Addressing the critical challenge of precise boundary delineation in medical image segmentation, we introduce DPGNet, an adaptive deep learning model engineered to emulate expert perception of intricate anatomical edges. Our key innovations drive its superior performance and clinical utility, encompassing: 1) a three-stage progressive refinement strategy that establishes global context, performs hierarchical feature enhancement, and precisely delineates local boundaries; 2) a novel Edge Difference Attention (EDA) module that implicitly learns and quantifies boundary uncertainties without requiring explicit ground truth supervision; and 3) a lightweight, transformer-based architecture ensuring an exceptional balance between performance and computational efficiency. Extensive experiments across diverse and challenging medical image datasets demonstrate DPGNet's consistent superiority over state-of-the-art methods, notably achieving this with significantly lower computational overhead (25.51 M parameters). Its exceptional boundary refinement is rigorously validated through comprehensive metrics (Boundary-IoU, HD95) and confirmed by rigorous clinical expert evaluations. Crucially, DPGNet generates an explicit uncertainty boundary map, providing clinicians with actionable insights to identify ambiguous regions, thereby enhancing diagnostic precision and facilitating more accurate clinical segmentation outcomes. Our code is available at: https://github.fangnengwuyou/DPGNet.

The thickness of the diaphragm serves as a crucial biometric indicator, particularly in assessing rehabilitation and respiratory dysfunction. However, measuring diaphragm thickness from ultrasound images mainly depends on manual delineation of the fascia, which is subjective, time-consuming, and sensitive to the inherent speckle noise. In this study, we introduce an edge-aware diffusion segmentation model (ESADiff), which incorporates prior structural knowledge of the fascia to improve the accuracy and reliability of diaphragm thickness measurements in ultrasound imaging. We first apply a diffusion model, guided by annotations, to learn the image features while preserving edge details through an iterative denoising process. Specifically, we design an anisotropic edge-sensitive annotation refinement module that corrects inaccurate labels by integrating Hessian geometric priors with a backtracking shortest-path connection algorithm, further enhancing model accuracy. Moreover, a curvature-aware deformable convolution and edge-prior ranking loss function are proposed to leverage the shape prior knowledge of the fascia, allowing the model to selectively focus on relevant linear structures while mitigating the influence of noise on feature extraction. We evaluated the proposed model on an in-house diaphragm ultrasound dataset, a public calf muscle dataset, and an internal tongue muscle dataset to demonstrate robust generalization. Extensive experimental results demonstrate that our method achieves finer fascia segmentation and significantly improves the accuracy of thickness measurements compared to other state-of-the-art techniques, highlighting its potential for clinical applications.

Extrapolation plays a critical role in machine/deep learning (ML/DL), enabling models to predict data points beyond their training constraints, particularly useful in scenarios deviating significantly from training conditions. This article addresses the limitations of current convolutional neural networks (CNNs) in extrapolation tasks within image restoration and compressed sensing (CS). While CNNs show potential in tasks such as image outpainting and CS, traditional convolutions are limited by their reliance on interpolation, failing to fully capture the dependencies needed for predicting values outside the known data. This work proposes an extrapolation convolution (EC) framework that models missing data prediction as an extrapolation problem using linear prediction within DL architectures. The approach is applied in two domains: first, image outpainting, where EC in encoder-decoder (EnDec) networks replaces conventional interpolation methods to reduce artifacts and enhance fine detail representation; second, Fourier-based CS-magnetic resonance imaging (CS-MRI), where it predicts high-frequency signal values from undersampled measurements in the frequency domain, improving reconstruction quality and preserving subtle structural details at high acceleration factors. Comparative experiments demonstrate that the proposed EC-DecNet and FDRN outperform traditional CNN-based models, achieving high-quality image reconstruction with finer details, as shown by improved peak signal-to-noise ratio (PSNR), structural similarity index (SSIM), and kernel inception distance (KID)/Frechet inception distance (FID) scores. Ablation studies and analysis highlight the effectiveness of larger kernel sizes and multilevel semi-supervised learning in FDRN for enhancing extrapolation accuracy in the frequency domain.

Accurately imaging the spatial distribution of longitudinal speed of sound (SoS) has a profound impact on image quality and the diagnostic value of ultrasound. Knowledge of SoS distribution allows effective aberration correction to improve image quality. SoS imaging also provides a new contrast mechanism to facilitate disease diagnosis. However, SoS imaging is challenging in the pulse-echo mode. Deep learning (DL) is a promising approach for pulse-echo SoS imaging, which may yield more accurate results than pure physics-based approaches. Herein, we developed a robust DL approach for SoS imaging that learns the nonlinear mapping between measured time shifts and the underlying SoS without subjecting to the constraints of a specific forward model. Various strategies were adopted to enhance model performance. Time-shift maps were computed by adopting a common mid-angle configuration from the non-DL literature, normalizing complex beamformed ultrasound data, and accounting for depth-dependent frequency when converting phase shifts to time shifts. The structural similarity index measure (SSIM) was incorporated into the loss function to learn the global structure for SoS imaging. A two-stage training strategy was employed, leveraging computationally efficient ray-tracing synthesis for extensive pretraining, and more realistic but computationally expensive full-wave simulations for fine-tuning. Using these combined strategies, our model was shown to be robust and generalizable across different conditions. The simulation-trained model successfully reconstructed the SoS maps of phantoms using experimental data. Compared with the physics-based inversion approach, our method improved reconstruction accuracy and contrast-to-noise ratio in phantom experiments. These results demonstrated the accuracy and robustness of our approach.

Cardiovascular ageing is a progressive loss of physiological reserve, modified by environmental and genetic risk factors, that contributes to multi-morbidity due to accumulated damage across diverse cell types, tissues, and organs. Obesity is implicated in premature ageing, but the effect of body fat distribution in humans is unknown. This study determined the influence of sex-dependent fat phenotypes on human cardiovascular ageing. Data from 21 241 participants in the UK Biobank were analysed. Machine learning was used to predict cardiovascular age from 126 image-derived traits of vascular function, cardiac motion, and myocardial fibrosis. An age-delta was calculated as the difference between predicted age and chronological age. The volume and distribution of body fat was assessed from whole-body imaging. The association between fat phenotypes and cardiovascular age-delta was assessed using multivariable linear regression with age and sex as co-covariates, reporting β coefficients with 95% confidence intervals (CI). Two-sample Mendelian randomization was used to assess causal associations. Visceral adipose tissue volume [β = 0.656, (95% CI, .537-.775), P < .0001], muscle adipose tissue infiltration [β = 0.183, (95% CI, .122-.244), P = .0003], and liver fat fraction [β = 1.066, (95% CI .835-1.298), P < .0001] were the strongest predictors of increased cardiovascular age-delta for both sexes. Abdominal subcutaneous adipose tissue volume [β = 0.432, (95% CI, .269-.596), P < .0001] and android fat mass [β = 0.983, (95% CI, .64-1.326), P < .0001] were each associated with increased age-delta only in males. Genetically predicted gynoid fat showed an association with decreased age-delta. Shared and sex-specific patterns of body fat are associated with both protective and harmful changes in cardiovascular ageing, highlighting adipose tissue distribution and function as a key target for interventions to extend healthy lifespan.

Breast cancer is the most common cancer and the leading cause of cancer-related deaths in women. Texture analysis provides crucial prognostic information about many types of cancer, including breast cancer. The aim was to examine the relationship between texture features (TFs) of 2-deoxy-2[¹⁸F] fluoro-D-glucose positron emission tomography (PET)/computed tomography and disease progression in patients with invasive breast cancer. TFs of the primary malignant lesion were extracted from PET images of 112 patients. TFs that showed significant differences between patients who achieved one-, three-, and five-year progression-free survival (PFS) and those who did not were selected and subjected to the least absolute shrinkage and selection operator regression method to reduce features and prevent overfitting. Machine learning (ML) was used to predict PFS using TFs and selected clinicopathological parameters. In models using only TFs, random forest predicted one-, three-, and five-year PFS with area under the curve (AUC) values of 0.730, 0.758, and 0.797, respectively. Naive Bayes predicted one-, three-, and five-year PFS with AUC values of 0.857, 0.804, and 0.843, respectively. The neural network predicted one-, three-, and five-year PFS with AUC values of 0.782, 0.828, and 0.780, respectively. These findings indicated increased AUC values when the models combined TFs with clinicopathological parameters. The lowest AUC values of the models combining TFs and clinicopathological parameters when predicting one-year, three-year, and five-year PFS were 0.867, 0.898, and 0.867, respectively. ML models incorporating PET-derived TFs and clinical parameters may assist in predicting progression during the pre-treatment period in patients with invasive breast carcinoma.

To differentiate interstitial lung diseases (ILDs) with fibrotic and inflammatory patterns using high-resolution computed tomography (HRCT) and a radiomics-based artificial intelligence (AI) pipeline. This single-center study included 84 patients: 50 with idiopathic pulmonary fibrosis (IPF)-representative of fibrotic pattern-and 34 with cellular non-specific interstitial pneumonia (NSIP) secondary to connective tissue disease (CTD)-as an example of mostly inflammatory pattern. For a secondary objective, we analyzed 50 additional patients with COVID-19 pneumonia. We performed semi-automatic segmentation of ILD regions using a deep learning model followed by manual review. From each segmented region, 103 radiomic features were extracted. Classification was performed using an XGBoost model with 1000 bootstrap repetitions and SHapley Additive exPlanations (SHAP) were applied to identify the most predictive features. The model accurately distinguished a fibrotic ILD pattern from an inflammatory ILD one, achieving an average test set accuracy of 0.91 and AUROC of 0.98. The classification was driven by radiomic features capturing differences in lung morphology, intensity distribution, and textural heterogeneity between the two disease patterns. In differentiating cellular NSIP from COVID-19, the model achieved an average accuracy of 0.89. Inflammatory ILDs exhibited more uniform imaging patterns compared to the greater variability typically observed in viral pneumonia. Radiomics combined with explainable AI offers promising diagnostic support in distinguishing fibrotic from inflammatory ILD patterns and differentiating inflammatory ILDs from viral pneumonias. This approach could enhance diagnostic precision and provide quantitative support for personalized ILD management.

To compare PET-derived metrics between digital and analogue PET/CT in hyperparathyroidism, and to assess whether machine learning (ML) applied to quantitative PET parameters can distinguish parathyroid adenoma (PA) from hyperplasia (PH). From an initial multi-centre cohort of 179 patients, 86 were included, comprising 89 PET-positive lesions confirmed histologically (74 PA, 15 PH). Quantitative PET parameters-maximum standardised uptake value (SUVmax), metabolic tumour volume (MTV), target-to-background ratio (TBR), and maximum diameter-along with serum PTH and calcium levels, were compared between digital and analogue PET scanners using the Mann-Whitney U test. Receiver operating characteristic (ROC) analysis identified optimal threshold values. ML models (LASSO, decision tree, Gaussian naïve Bayes) were trained on harmonised quantitative features to distinguish PA from PH. Digital PET detected significantly smaller lesions than analogue PET, in both metabolic volume (1.32 ± 1.39 vs. 2.36 ± 2.01 cc; p < 0.001) and maximum diameter (8.35 ± 4.32 vs. 11.87 ± 5.29 mm; p < 0.001). PA lesions showed significantly higher SUVmax and TBR compared to PH (SUVmax: 8.58 ± 3.70 vs. 5.27 ± 2.34; TBR: 14.67 ± 6.99 vs. 8.82 ± 5.90; both p < 0.001). The optimal thresholds for identifying PA were SUVmax > 5.89 and TBR > 11.5. The best ML model (LASSO) achieved an AUC of 0.811, with 79.7% accuracy and balanced sensitivity and specificity. Digital PET outperforms analogue system in detecting small parathyroid lesions. Additionally, ML analysis of PET-derived metrics and PTH may support non-invasive distinction between adenoma and hyperplasia.

The aim of this study was to evaluate the feasibility and reproducibility of a novel deep learning (DL)-based coronary plaque quantification tool with automatic case preparation in patients undergoing ultra-high resolution (UHR) photon-counting detector CT coronary angiography (CCTA), and to assess the influence of temporal resolution on plaque quantification. In this retrospective single-center study, 45 patients undergoing clinically indicated UHR CCTA were included. In each scan, 2 image data sets were reconstructed: one in the dual-source mode with 66 ms temporal resolution and one simulating a single-source mode with 125 ms temporal resolution. A novel, DL-based algorithm for fully automated coronary segmentation and intensity-based plaque quantification was applied to both data sets in each patient. Plaque volume quantification was performed at the vessel-level for the entire left anterior descending artery (LAD), left circumflex artery (CX), and right coronary artery (RCA), as well as at the lesion-level for the largest coronary plaque in each vessel. Diameter stenosis grade was quantified for the coronary lesion with the greatest longitudinal extent in each vessel. To assess reproducibility, the algorithm was rerun 3 times in 10 randomly selected patients, and all outputs were visually reviewed and confirmed by an expert reader. Paired Wilcoxon signed-rank tests with Benjamini-Hochberg correction were used for statistical comparisons. One hundred nineteen out of 135 (88.1%) coronary arteries showed atherosclerotic plaques and were included in the analysis. In the reproducibility analysis, repeated runs of the algorithm yielded identical results across all plaque and lumen measurements (P > 0.999). All outputs were confirmed to be anatomically correct, visually consistent, and did not require manual correction. At the vessel level, total plaque volumes were higher in the 125 ms reconstructions compared with the 66 ms reconstructions in 28 of 45 patients (62%), with both calcified and noncalcified plaque volumes being higher in 32 (71%) and 28 (62%) patients, respectively. Total plaque volumes in the LAD, CX, and RCA were significantly higher in the 125 ms reconstructions (681.3 vs. 647.8  mm3, P < 0.05). At the lesion level, total plaque volumes were higher in the 125 ms reconstructions in 44 of 45 patients (98%; 447.3 vs. 414.9  mm3, P < 0.001), with both calcified and noncalcified plaque volumes being higher in 42 of 45 patients (93%). The median diameter stenosis grades for all vessels were significantly higher in the 125 ms reconstructions (35.4% vs. 28.1%, P < 0.01). This study evaluated a novel DL-based tool with automatic case preparation for quantitative coronary plaque in UHR CCTA data sets. The algorithm was technically robust and reproducible, delivering anatomically consistent outputs not requiring manual correction. Reconstructions with lower temporal resolution (125 ms) systematically overestimated plaque burden compared with higher temporal resolution (66 ms), underscoring that protocol standardization is essential for reliable DL-based plaque quantification.

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