Image-based modeling is essential for understanding cardiovascular hemodynamics and advancing the diagnosis and treatment of cardiovascular diseases. Constructing patient-specific vascular models remains labor-intensive, error-prone, and time-consuming, limiting their clinical applications. This study introduces a deep-learning framework that automates the creation of simulation-ready vascular models from medical images. The framework integrates a segmentation module for accurate voxel-based vessel delineation with a surface deformation module that performs anatomically consistent and unsupervised surface refinements guided by medical image data. The integrated pipeline addresses key limitations of existing methods, enhancing geometric accuracy and computational efficiency. Evaluated on public datasets, it achieves state-of-the-art segmentation performance while substantially reducing manual effort and processing time. The resulting vascular models exhibit anatomically accurate and visually realistic geometries, effectively capturing both primary vessels and intricate branching patterns. In conclusion, this work advances the scalability and reliability of image-based computational modeling, facilitating broader applications in clinical and research settings.

Scoliosis is the most common developmental spinal deformity, but its genetic underpinnings remain only partially understood. To enhance the identification of scoliosis-related loci, we utilized whole body dual energy X-ray absorptiometry (DXA) scans from 57,887 individuals in the UK Biobank (UKB), and quantified spine curvature by applying deep learning models to segment then landmark vertebrae to measure the cumulative horizontal displacement of the spine from a central axis. On a subset of 120 individuals, our automated image-derived curvature measurements showed a correlation 0.92 with clinical Cobb angle assessments, supporting their validity as a proxy for scoliosis severity. To connect spinal curvature with its genetic basis we conducted a genome-wide association study (GWAS). Our quantitative imaging phenotype allowed us to identify 2 novel loci associated with scoliosis in a European population not seen in previous GWAS. These loci are in the gene SEM1/SHFM1 as well as on a lncRNA on chr 3 that is downstream of EDEM1 and upstream of GRM7. Genetic correlation analysis revealed significant overlap between our image-based GWAS and ICD-10 based GWAS in both the UKB and Biobank of Japan. We also showed that our quantitative GWAS had more statistical power to identify new loci than a case-control dataset with an order of magnitude larger sample size. Increased spine curvature was also associated with increased leg length discrepancy, reduced muscle strength and decreased bone density, and increased incidence of knee but not hip osteoarthritis. Our results illustrate the potential of using quantitative imaging phenotypes to uncover genetic associations that are challenging to capture with medical records alone and identify new loci for functional follow-up.

<b><i>Background:</i></b> Oncologic emergencies in critically ill cancer patients frequently require rapid, real-time assessment of tumor responses to therapeutic interventions. However, conventional imaging modalities such as computed tomography and magnetic resonance imaging are often impractical in intensive care units (ICUs) due to logistical constraints and patient instability. Super-resolution ultrasound (SR-US) imaging has emerged as a promising noninvasive alternative, facilitating bedside evaluation of tumor microvascular dynamics with exceptional spatial resolution. This study assessed the clinical utility of real-time SR-US imaging in monitoring tumor perfusion changes during emergency management in oncological ICU settings. <b><i>Methods:</i></b> In this prospective observational study, critically ill patients with oncologic emergencies underwent bedside SR-US imaging before and after the initiation of emergency therapy (e.g., corticosteroids, decompression, or chemotherapy). SR-US was employed to quantify microvascular parameters, including perfusion density and flow heterogeneity. Data processing incorporated artificial intelligence for real-time vessel segmentation and quantitative analysis. <b><i>Results:</i></b> SR-US imaging successfully detected perfusion changes within hours of therapy initiation. A significant correlation was observed between reduced tumor perfusion and clinical improvement, including symptom relief and shorter ICU stay. This technology enables visualization of microvessels as small as 30 µm, surpassing conventional ultrasound limits. No adverse events were reported with the use of contrast microbubbles. In addition, SR-US imaging reduces the need for transportation to radiology departments, thereby optimizing ICU workflow. <b><i>Conclusions:</i></b> Real-time SR-US imaging offers a novel, bedside-compatible method for evaluating tumor vascular response during the acute phase of oncological emergencies. Its integration into ICU care pathways could enhance timely decision-making, reduce reliance on static imaging, and support personalized cancer management. Further multicenter validation is required.

Non-contrast computed tomography (NCCT) is a first-line imaging technique for determining treatment options for acute ischemic stroke (AIS). However, its poor contrast and signal-to-noise ratio limit the diagnosis accuracy for radiologists, and automated AIS lesion segmentation using NCCT also remains a challenge. This study aims to develop a segmentation method for ischemic lesions in NCCT scans, combining symmetry-based principles with the nnUNet segmentation model. Our novel approach integrates a Generative Module (GM) utilizing 2.5 D ResUNet and an Upstream Segmentation Module (UM) with additional inputs and constraints under the 3D nnUNet segmentation model, utilizing symmetry-based learning to enhance the identification and segmentation of ischemic regions. We utilized the publicly accessible AISD dataset for our experiments. This dataset contains 397 NCCT scans of acute ischemic stroke taken within 24 h of the onset of symptoms. Our method was trained and validated using 345 scans, while the remaining 52 scans were used for internal testing. Additionally, we included 60 positive cases (External Set 1) with segmentation labels obtained from our hospital for external validation of the segmentation task. External Set 2 was employed to evaluate the model's sensitivity and specificity in case-dimensional classification, further assessing its clinical performance. We introduced innovative features such as an intensity-based lesion probability (ILP) function and specific input channels for suspected lesion areas to augment the model's sensitivity and specificity. The methodology demonstrated commendable segmentation efficacy, attaining a Dice Similarity Coefficient (DSC) of 0.6720 and a Hausdorff Distance (HD95) of 35.28 on the internal test dataset. Similarly, on the external test dataset, the method yielded satisfactory segmentation outcomes, with a DSC of 0.4891 and an HD 95 of 46.06. These metrics reflect a substantial overlap with expert-drawn boundaries and demonstrate the model's potential for reliable clinical application. In terms of classification performance, the method achieved an Area Under the Curve (AUC) of 0.991 on the external test set, surpassing the performance of nnUNet, which recorded an AUC of 0.947. This study introduces a novel segmentation technique for ischemic lesions in NCCT scans, leveraging symmetry-based principles integrated with nnUNet, which shows potential for improving clinical decision-making in stroke care.

Lung lobe segmentation is required to assess lobar function with nuclear imaging before surgical interventions. We evaluated the performance of open-source deep learning-based lung lobe segmentation tools, compared to a similar nnU-Net model trained on a smaller but more representative clinical dataset. We collated and semi-automatically segmented an internal dataset of 164 computed tomography scans and classified them for task difficulty as easy, moderate, or hard. The performance of three open-source models-multi-organ objective segmentation (MOOSE), TotalSegmentator, and LungMask-was assessed using Dice similarity coefficient (DSC), robust Hausdorff distance (rHd95), and normalized surface distance (NSD). Additionally, we trained, validated, and tested an nnU-Net model using our local dataset and compared its performance with that of the other software on the test subset. All models were evaluated for generalizability using an external competition (LOLA11, n = 55). TotalSegmentator outperformed MOOSE in DSC and NSD across all difficulty levels (p < 0.001), but not in rHd95 (p = 1.000). MOOSE and TotalSegmentator surpassed LungMask across metrics and difficulty classes (p < 0.001). Our model exceeded all other models on the internal dataset (n = 33) in all metrics, across all difficulty classes (p < 0.001), and on the external dataset. Missing lobes were correctly identified only by our model and LungMask in 3 and 1 of 7 cases, respectively. Open-source segmentation tools perform well in straightforward cases but struggle in unfamiliar, complex cases. Training on diverse, specialized datasets can improve generalizability, emphasizing representative data over sheer quantity. Training lung lobe segmentation models on a local variety of cases improves accuracy, thus enhancing presurgical planning, ventilation-perfusion analysis, and disease localization, potentially impacting treatment decisions and patient outcomes in respiratory and thoracic care. Deep learning models trained on non-specialized datasets struggle with complex lung anomalies, yet their real-world limitations are insufficiently assessed. Training an identical model on a smaller yet clinically diverse and representative cohort improved performance in challenging cases. Data diversity outweighs the quantity in deep learning-based segmentation models. Accurate lung lobe segmentation may enhance presurgical assessment of lung lobar ventilation and perfusion function, optimizing clinical decision-making and patient outcomes.

Collimation in X-ray imaging restricts exposure to the region-of-interest (ROI) and minimizes the radiation dose applied to the patient. The detection of collimator shadows is an essential image-based preprocessing step in digital radiography posing a challenge when edges get obscured by scattered X-ray radiation. Regardless, the prior knowledge that collimation forms polygonal-shaped shadows is evident. For this reason, we introduce a deep learning-based segmentation that is inherently constrained to its geometry. We achieve this by incorporating a differentiable Hough transform-based network to detect the collimation borders and enhance its capability to extract the information about the ROI center. During inference, we combine the information of both tasks to enable the generation of refined, line-constrained segmentation masks. We demonstrate robust reconstruction of collimated regions achieving median Hausdorff distances of 4.3-5.0mm on diverse test sets of real Xray images. While this application involves at most four shadow borders, our method is not fundamentally limited by a specific number of edges.

To develop a cascaded deep learning (DL) framework integrating tumor segmentation with metastatic risk stratification for preoperative prediction of occult peritoneal metastasis (OPM) in advanced gastric cancer (GC), and validate its generalizability for early peritoneal recurrence (PR) prediction. This multicenter study enrolled 765 patients with advanced GC from three institutions. We developed a two-stage framework as follows: (1) V-Net-based tumor segmentation on CT; (2) DL-based metastatic risk classification using segmented tumor regions. Clinicopathological predictors were integrated with deep learning probabilities to construct a combined model. Validation cohorts comprised: Internal validation (Test1 for OPM, n=168; Test2 for early PR, n=212) and External validation (Test3 for early PR, n=57 from two independent centers). Multivariable analysis identified Borrmann type (OR=1.314, 95% CI: 1.239-1.394), CA125 ≥35U/mL (OR=1.301, 95% CI: 1.127-1.499), and CT-N+ stage (OR=1.259, 95% CI: 1.124-1.415) as independent OPM predictors. The combined model demonstrated robust performance for both OPM and early PR prediction: achieving AUCs of 0.938 (Train) and 0.916 (Test1) for OPM with improvements over clinical (∆AUC +0.039-+0.107) and DL-only models (∆AUC +0.044-+0.104), while attaining AUC 0.820-0.825 for early PR (Test2 and Test3) with balanced sensitivity (79.7-88.9%) and specificity (72.4-73.3%). Decision curve analysis confirmed net clinical benefit across clinical thresholds. This CT-based cascaded framework enables reliable preoperative risk stratification for OPM and early PR in advanced GC, potentially refining indications for personalized therapeutic pathways.

Pneumothorax, the abnormal accumulation of air in the pleural space, can be life-threatening if undetected. Chest X-rays are the first-line diagnostic tool, but small cases may be subtle. We propose an automated deep-learning pipeline using a U-Net with an EfficientNet-B4 encoder to segment pneumothorax regions. Trained on the SIIM-ACR dataset with data augmentation and a combined binary cross-entropy plus Dice loss, the model achieved an IoU of 0.7008 and Dice score of 0.8241 on the independent PTX-498 dataset. These results demonstrate that the model can accurately localize pneumothoraces and support radiologists.

The use of machine learning has seen extraordinary growth since the development of deep learning techniques, notably the deep artificial neural network. Deep learning methodology excels in addressing complicated problems such as image classification, object detection, and natural language processing. A key feature of these networks is the capability to extract useful patterns from vast quantities of complex data, including images. As many branches of healthcare revolves around the generation, processing, and analysis of images, these techniques have become increasingly commonplace. This is especially true for radiotherapy, which relies on the use of anatomical and functional images from a range of imaging modalities, such as Computed Tomography (CT). The aim of this review is to provide an understanding of deep learning methodologies, including neural network types and structure, as well as linking these general concepts to medical CT image processing for radiotherapy. Specifically, it focusses on the stages of enhancement and analysis, incorporating image denoising, super-resolution, generation, registration, and segmentation, supported by examples of recent literature.

Precise implant placement in the anterior and posterior maxilla often presents challenges due to variable bone and soft tissue anatomy. Many clinicians elect a freehand surgical approach because conventional surgical guides may not always be easy to design, fabricate, or utilize. Guided surgery has been proven to have advantages over freehand surgical protocols and therefore, the present study proposed utilizing the nasopalatine canal (NPC) as an anatomical reference and point of fixation for a novel rotational path surgical template during computer-aided implant surgery (CAIS). The present digital workflow combined artificial intelligence (AI) facilitated cone beam computed tomography (CBCT) software bone segmentation of the maxillary arch to assess the NPC and surrounding hard tissues, to design and fabricate static surgical guides to precisely place implants. After rotational engagement of the maxillary buccal undercuts, each novel surgical guide incorporated the NPC for fixation with a single pin to achieve initial stability. 22 consecutive patients requiring maxillary reconstruction received 123 implants (7 fully and 15 partially edentulous) utilizing a fully-guided surgical protocol to complete 4 overdenture and 18 full-arch fixed restorations. 12 patients required extensive maxillary bone augmentation before implant placement. 13 patients required delayed loading based on bone density and 9 patients were restoratively loaded within 24 to 96 hours post-surgery, accomplished with the use of photogrammetry for the fabrication of 3D-printed restorations. The initial implant success rate was 98.37% and 100% initial prosthetic success. The use of the NPC for fixation of surgical guides did not result in any neurovascular post-operative complications. The novel template concept can improve surgical outcomes using a bone-borne template design for implant-supported rehabilitation of the partial and fully edentulous maxillary arch. Preliminary case series confirmed controlled placement accuracy with limited risk of neurovascular complications for full-arch overdenture and fixed restorations. NPC is a vital maxillary anatomic landmark for implant planning, with an expanded role for the stabilization of novel surgical guide designs due to advancements in AI bone segmentation.