This research aims to enhance X-ray lung segmentation by addressing boundary distortions in anatomical structures, with the objective of refining segmentation boundaries and improving the morphological shape of segmented objects. The proposed approach combines the K-segment principal curve with Gaussian Process Regression (GPR) to refine segmentation boundaries, evaluated using lung X-ray datasets at varying resolutions. Several state-of-the-art models, including U-Net, SegNet, and TransUnet, were also assessed for comparison. The model employed a custom kernel for GPR, combining Radial Basis Function (RBF) with a cosine similarity term. The effectiveness of the model was evaluated using metrics such as the Dice Coefficient (DC) and Jaccard Index (JC) for segmentation accuracy, along with Average Symmetric Surface Distance (ASSD) and Hausdorff Distance (HD) for boundary alignment. The proposed method achieved superior segmentation performance, particularly at the highest resolution (1024x1024 pixels), with a DC of 95.7% for the left lung and 94.1% for the right lung. Among the different models, TransUnet outperformed others across both the semantic segmentation and boundary refinement stages, showing significant improvements in DC, JC, ASSD, and HD. The results indicate that the proposed boundary refinement approach effectively improves the segmentation quality of lung X-rays, excelling in refining well-defined structures and achieving superior boundary alignment, showcasing its potential for clinical applications. However, limitations exist when dealing with irregular or unpredictable shapes, suggesting areas for future enhancement.

Volumetric rendering of Computed Tomography (CT) scans is crucial for visualizing complex 3D anatomical structures in medical imaging. Current high-fidelity approaches, especially neural rendering techniques, require time-consuming per-scene optimization, limiting clinical applicability due to computational demands and poor generalizability. We propose Render-FM, a novel foundation model for direct, real-time volumetric rendering of CT scans. Render-FM employs an encoder-decoder architecture that directly regresses 6D Gaussian Splatting (6DGS) parameters from CT volumes, eliminating per-scan optimization through large-scale pre-training on diverse medical data. By integrating robust feature extraction with the expressive power of 6DGS, our approach efficiently generates high-quality, real-time interactive 3D visualizations across diverse clinical CT data. Experiments demonstrate that Render-FM achieves visual fidelity comparable or superior to specialized per-scan methods while drastically reducing preparation time from nearly an hour to seconds for a single inference step. This advancement enables seamless integration into real-time surgical planning and diagnostic workflows. The project page is: https://gaozhongpai.github.io/renderfm/.

Current brain tumor segmentation methods often utilize a U-Net architecture based on efficient convolutional neural networks. While effective, these architectures primarily model local dependencies, lacking the ability to capture global interactions like pure Transformer. However, using pure Transformer directly causes the network to lose local feature information. To address this limitation, we propose the Generative Adversarial Dilated Attention Convolutional Transformer(GDacFormer). GDacFormer enhances interactions between tumor regions while balancing global and local information through the integration of adversarial learning with an improved transformer module. Specifically, GDacFormer leverages a generative adversarial segmentation network to learn richer and more detailed features. It integrates a novel Transformer module, DacFormer, featuring multi-scale dilated attention and a next convolution block. This module, embedded within the generator, aggregates semantic multi-scale information, efficiently reduces the redundancy in the self-attention mechanism, and enhances local feature representations, thus refining the brain tumor segmentation results. GDacFormer achieves Dice values for whole tumor, core tumor, and enhancing tumor segmentation of 90.9%/90.8%/93.7%, 84.6%/85.7%/93.5%, and 77.9%/79.3%/86.3% on BraTS2019-2021 datasets. Extensive evaluations demonstrate the effectiveness and competitiveness of GDacFormer. The code for GDacFormer will be made publicly available at https://github.com/MuqinZ/GDacFormer.

To evaluate the variability of fully automated airway quantitative CT (QCT) measures caused by different kernels and the effect of kernel conversion. This retrospective study included 96 patients who underwent non-enhanced chest CT at two centers. CT scans were reconstructed using four kernels (medium soft, medium sharp, sharp, very sharp) from three vendors. Kernel conversion targeting the medium soft kernel as reference was applied to sharp kernel images. Fully automated airway quantification was performed before and after conversion. The effects of kernel type and conversion on airway quantification were evaluated using analysis of variance, paired t-tests, and concordance correlation coefficient (CCC). Airway QCT measures (e.g., Pi10, wall thickness, wall area percentage, lumen diameter) decreased with sharper kernels (all, p < 0.001), with varying degrees of variability across variables and vendors. Kernel conversion substantially reduced variability between medium soft and sharp kernel images for vendors A (pooled CCC: 0.59 vs. 0.92) and B (0.40 vs. 0.91) and lung-dedicated sharp kernels of vendor C (0.26 vs. 0.71). However, it was ineffective for non-lung-dedicated sharp kernels of vendor C (0.81 vs. 0.43) and showed limited improvement in variability of QCT measures at the subsegmental level. Consistent airway segmentation and identical anatomic labeling improved subsegmental airway variability in theoretical tests. Deep learning-based kernel conversion reduced the measurement variability of airway QCT across various kernels and vendors but was less effective for non-lung-dedicated kernels and subsegmental airways. Consistent airway segmentation and precise anatomic labeling can further enhance reproducibility for reliable automated quantification. Question How do different CT reconstruction kernels affect the measurement variability of automated airway measurements, and can deep learning-based kernel conversion reduce this variability? Findings Kernel conversion improved measurement consistency across vendors for lung-dedicated kernels, but showed limited effectiveness for non-lung-dedicated kernels and subsegmental airways. Clinical relevance Understanding kernel-related variability in airway quantification and mitigating it through deep learning enables standardized analysis, but further refinements are needed for robust airway segmentation, particularly for improving measurement variability in subsegmental airways and specific kernels.

Synthetic CT (sCT) generation from cone-beam CT (CBCT) must maintain stable performance and allow for accurate dose calculation across all treatment fractions to effectively support adaptive proton therapy. This study evaluated a 3D deep-learning (DL) network for sCT generation for prostate cancer patients over the full treatment course. Patient data from 25/6 prostate cancer patients were used to train/test the DL network. Patients in the test set had a planning CT, 39 CBCT images, and at least one repeat CT (reCT) used for replanning. The generated sCT images were compared to fan-beam planning and reCT images in terms of i) CT number accuracy and stability within spherical regions-of-interest (ROIs) in the bladder, prostate, and femoral heads, ii) proton range calculation accuracy through single-spot plans, and iii) dose trends in target coverage over the treatment course (one patient). The sCT images demonstrated image quality comparable to CT, while preserving the CBCT anatomy. The mean CT numbers on the sCT and CT images were comparable, e.g. for the prostate ROI they ranged from 29 HU to 59 HU for sCT, and from 36 HU to 50 HU for CT. The largest median proton range difference was 1.9 mm. Proton dose calculations showed excellent target coverage (V95%≥99.6 %) for the high-dose target. The DL network effectively generated high-quality sCT images with CT numbers, proton range, and dose characteristics comparable to fan-beam CT. Its robustness against intra-patient variations makes it a feasible tool for adaptive proton therapy.

Accurate prediction of depressive symptoms in healthy individuals can enable early intervention and reduce both individual and societal costs. This study aimed to develop predictive models for depression in young adults using machine learning (ML) techniques and longitudinal data from the Beck Depression Inventory, structural MRI (sMRI), and resting-state functional MRI (rs-fMRI). Feature selection methods, including the least absolute shrinkage and selection operator (LASSO), Boruta, and VSURF, were applied to identify MRI features associated with depression. Support vector machine and random forest algorithms were then used to construct prediction models. Eight MRI features were identified as predictive of depression, including brain regions in the Orbital Gyrus, Superior Frontal Gyrus, Middle Frontal Gyrus, Parahippocampal Gyrus, Cingulate Gyrus, and Inferior Parietal Lobule. The overlaps and the differences between selected features and brain regions with significant between-group differences in t-tests suggest that ML provides a unique perspective on the neural changes associated with depression. Six pairs of prediction models demonstrated varying performance, with accuracies ranging from 0.68 to 0.85 and areas under the curve (AUC) ranging from 0.57 to 0.81. The best-performing model achieved an accuracy of 0.85 and an AUC of 0.80, highlighting the potential of combining sMRI and rs-fMRI features with ML for early depression detection while revealing the potential of overfitting in small-sample and high-dimensional settings. This study necessitates further research to (1) replicate findings in independent larger datasets to address potential overfitting and (2) utilize different advanced ML techniques and multimodal data fusion to improve model performance.

High-resolution magnetic resonance angiography (~ 50 μm³ MRA) data plays a critical role in the accurate diagnosis of various vascular disorders. However, it is very challenging to acquire, and it is susceptible to artifacts and noise which limits its ability to visualize smaller blood vessels and necessitates substantial noise reduction measures. Among many techniques, the BM4D filter is a state-of-the-art denoising technique but comes with high computational cost, particularly for high-resolution 3D MRA data. In this research, five different optimized convolutional neural networks were utilized to denoise contrast-enhanced UTE-MRA data using a supervised learning approach. Since noise-free MRA data is challenging to acquire, the denoised image using BM4D filter was used as ground truth and this research mainly focused on reducing computational cost and inference time for denoising high-resolution UTE-MRA data. All five models were able to generate nearly similar denoised data compared to the ground truth with different computational footprints. Among all, the nested-UNet model generated almost similar images with the ground truth and achieved SSIM, PSNR, and MSE of 0.998, 46.12, and 3.38e-5 with 3× faster inference time than the BM4D filter. In addition, most optimized models like UNet and attention-UNet models generated nearly similar images with nested-UNet but 8.8× and 7.1× faster than the BM4D filter. In conclusion, using highly optimized networks, we have shown the possibility of denoising high-resolution UTE-MRA data with significantly shorter inference time, even with limited datasets from animal models. This can potentially make high-resolution 3D UTE-MRA data to be less computationally burdensome.

As machine learning (ML) continuously develops in cancer diagnosis and treatment, some researchers have attempted to predict the expression of programmed death ligand-1 (PD-L1) in non-small cell lung cancer (NSCLC) by ML. However, there is a lack of systematic evidence on the effectiveness of ML. We conducted a thorough search across Embase, PubMed, the Cochrane Library, and Web of Science from inception to December 14th, 2023.A systematic review and meta-analysis was conducted to assess the value of ML for predicting PD-L1 expression in NSCLC. Totally 30 studies with 12,898 NSCLC patients were included. The thresholds of PD-L1 expression level were < 1%, 1-49%, and ≥ 50%. In the validation set, in the binary classification for PD-L1 ≥ 1%, the pooled C-index was 0.646 (95%CI: 0.587-0.705), 0.799 (95%CI: 0.782-0.817), 0.806 (95%CI: 0.753-0.858), and 0.800 (95%CI: 0.717-0.883), respectively, for the clinical feature-, radiomics-, radiomics + clinical feature-, and pathomics-based ML models; in the binary classification for PD-L1 ≥ 50%, the pooled C-index was 0.649 (95%CI: 0.553-0.744), 0.771 (95%CI: 0.728-0.814), and 0.826 (95%CI: 0.783-0.869), respectively, for the clinical feature-, radiomics-, and radiomics + clinical feature-based ML models. At present, radiomics- or pathomics-based ML methods are applied for the prediction of PD-L1 expression in NSCLC, which both achieve satisfactory accuracy. In particular, the radiomics-based ML method seems to have wider clinical applicability as a non-invasive diagnostic tool. Both radiomics and pathomics serve as processing methods for medical images. In the future, we expect to develop medical image-based DL methods for intelligently predicting PD-L1 expression.

Background and Purpose Assessment of brain lesions on MRI is crucial for research in multiple sclerosis (MS). Manual segmentation is time consuming and inconsistent. We aimed to develop an automated MS lesion segmentation algorithm for T2-weighted fluid-attenuated inversion recovery (FLAIR) MRI. Methods We developed FLAIR Lesion Analysis in Multiple Sclerosis (FLAMeS), a deep learning-based MS lesion segmentation algorithm based on the nnU-Net 3D full-resolution U-Net and trained on 668 FLAIR 1.5 and 3 tesla scans from persons with MS. FLAMeS was evaluated on three external datasets: MSSEG-2 (n=14), MSLesSeg (n=51), and a clinical cohort (n=10), and compared to SAMSEG, LST-LPA, and LST-AI. Performance was assessed qualitatively by two blinded experts and quantitatively by comparing automated and ground truth lesion masks using standard segmentation metrics. Results In a blinded qualitative review of 20 scans, both raters selected FLAMeS as the most accurate segmentation in 15 cases, with one rater favoring FLAMeS in two additional cases. Across all testing datasets, FLAMeS achieved a mean Dice score of 0.74, a true positive rate of 0.84, and an F1 score of 0.78, consistently outperforming the benchmark methods. For other metrics, including positive predictive value, relative volume difference, and false positive rate, FLAMeS performed similarly or better than benchmark methods. Most lesions missed by FLAMeS were smaller than 10 mm3, whereas the benchmark methods missed larger lesions in addition to smaller ones. Conclusions FLAMeS is an accurate, robust method for MS lesion segmentation that outperforms other publicly available methods.

CT imaging provides essential information about internal anatomy; however, conventional CT imaging delivers radiation doses that can become problematic for patients requiring repeated imaging, highlighting the need for dose-reduction techniques. This study aims to reduce radiation doses without compromising image quality. We propose an approach that combines Neural Attenuation Fields (NAF) with an active learning strategy to better optimize CT reconstructions given a limited number of X-ray projections. Our method uses a secondary neural network to predict the Peak Signal-to-Noise Ratio (PSNR) of 2D projections generated by NAF from a range of angles in the operational range of the CT scanner. This prediction serves as a guide for the active learning process in choosing the most informative projections. In contrast to conventional techniques that acquire all X-ray projections in a single session, our technique iteratively acquires projections. The iterative process improves reconstruction quality, reduces the number of required projections, and decreases patient radiation exposure. We tested our methodology on spinal imaging using a limited subset of the VerSe 2020 dataset. We compare image quality metrics (PSNR3D, SSIM3D, and PSNR2D) to the baseline method and find significant improvements. Our method achieves the same quality with 36 projections as the baseline method achieves with 60. Our findings demonstrate that our approach achieves high-quality 3D CT reconstructions from sparse data, producing clearer and more detailed images of anatomical structures. This work lays the groundwork for advanced imaging techniques, paving the way for safer and more efficient medical imaging procedures.