Recent advances in medical image segmentation have been driven by deep learning; however, most existing methods remain limited by modality-specific designs and exhibit poor adaptability to dynamic medical imaging scenarios. The Segment Anything Model 2 (SAM2) and its related variants, which introduce a streaming memory mechanism for real-time video segmentation, present new opportunities for prompt-based, generalizable solutions. Nevertheless, adapting these models to medical video scenarios typically requires large-scale datasets for retraining or transfer learning, leading to high computational costs and the risk of catastrophic forgetting. To address these challenges, we propose DD-SAM2, an efficient adaptation framework for SAM2 that incorporates a Depthwise-Dilated Adapter (DD-Adapter) to enhance multi-scale feature extraction with minimal parameter overhead. This design enables effective fine-tuning of SAM2 on medical videos with limited training data. Unlike existing adapter-based methods focused solely on static images, DD-SAM2 fully exploits SAM2's streaming memory for medical video object tracking and segmentation. Comprehensive evaluations on TrackRad2025 (tumor segmentation) and EchoNet-Dynamic (left ventricle tracking) datasets demonstrate superior performance, achieving Dice scores of 0.93 and 0.97, respectively. To the best of our knowledge, this work provides an initial attempt at systematically exploring adapter-based SAM2 fine-tuning for medical video segmentation and tracking. Code, datasets, and models will be publicly available at https://github.com/apple1986/DD-SAM2.

Accurate classification of computed tomography (CT) images is essential for diagnosis and treatment planning, but existing methods often struggle with the subtle and spatially diverse nature of pathological features. Current approaches typically process images uniformly, limiting their ability to detect localized abnormalities that require focused analysis. We introduce UGPL, an uncertainty-guided progressive learning framework that performs a global-to-local analysis by first identifying regions of diagnostic ambiguity and then conducting detailed examination of these critical areas. Our approach employs evidential deep learning to quantify predictive uncertainty, guiding the extraction of informative patches through a non-maximum suppression mechanism that maintains spatial diversity. This progressive refinement strategy, combined with an adaptive fusion mechanism, enables UGPL to integrate both contextual information and fine-grained details. Experiments across three CT datasets demonstrate that UGPL consistently outperforms state-of-the-art methods, achieving improvements of 3.29%, 2.46%, and 8.08% in accuracy for kidney abnormality, lung cancer, and COVID-19 detection, respectively. Our analysis shows that the uncertainty-guided component provides substantial benefits, with performance dramatically increasing when the full progressive learning pipeline is implemented. Our code is available at: https://github.com/shravan-18/UGPL

Mild Cognitive Impairment (MCI) serves as a prodromal stage of Alzheimer's Disease (AD), where early identification and intervention can effectively slow the progression to dementia. However, diagnosing AD remains a significant challenge in neurology due to the confounders caused mainly by the selection bias of multimodal data and the complex relationships between variables. To address these issues, we propose a novel visual-language causal intervention framework named Alzheimer's Disease Prediction with Cross-modal Causal Intervention (ADPC) for diagnostic assistance. Our ADPC employs large language model (LLM) to summarize clinical data under strict templates, maintaining structured text outputs even with incomplete or unevenly distributed datasets. The ADPC model utilizes Magnetic Resonance Imaging (MRI), functional MRI (fMRI) images and textual data generated by LLM to classify participants into Cognitively Normal (CN), MCI, and AD categories. Because of the presence of confounders, such as neuroimaging artifacts and age-related biomarkers, non-causal models are likely to capture spurious input-output correlations, generating less reliable results. Our framework implicitly eliminates confounders through causal intervention. Experimental results demonstrate the outstanding performance of our method in distinguishing CN/MCI/AD cases, achieving state-of-the-art (SOTA) metrics across most evaluation metrics. The study showcases the potential of integrating causal reasoning with multi-modal learning for neurological disease diagnosis.

Ultra-high resolution 7 tesla (7T) magnetic resonance imaging (MRI) provides detailed anatomical views, offering better signal-to-noise ratio, resolution and tissue contrast than 3T MRI, though at the cost of accessibility. We present an advanced deep learning model for synthesizing 7T brain MRI from 3T brain MRI. Paired 7T and 3T T1-weighted images were acquired from 172 participants (124 cognitively unimpaired, 48 impaired) from the Swedish BioFINDER-2 study. To synthesize 7T MRI from 3T images, we trained two models: a specialized U-Net, and a U-Net integrated with a generative adversarial network (GAN U-Net). Our models outperformed two additional state-of-the-art 3T-to-7T models in image-based evaluation metrics. Four blinded MRI professionals judged our synthetic 7T images as comparable in detail to real 7T images, and superior in subjective visual quality to 7T images, apparently due to the reduction of artifacts. Importantly, automated segmentations of the amygdalae of synthetic GAN U-Net 7T images were more similar to manually segmented amygdalae (n=20), than automated segmentations from the 3T images that were used to synthesize the 7T images. Finally, synthetic 7T images showed similar performance to real 3T images in downstream prediction of cognitive status using MRI derivatives (n=3,168). In all, we show that synthetic T1-weighted brain images approaching 7T quality can be generated from 3T images, which may improve image quality and segmentation, without compromising performance in downstream tasks. Future directions, possible clinical use cases, and limitations are discussed.

Advancements in cardiac computed tomography angiography (CCTA) have enabled the extraction of physiological data from an anatomy-based imaging modality. This review outlines the key methodologies for deriving fractional flow reserve (FFR) from CCTA, with a focus on two primary methods: 1) computational fluid dynamics-based FFR (CT-FFR) and 2) plaque-derived ischemia assessment using artificial intelligence and quantitative plaque metrics. These techniques have expanded the role of CCTA beyond anatomical assessment, allowing for concurrent evaluation of coronary physiology without the need for invasive testing. This review provides an overview of the principles, workflows, and limitations of each technique and aims to inform on the current state and future direction of non-invasive coronary physiology assessment.

This study sought to develop a radiomics model capable of predicting axillary lymph node metastasis (ALNM) in patients with invasive breast cancer (IBC) based on dual-sequence magnetic resonance imaging(MRI) of diffusion-weighted imaging (DWI) and dynamic contrast enhancement (DCE) data. The interpretability of the resultant model was probed with the SHAP (Shapley Additive Explanations) method. Established inclusion/exclusion criteria were used to retrospectively compile MRI and matching clinical data from 183 patients with pathologically confirmed IBC from our hospital evaluated between June 2021 and December 2023. All of these patients had undergone plain and enhanced MRI scans prior to treatment. These patients were separated according to their pathological biopsy results into those with ALNM (n = 107) and those without ALNM (n = 76). These patients were then randomized into training (n = 128) and testing (n = 55) cohorts at a 7:3 ratio. Optimal radiomics features were selected from the extracted data. The random forest method was used to establish three predictive models (DWI, DCE, and combined DWI + DCE sequence models). Area under the curve (AUC) values for receiver operating characteristic (ROC) curves were utilized to assess model performance. The DeLong test was utilized to compare model predictive efficacy. Model discrimination was assessed based on the integrated discrimination improvement (IDI) method. Decision curves revealed net clinical benefits for each of these models. The SHAP method was used to achieve the best model interpretability. Clinicopathological characteristics (age, menopausal status, molecular subtypes, and estrogen receptor, progesterone receptor, human epidermal growth factor receptor 2, and Ki-67 status) were comparable when comparing the ALNM and non-ALNM groups as well as the training and testing cohorts (P > 0.05). AUC values for the DWI, DCE, and combined models in the training cohort were 0.793, 0.774, and 0.864, respectively, with corresponding values of 0.728, 0.760, and 0.859 in the testing cohort. The predictive efficacy of the DWI and combined models was found to differ significantly according to the DeLong test, as did the predictive efficacy of the DCE and combined models in the training groups (P < 0.05), while no other significant differences were noted in model performance (P > 0.05). IDI results indicated that the combined model offered predictive power levels that were 13.5% (P < 0.05) and 10.2% (P < 0.05) higher than those for the respective DWI and DCE models. In a decision curve analysis, the combined model offered a net clinical benefit over the DCE model. The combined dual-sequence MRI-based radiomics model constructed herein and the supporting interpretability analyses can aid in the prediction of the ALNM status of IBC patients, helping to guide clinical decision-making in these cases.

To evaluate image quality and diagnostic interchangeability of synth short-tau inversion recovery (STIR) generated by deep learning in comparison with standard STIR. This prospective study recruited participants between July 2023 and August 2023. Participants were scanned with T1WI and T2WI, then generated Synth-STIR. Signal-to-noise ratios (SNR), contrast-to-noise ratios (CNR) were calculated for quantitative evaluation. Four independent, blinded radiologists performed subjective quality and lesion characteristic assessment. Wilcoxon tests were used to assess the differences in SNR, CNR, and subjective image quality. Various diagnostic findings pertinent to the spine were tested for interchangeability using the individual equivalence index (IEI). Inter-reader and intra-reader agreement and concordance were computed, and McNemar tests were performed for comprehensive evaluation. One hundred ninety-nine participants (106 male patients, mean age 46.8 ± 16.9 years) were included. Compared to standard-STIR, Synth-STIR reduces sequence scanning time by approximately 180 s, has significantly higher SNR and CNR (p < 0.001). For artifacts, noise, sharpness, and diagnostic confidence, all readers agreed that Synth-STIR was significantly better than standard-STIR (all p < 0.001). In addition, the IEI was less than 1.61%. Kappa and Kendall showed a moderate to excellent agreement in the range of 0.52-0.97. There was no significant difference in the frequencies of the major features as reported with standard-STIR and Synth-STIR (p = 0.211-1). Synth-STIR shows significantly higher SNR and CNR, and is diagnostically interchangeable with standard-STIR with a substantial overall reduction in the imaging time, thereby improving efficiency without sacrificing diagnostic value. Question Can generating STIR improve image quality while reducing spine MRI acquisition time in order to increase clinical spine MRI throughput? Findings With reduced acquisition time, Synth-STIR has significantly higher SNR and CNR than standard-STIR and can be interchangeably diagnosed with standard-STIR in detecting spinal abnormalities. Clinical relevance Our Synth-STIR provides the same high-quality images for clinical diagnosis as standard-STIR, while reducing scanning time for spine MRI protocols. Increase clinical spine MRI throughput.

Hematoma expansion (HE), including intraventricular hemorrhage (IVH) growth, significantly affects outcomes in patients with intracerebral hemorrhage (ICH). This study aimed to develop, validate, and interpret a deep learning model, HENet, for predicting three definitions of HE. Using CT scans and clinical data from 718 ICH patients across three hospitals, the multicenter retrospective study focused on revised hematoma expansion (RHE) definitions 1 and 2, and conventional HE (CHE). HENet's performance was compared with 2D models and physician predictions using two external validation sets. Results showed that HENet achieved high AUC values for RHE1, RHE2, and CHE predictions, surpassing physicians' predictions and 2D models in net reclassification index and integrated discrimination index for RHE1 and RHE2 outcomes. The Grad-CAM technique provided visual insights into the model's decision-making process. These findings suggest that integrating HENet into clinical practice could improve prediction accuracy and patient outcomes in ICH cases.

The Transformer architecture has demonstrated remarkable results in 3D medical image segmentation due to its capability of modeling global relationships. However, it poses a significant computational burden when processing high-dimensional medical images. Mamba, as a State Space Model (SSM), has recently emerged as a notable approach for modeling long-range dependencies in sequential data. Although a substantial amount of Mamba-based research has focused on natural language and 2D image processing, few studies explore the capability of Mamba on 3D medical images. In this paper, we propose SegMamba-V2, a novel 3D medical image segmentation model, to effectively capture long-range dependencies within whole-volume features at each scale. To achieve this goal, we first devise a hierarchical scale downsampling strategy to enhance the receptive field and mitigate information loss during downsampling. Furthermore, we design a novel tri-orientated spatial Mamba block that extends the global dependency modeling process from one plane to three orthogonal planes to improve feature representation capability. Moreover, we collect and annotate a large-scale dataset (named CRC-2000) with fine-grained categories to facilitate benchmarking evaluation in 3D colorectal cancer (CRC) segmentation. We evaluate the effectiveness of our SegMamba-V2 on CRC-2000 and three other large-scale 3D medical image segmentation datasets, covering various modalities, organs, and segmentation targets. Experimental results demonstrate that our Segmamba-V2 outperforms state-of-the-art methods by a significant margin, which indicates the universality and effectiveness of the proposed model on 3D medical image segmentation tasks. The code for SegMamba-V2 is publicly available at: https://github.com/ge-xing/SegMamba-V2.

The systematic literature review was performed on the use of artificial intelligence (AI) algorithms in nonsmall cell lung cancer (NSCLC) prognostication. Studies were evaluated for the type of input data (histology and whether CT, PET, and MRI were used), cancer therapy intervention, prognosis performance, and comparisons to clinical prognosis systems such as TNM staging. Further comparisons were drawn between different types of AI, such as machine learning (ML) and deep learning (DL). Syntheses of therapeutic interventions and algorithm input modalities were performed for comparison purposes. The review adheres to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA). The initial database identified 3880 results, which were reduced to 513 after the automatic screening, and 309 after the exclusion criteria. The prognostic performance of AI for NSCLC has been investigated using histology and genetic data, and CT, PET, and MR imaging for surgery, immunotherapy, and radiation therapy patients with and without chemotherapy. Studies per therapy intervention were 13 for immunotherapy, 10 for radiotherapy, 14 for surgery, and 34 for other, multiple, or no specific therapy. The results of this systematic review demonstrate that AI-based prognostication methods consistently present higher prognostic performance for NSCLC, especially when directly compared with traditional prognostication techniques such as TNM staging. The use of DL outperforms ML-based prognostication techniques. DL-based prognostication demonstrates the potential for personalized precision cancer therapy as a supplementary decision-making tool. Before it is fully utilized in clinical practice, it is recommended that it be thoroughly validated through well-designed clinical trials.