The automated segmentation of computed tomography (CT) images has made their opportunistic use more feasible, yet, the association of muscle area and density from multiple anatomical regions with functional outcomes and mortality risk in older adults has not been fully explored. We aimed to determine if muscle area and density at the L1 and L3 vertebra and right and left proximal thigh were similarly related to functional outcomes and 10-year mortality risk. Men from the Osteoporotic Fractures in Men (MrOS) study who had CT images, measures of grip strength, 6 m walking speed, and leg power (Nottingham Power Rig) at the baseline visit were included in the analyses (n = 3290, 73.7 ± 5.8 years). CT images were automatically segmented to derive muscle area and muscle density. Deaths were centrally adjudicated over a 10-year follow-up. Linear regression and proportional hazards were used to model relationships of CT muscle metrics with functional outcomes and mortality, respectively, while adjusting for covariates. Muscle area and density were positively related to functional outcomes regardless of anatomical region, with the most variance explained in leg power (adjusted R² = 0.40-0.46), followed by grip strength (adjusted R² = 0.25-0.29) and walking speed (adjusted R² = 0.18-0.20). A one-unit SD increase in muscle area and density was associated with a 5%-13% and 8%-21% decrease in the risk of all-cause mortality, respectively, with the strongest associations observed at the right and left thigh. Automated measures of CT muscle area and density are related to functional outcomes and risk of mortality in older men, regardless of CT anatomical region.

Lung magnetic resonance imaging (MRI) with ultrashort echo-time (UTE) represents a recent breakthrough in lung structure imaging, providing image resolution and quality comparable to computed tomography (CT). Due to the absence of ionising radiation, MRI is often preferred over CT in paediatric diseases such as cystic fibrosis (CF), one of the most common genetic disorders in Caucasians. To assess structural lung damage in CF imaging, CT scoring systems provide valuable quantitative insights for disease diagnosis and progression. However, few quantitative scoring systems are available in structural lung MRI (e.g., UTE-MRI). To provide fast and accurate quantification in lung MRI, we investigated the feasibility of novel Artificial intelligence-assisted Pixel-level Lung (APL) scoring for CF. APL scoring consists of 5 stages, including 1) image loading, 2) AI lung segmentation, 3) lung-bounded slice sampling, 4) pixel-level annotation, and 5) quantification and reporting. The results shows that our APL scoring took 8.2 minutes per subject, which was more than twice as fast as the previous grid-level scoring. Additionally, our pixel-level scoring was statistically more accurate (p=0.021), while strongly correlating with grid-level scoring (R=0.973, p=5.85e-9). This tool has great potential to streamline the workflow of UTE lung MRI in clinical settings, and be extended to other structural lung MRI sequences (e.g., BLADE MRI), and for other lung diseases (e.g., bronchopulmonary dysplasia).

Artificial intelligence (AI) has enabled accurate and fast plaque quantification from coronary computed tomography angiography (CCTA). However, AI detects any coronary plaque in up to 97% of patients. To avoid overdiagnosis, a plaque burden safety cut-off for future coronary events is needed. Percent atheroma volume (PAV) was quantified with AI-guided quantitative computed tomography in a blinded fashion. Safety cut-off derivation was performed in the Turku CCTA registry (Finland), and pre-defined as ≥90% sensitivity for acute coronary syndrome (ACS). External validation was performed in the Amsterdam CCTA registry (the Netherlands). In the derivation cohort, 100/2271 (4.4%) patients experienced ACS (median follow-up 6.9 years). A threshold of PAV ≥ 2.6% was derived with 90.0% sensitivity and negative predictive value (NPV) of 99.0%. In the validation cohort 27/568 (4.8%) experienced ACS (median follow-up 6.7 years) with PAV ≥ 2.6% showing 92.6% sensitivity and 99.0% NPV for ACS. In the derivation cohort, 45.2% of patients had PAV < 2.6 vs. 4.3% with PAV 0% (no plaque) (P < 0.001) (validation cohort: 34.3% PAV < 2.6 vs. 2.6% PAV 0%; P < 0.001). Patients with PAV ≥ 2.6% had higher adjusted ACS rates in the derivation [Hazard ratio (HR) 4.65, 95% confidence interval (CI) 2.33-9.28, P < 0.001] and validation cohort (HR 7.31, 95% CI 1.62-33.08, P = 0.010), respectively. This study suggests that PAV up to 2.6% quantified by AI is associated with low-ACS risk in two independent patient cohorts. This cut-off may be helpful for clinical application of AI-guided CCTA analysis, which detects any plaque in up to 96-97% of patients.

Ultrasound (US) is widely accessible and radiation-free but has a steep learning curve due to its dynamic nature and non-standard imaging planes. Additionally, the constant need to shift focus between the US screen and the patient poses a challenge. To address these issues, we integrate deep learning (DL)-based semantic segmentation for real-time (RT) automated kidney volumetric measurements, which are essential for clinical assessment but are traditionally time-consuming and prone to fatigue. This automation allows clinicians to concentrate on image interpretation rather than manual measurements. Complementing DL, augmented reality (AR) enhances the usability of US by projecting the display directly into the clinician's field of view, improving ergonomics and reducing the cognitive load associated with screen-to-patient transitions. Two AR-DL-assisted US pipelines on HoloLens-2 are proposed: one streams directly via the application programming interface for a wireless setup, while the other supports any US device with video output for broader accessibility. We evaluate RT feasibility and accuracy using the Open Kidney Dataset and open-source segmentation models (nnU-Net, Segmenter, YOLO with MedSAM and LiteMedSAM). Our open-source GitHub pipeline includes model implementations, measurement algorithms, and a Wi-Fi-based streaming solution, enhancing US training and diagnostics, especially in point-of-care settings.

Biomechanical analysis of the lumbar spine is vital for understanding load distribution and stress patterns under physiological conditions. Traditional finite element analysis (FEA) relies on time-consuming manual segmentation and meshing, leading to long runtimes and inconsistent accuracy. Automating this process improves efficiency and reproducibility. This study introduces an automated FEA methodology for lumbar spine biomechanics, integrating deep learning-based segmentation with computational modeling to streamline workflows from imaging to simulation. Medical imaging data were segmented using deep learning frameworks for vertebrae and intervertebral discs. Segmented structures were transformed into optimized surface meshes via Laplacian smoothing and decimation. Using the Gibbon library and FEBio, FEA models incorporated cortical and cancellous bone, nucleus, annulus, cartilage, and ligaments. Ligament attachments used spherical coordinate-based segmentation; vertebral endplates were extracted via principal component analysis (PCA) for cartilage modeling. Simulations assessed stress, strain, and displacement under axial rotation, extension, flexion, and lateral bending. The automated pipeline cut model preparation time by 97.9%, from over 24 hours to 30 minutes and 49.48 seconds. Biomechanical responses aligned with experimental and traditional FEA data, showing high posterior element loads in extension and flexion, consistent ligament forces, and disc deformations. The approach enhanced reproducibility with minimal manual input. This automated methodology provides an efficient, accurate framework for lumbar spine biomechanics, eliminating manual segmentation challenges. It supports clinical diagnostics, implant design, and rehabilitation, advancing computational and patient-specific spinal studies. Rapid simulations enhance implant optimization, and early detection of degenerative spinal issues, improving personalized treatment and research.

Coronary computed tomography angiography (CCTA) and magnetic resonance imaging (MRI) can predict periprocedural myocardial injury (PMI) after percutaneous coronary intervention (PCI). We aimed to investigate whether integrating MRI with CCTA, using the latest imaging and quantitative techniques, improves PMI prediction and to explore a potential hybrid CCTA-MRI strategy. This prospective, multi-centre study conducted coronary atherosclerosis T1-weighted characterization MRI for patients scheduled for elective PCI for an atherosclerotic lesion detected on CCTA without prior revascularization. PMI was defined as post-PCI troponin-T > 5× the upper reference limit. Using deep learning-enabled software, volumes of total plaque, calcified plaque, non-calcified plaque (NCP), and low-attenuation plaque (LAP; < 30 Hounsfield units) were quantified on CCTA. In non-contrast T1-weighted MRI, high-intensity plaque (HIP) volume was quantified as voxels with signal intensity exceeding that of the myocardium, weighted by their respective intensities. Of the 132 lesions from 120 patients, 43 resulted in PMI. In the CCTA-only strategy, LAP volume (P = 0.012) and NCP volume (P = 0.016) were independently associated with PMI. In integrating MRI with CCTA, LAP volume (P = 0.029), and HIP volume (P = 0.024) emerged as independent predictors. MRI integration with CCTA achieved a higher C-statistic value than CCTA alone (0.880 vs. 0.738; P = 0.004). A hybrid CCTA-MRI strategy, incorporating MRI for lesions with intermediate PMI risk based on CCTA, maintained superior diagnostic accuracy over the CCTA-only strategy (0.803 vs. 0.705; P = 0.028). Integrating MRI with CCTA improves PMI prediction compared with CCTA alone.

Fetal ultrasound (US) image segmentation plays an important role in fetal development assessment, maternal pregnancy management, and intrauterine surgery planning. However, obtaining large-scale, accurately annotated fetal US imaging data is time-consuming and labor-intensive, posing challenges to the application of deep learning in this field. To address this challenge, we propose a semi-supervised fetal US image segmentation method based on bidirectional prototype-guided consistency constraint (BiPCC). BiPCC utilizes the prototype to bridge labeled and unlabeled data and establishes interaction between them. Specifically, the model generates pseudo-labels using prototypes from labeled data and then utilizes these pseudo-labels to generate pseudo-prototypes for segmenting the labeled data inversely, thereby achieving bidirectional consistency. Additionally, uncertainty-based cross-supervision is incorporated to provide additional supervision signals, thereby enhancing the quality of pseudo-labels. Extensive experiments on two fetal US datasets demonstrate that BiPCC outperforms state-of-the-art methods for semi-supervised fetal US segmentation. Furthermore, experimental results on two additional medical segmentation datasets exhibit BiPCC's outstanding generalization capability for diverse medical image segmentation tasks. Our proposed method offers a novel insight for semi-supervised fetal US image segmentation and holds promise for further advancing the development of intelligent healthcare.

Accurate segmentation of organs in the abdomen is a primary requirement for any medical analysis and treatment planning. In this study, we propose an approach based on 3D UNet and UNet +  + architectures implemented in the MONAI framework for addressing challenges that arise due to anatomical variability, complex shape rendering of organs, and noise in CT/MRI scans. The models can analyze information in three dimensions from volumetric data, making use of skip and dense connections, and optimizing the parameters using Secretary Bird Optimization (SBO), which together help in better feature extraction and boundary delineation of the structures of interest across sets of multi-organ tissues. The developed model's performance was evaluated on multiple datasets, ranging from Pancreas-CT to Liver-CT and BTCV. The results indicated that on the Pancreas-CT dataset, a DSC of 94.54% was achieved for 3D UNet, while a slightly higher DSC of 95.62% was achieved for 3D UNet +  +. Both models performed well on the Liver-CT dataset, with 3D UNet acquiring a DSC score of 95.67% and 3D UNet +  + a DSC score of 97.36%. And in the case of the BTCV dataset, both models had DSC values ranging from 93.42 to 95.31%. These results demonstrate the robustness and efficiency of the models presented for clinical applications and medical research in multi-organ segmentation. This study validates the proposed architectures, underpinning and accentuating accuracy in medical imaging, creating avenues for scalable solutions for complex abdominal-imaging tasks.

&#xD;Radiotherapy (RT) has become increasingly sophisticated, necessitating advanced tools for analyzing extensive treatment data in hospital databases. Such analyses can enhance future treatments, particularly through Knowledge-Based Planning, and aid in developing new treatment modalities like convergent kV RT.&#xD;Purpose: The objective is to develop automated software tools for large-scale retrospective analysis of over 10,000 MeV x-ray radiotherapy plans. This aims to identify trends and references in plans delivered at our institution across all treatment sites, focusing on: (A) Planning-Target-Volume, Clinical-Target-Volume, Gross-Tumor-Volume, and Organ-At-Risk (PTV/CTV/GTV/OAR) topology, morphology, and dosimetry, and (B) RT plan efficiency and complexity.&#xD;Methods:&#xD;The software tools are coded in Python. Topological metrics are evaluated using principal component analysis, including center of mass, volume, size, and depth. Morphology is quantified using Hounsfield Units, while dose distribution is characterized by conformity and homogeneity indexes. The total dose within the target versus the body is defined as the Dose Balance Index. &#xD;Results:&#xD;The primary outcome of this study is the toolkit and an analysis of our database. For example, the mean minimum and maximum PTV depths are about 2.5±2.3 cm and 9±3 cm, respectively.&#xD;Conclusions:&#xD;This study provides a statistical basis for RT plans and the necessary tools to generate them. It aids in selecting plans for knowledge-based models and deep-learning networks. The site-specific volume and depth results help identify the limitations and opportunities of current and future treatment modalities, in our case convergent kV RT. The compiled statistics and tools are versatile for training, quality assurance, comparing plans from different periods or institutions, and establishing guidelines. The toolkit is publicly available at https://github.com/m-kinz/STAR.

Precise delineation of brain tissues, including lesions, in MR images is crucial for data analysis and objectively assessing conditions like neurological disorders and brain tumors. Existing methods for tissue segmentation often fall short in addressing patients with lesions, particularly those with brain tumors. This study aimed to develop and evaluate a robust pipeline utilizing convolutional neural networks for rapid and automatic segmentation of whole brain tissues, including tumor lesions. The proposed pipeline was developed using BraTS'21 data (1251 patients) and tested on local hospital data (100 patients). Ground truth masks for lesions as well as brain tissues were generated. Two convolutional neural networks based on deep residual U-Net framework were trained for segmenting brain tissues and tumor lesions. The performance of the pipeline was evaluated on independent test data using dice similarity coefficient (DSC) and volume similarity (VS). The proposed pipeline achieved a mean DSC of 0.84 and a mean VS of 0.93 on the BraTS'21 test data set. On the local hospital test data set, it attained a mean DSC of 0.78 and a mean VS of 0.91. The proposed pipeline also generated satisfactory masks in cases where the SPM12 software performed inadequately. The proposed pipeline offers a reliable and automatic solution for segmenting brain tissues and tumor lesions in MR images. Its adaptability makes it a valuable tool for both research and clinical applications, potentially streamlining workflows and enhancing the precision of analyses in neurological and oncological studies.