:日期:2026-08-29Predicting the survival time of brain tumor patients remains a critical challenge in the medical prognosis. Existing methods often struggle to integrate heterogeneous data types, such as imaging and clinical records. This paper proposes a novel approach, the Three-Dimensional Feature Fusion Network (3D-FuseNet), designed to address these limitations by integrating high-dimensional imaging features with clinical variables through a late fusion strategy. 3D-FuseNet leverages artificial intelligence (AI) models to automatically extract features from 3D of four MRI sequence images and fuses them with key clinical variables to fully exploit their complementary nature. The network was evaluated using 10 widely-used optimizers and 5 machine learning (ML) methods to assess its performance comprehensively. In the best-case scenario, 3D-FuseNet achieved a mean absolute error (MAE) that is 4% lower than the best baseline model. Furthermore, it reduced the MAE by 2.7% compared to the lowest MAE obtained using individual models such as ResNet50, Unet3D encoder, and Vision Transformer. By effectively integrating multimodal data sources, 3D-FuseNet has demonstrated competitive performance in predicting survival in brain tumor patients, achieving moderate yet consistent improvements over the evaluated baseline. The proposed method outperforms existing architectures and is publicly available at: https://github.com/AIPMLab/3D-FuseNet.
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8. Semi-supervised medical image segmentation via Cross Reliable Knowledge Distillation.
8. 通过交叉可靠知识蒸馏的半监督医学图像分割PMID:期刊:日期:2026-08-25[中文摘要] 半监督医学图像分割旨在通过利用无标注数据来缓解对大规模标注的依赖。然而，现有方法往往缺乏有效机制，既无法充分利用有标注数据中的语义知识，也难以在无标注扰动分支之间实现可靠的知识交互。为解决这一问题，我们提出了交叉可靠知识蒸馏（Cross Reliable Knowledge Distillation，CRKD），一种面向医学图像的半监督框架。具体而言，我们首先引入类别语义蒸馏（Class Semantic Distillation，CSD）模块，通过特征空间中的类别原型对齐，将有标注数据中的类别级语义知识迁移至无标注表示，促进判别性特征学习。随后，我们提出低熵一致性（Low-Entropy Consistency，LEC）模块，动态强调可靠的低熵区域，提升多样扰动下像素级预测的一致性。最后，我们设计了交叉知识蒸馏（Cross Knowledge Distillation，CKD）模块，由交叉像素知识蒸馏（Cross Pixel Knowledge Distillation，CPKD）和交叉特征知识蒸馏（Cross Feature Knowledge Distillation，CFKD）组成，能够在特征空间和像素空间中实现可靠性感知的双向知识迁移，使扰动分支在不引入额外网络架构的情况下协同修正不确定的表示与预测。大量实验证明了CRKD的有效性，在多种成像模态和病变类型上均取得了显著的性能提升和稳健的泛化能力。代码可在 https://github.com/eeaesa/CRKD 获取。
[英文摘要] Semi-supervised medical image segmentation aims to alleviate the dependence on large-scale annotations by exploiting unlabeled data. However, existing methods often lack effective mechanisms for leveraging semantic knowledge from labeled data and enabling reliable knowledge interaction among unlabeled perturbation branches. To address this, we propose Cross Reliable Knowledge Distillation (CRKD), a semi-supervised framework for medical images. Specifically, we first introduce a Class Semantic Distillation (CSD) module, which transfers class-level semantic knowledge from labeled data to unlabeled representations through class prototype alignment in the feature space, facilitating discriminative feature learning. Then, we propose a Low-Entropy Consistency (LEC) module that dynamically emphasizes reliable low-entropy regions, improving pixel-level prediction consistency under diverse perturbations. Finally, we develop a Cross Knowledge Distillation (CKD) module, consisting of Cross Pixel Knowledge Distillation (CPKD) and Cross Feature Knowledge Distillation (CFKD), which enables reliability-aware bidirectional knowledge transfer in both feature and pixel spaces, allowing perturbation branches to collaboratively correct uncertain representations and predictions without introducing additional network architectures. Extensive experiments demonstrate the effectiveness of CRKD, achieving significant performance gains and robust generalization across diverse imaging modalities and lesion types. Code is available at https://github.com/eeaesa/CRKD.
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9. Enhancing semi-supervised skin lesion segmentation with text-guided pseudo descriptions.
9. 利用文本引导的伪描述增强半监督皮肤病变分割PMID:期刊:日期:2026-08-20Accurate segmentation of skin lesions in dermoscopy images remains challenging due to the scarcity of densely annotated medical images, as manual pixel-level annotation demands significant expert effort. Conversely, textual descriptions are more readily obtainable and can potentially provide rich semantic context for improving segmentation models. In this paper, we propose a novel framework leveraging text-guided pseudo descriptions to enhance semi-supervised skin lesion segmentation. Our approach capitalizes on the emerging capabilities of vision large-language models to generate approximate textual descriptions of unlabeled dermoscopy images, which, despite being noisy, contain valuable semantic information. We also introduce a novel image-text feature fusion mechanism that effectively integrates visual features with textual semantic. To handle the inherent high noise levels in dermoscopy images, we develop a specialized data augmentation strategy specifically designed for skin lesion characteristics. Experimental results on public benchmark datasets demonstrate that our text-guided approach significantly improves segmentation performance compared to traditional semi-supervised methods, particularly in low-annotation settings. Moreover, the textual descriptions provide interpretable insights into the segmentation process, offering clinically relevant context beyond pixel-level predictions. From a long-term perspective, our work suggests that combining minimal image annotations with more easily obtained textual descriptions presents a more efficient and effective approach to medical image segmentation. To facilitate future research, we release the ISIC-GPT dataset, a novel text-image paired dataset based on ISIC 2017 and 2018 challenges.
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10. SOM-GAN: A structure-preserving one-to-multiple generative adversarial network for unpaired medical image synthesis.
10. SOM-GAN：一种用于非配对医学图像合成的结构保持一对多生成对抗网络PMID:期刊:日期:2026-08-20[中文摘要] 基于生成对抗网络（GAN）的图像翻译已广泛应用于跨域医学图像合成。然而，现有大多数方法遵循一对一的映射范式，需要为每个目标域单独训练模型，在多目标任务（如多序列MRI合成）中增加了训练成本。尽管近期的一对多方法提高了翻译效率，但由于缺乏输入图像与合成图像之间显式的几何约束，它们往往难以保持结构一致性。分割基础模型的最新进展使医学成像中的解剖标注更加易于获取。然而，现有的图像翻译方法尚未充分利用这些标注所提供的丰富解剖与结构先验信息。因此，生成的图像可能出现解剖扭曲和语义错位，可能限制其在对解剖结构敏感的下游任务中的可靠性，例如多序列MRI中的器官和肿瘤分割。为解决上述局限，我们提出SOM-GAN，一种结构保持的一对多生成对抗网络，用于从单一源域向多个目标域进行高效且结构保持的图像合成。SOM-GAN将翻译过程建模为针对每个目标域的一对三方博弈，并采用双路径对抗设计。具体而言，多域外观判别器促使生成逼真的目标域纹理渲染，而多域结构判别器通过对图像与语义标注的联合分布进行对抗对齐，引入显式的结构约束。通过交替优化这些对抗目标，SOM-GAN使多域条件生成器能够在多个目标域上合成高质量图像。在四项典型且具有挑战性的多目标医学图像翻译任务上的大量实验表明，SOM-GAN在合成图像保真度以及下游临床分割和诊断分类任务中均优于最先进的方法。代码已发布于https://github.com/JinhaoLi98/SOM-GAN。
[英文摘要] GAN-based image translation has been widely used for cross-domain medical image synthesis. However, most existing methods follow a one-to-one mapping paradigm, requiring a separate model for each target domain and increasing the training cost in multi-target tasks such as multi-sequence MRI synthesis. Although recent one-to-multiple methods improve translation efficiency, they often struggle to preserve structural consistency because they lack explicit geometric constraints between the input and the synthesized images. Recent advances in segmentation foundation models have made anatomical annotations more accessible in medical imaging. However, existing image translation methods have not fully exploited the rich anatomical and structural priors provided by these annotations. As a result, the generated images may exhibit anatomical distortions and semantic misalignments, potentially limiting their reliability for anatomy-sensitive downstream tasks, such as organ and tumor segmentation in multi-sequence MRI. To address these limitations, we propose SOM-GAN, a Structure-preserving One-to-Multiple Generative Adversarial Network for efficient and structure-preserving image synthesis from a single source domain to multiple target domains. SOM-GAN formulates the translation process as a pair of three-player games for each target domain with a dual-path adversarial design. Specifically, a multi-domain appearance discriminator encourages realistic target-domain texture rendering, while a multi-domain structure discriminator introduces explicit structural constraints by adversarially aligning the joint distributions of images and semantic annotations. Through alternating optimization of these adversarial objectives, SOM-GAN enables the multi-domain conditional generator to synthesize high-quality images across multiple target domains. Extensive experiments on four typical yet challenging multi-target medical image translation tasks demonstrate that SOM-GAN outperforms state-of-the-art methods in both synthesized-image fidelity and downstream clinical segmentation and diagnostic classification tasks. The code is released at https://github.com/JinhaoLi98/SOM-GAN.