Recently, Ultra-High Field MRI (UHF-MRI) has become more available and one of the best tools to study the brain. One common step in quantitative neuroimaging is the brain segmentation. However, the differences between UHF-MRI and 1.5-3T images are such that the automatic segmentation techniques optimized at these field strengths usually produce unsatisfactory segmentation results for UHF images. It has been particularly challenging to perform quantitative analyses as typically done with 1.5-3T data, considerably limiting the potential of UHF-MRI. Hence, we propose a novel Deep Learning (DL)-based segmentation technique called GOUHFI: Generalized and Optimized segmentation tool for Ultra-High Field Images, designed to segment UHF images of various contrasts and resolutions. For training, we used a total of 206 label maps from four datasets acquired at 3T, 7T and 9.4T. In contrast to most DL strategies, we used a previously proposed domain randomization approach, where synthetic images generated from the label maps were used for training a 3D U-Net. GOUHFI was tested on seven different datasets and compared to techniques like FastSurferVINN and CEREBRUM-7T. GOUHFI was able to the segment six contrasts and seven resolutions tested at 3T, 7T and 9.4T. Average Dice-Sorensen Similarity Coefficient (DSC) scores of 0.87, 0.84, 0.91 were computed against the ground truth segmentations at 3T, 7T and 9.4T. Moreover, GOUHFI demonstrated impressive resistance to the typical inhomogeneities observed at UHF-MRI, making it a new powerful segmentation tool that allows to apply the usual quantitative analysis pipelines also at UHF. Ultimately, GOUHFI is a promising new segmentation tool, being the first of its kind proposing a contrast- and resolution-agnostic alternative for UHF-MRI, making it the forthcoming alternative for neuroscientists working with UHF-MRI or even lower field strengths.

Severe respiratory infections pose a major challenge in clinical practice, especially in older adults. Body composition analysis could play a crucial role in risk assessment and therapeutic decision-making. This study investigates whether obesity or sarcopenia has a greater impact on mortality in patients with severe respiratory infections. The study focuses on the National Pandemic Cohort Network (NAPKON-SUEP) cohort, which includes patients over 60 years of age with confirmed severe COVID-19 pneumonia. An innovative approach was adopted, using pre-trained deep learning models for automated analysis of body composition based on routine thoracic CT scans. The study included 157 hospitalized patients (mean age 70 ± 8 years, 41% women, mortality rate 39%) from the NAPKON-SUEP cohort at 57 study sites. A pre-trained deep learning model was used to analyze body composition (muscle, bone, fat, and intramuscular fat volumes) from thoracic CT images of the NAPKON-SUEP cohort. Binary logistic regression was performed to investigate the association between obesity, sarcopenia, and mortality. Non-survivors exhibited lower muscle volume (p = 0.043), higher intramuscular fat volume (p = 0.041), and a higher BMI (p = 0.031) compared to survivors. Among all body composition parameters, muscle volume adjusted to weight was the strongest predictor of mortality in the logistic regression model, even after adjusting for factors such as sex, age, diabetes, chronic lung disease and chronic kidney disease, (odds ratio = 0.516). In contrast, BMI did not show significant differences after adjustment for comorbidities. This study identifies muscle volume derived from routine CT scans as a major predictor of survival in patients with severe respiratory infections. The results underscore the potential of AI supported CT-based body composition analysis for risk stratification and clinical decision making, not only for COVID-19 patients but also for all patients over 60 years of age with severe acute respiratory infections. The innovative application of pre-trained deep learning models opens up new possibilities for automated and standardized assessment in clinical practice.

Clinical assessment of lymphedema, particularly for lymphedema severity and fluid-fibrotic lesions, remains challenging with traditional methods. We aimed to develop and validate a deep learning segmentation tool for automated tissue component analysis in lower extremity CT scans. For development datasets, lower extremity CT venography scans were collected in 118 patients with gynecologic cancers for algorithm training. Reference standards were created by segmentation of fat, muscle, and fluid-fibrotic tissue components using 3D slicer. A deep learning model based on the Unet++ architecture with an EfficientNet-B7 encoder was developed and trained. Segmentation accuracy of the deep learning model was validated in an internal validation set (n = 10) and an external validation set (n = 10) using Dice similarity coefficient (DSC) and volumetric similarity (VS). A graphical user interface (GUI) tool was developed for the visualization of the segmentation results. Our deep learning algorithm achieved high segmentation accuracy. Mean DSCs for each component and all components ranged from 0.945 to 0.999 in the internal validation set and 0.946 to 0.999 in the external validation set. Similar performance was observed in the VS, with mean VSs for all components ranging from 0.97 to 0.999. In volumetric analysis, mean volumes of the entire leg and each component did not differ significantly between reference standard and deep learning measurements (p > 0.05). Our GUI displays lymphedema mapping, highlighting segmented fat, muscle, and fluid-fibrotic components in the entire leg. Our deep learning algorithm provides an automated segmentation tool enabling accurate segmentation, volume measurement of tissue component, and lymphedema mapping. Question Clinical assessment of lymphedema remains challenging, particularly for tissue segmentation and quantitative severity evaluation. Findings A deep learning algorithm achieved DSCs > 0.95 and VS > 0.97 for fat, muscle, and fluid-fibrotic components in internal and external validation datasets. Clinical relevance The developed deep learning tool accurately segments and quantifies lower extremity tissue components on CT scans, enabling automated lymphedema evaluation and mapping with high segmentation accuracy.

Enteral feeding needs secure access to the upper gastrointestinal tract, an evaluation of the gastric function to detect gastrointestinal intolerance, and a nutritional target to reach the patient's needs. Only in the last decades has progress been accomplished in techniques allowing an appropriate placement of the nasogastric tube, mainly reducing pulmonary complications. These techniques include point-of-care ultrasound (POCUS), electromagnetic sensors, real-time video-assisted placement, impedance sensors, and virtual reality. Again, POCUS is the most accessible tool available to evaluate gastric emptying, with antrum echo density measurement. Automatic measurements of gastric antrum content supported by deep learning algorithms and electric impedance provide gastric volume. Intragastric balloons can evaluate motility. Finally, advanced technologies have been tested to improve nutritional intake: Stimulation of the esophagus mucosa inducing contraction mimicking a contraction wave that may improve enteral nutrition efficacy, impedance sensors to detect gastric reflux and modulate the rate of feeding accordingly have been clinically evaluated. Use of electronic health records integrating nutritional needs, target, and administration is recommended.

Background and Objective: Precise preoperative planning and effective physician training for coronary interventions are increasingly important. Despite advances in medical imaging technologies, transforming static or limited dynamic imaging data into comprehensive dynamic cardiac models remains challenging. Existing training systems lack accurate simulation of cardiac physiological dynamics. This study develops a comprehensive dynamic cardiac model research framework based on 4D-CTA, integrating digital twin technology, computer vision, and physical model manufacturing to provide precise, personalized tools for interventional cardiology. Methods: Using 4D-CTA data from a 60-year-old female with three-vessel coronary stenosis, we segmented cardiac chambers and coronary arteries, constructed dynamic models, and implemented skeletal skinning weight computation to simulate vessel deformation across 20 cardiac phases. Transparent vascular physical models were manufactured using medical-grade silicone. We developed cardiac output analysis and virtual angiography systems, implemented guidewire 3D reconstruction using binocular stereo vision, and evaluated the system through angiography validation and CABG training applications. Results: Morphological consistency between virtual and real angiography reached 80.9%. Dice similarity coefficients for guidewire motion ranged from 0.741-0.812, with mean trajectory errors below 1.1 mm. The transparent model demonstrated advantages in CABG training, allowing direct visualization while simulating beating heart challenges. Conclusion: Our patient-specific digital-physical twin approach effectively reproduces both anatomical structures and dynamic characteristics of coronary vasculature, offering a dynamic environment with visual and tactile feedback valuable for education and clinical planning.

Deep learning models are increasingly being implemented for automated medical image analysis to inform patient care. Most models, however, lack uncertainty information, without which the reliability of model outputs cannot be ensured. Several uncertainty quantification (UQ) methods exist to capture model uncertainty. Yet, it is not clear which method is optimal for a given task. The purpose of this work was to investigate several commonly used UQ methods for the critical yet understudied task of metastatic lesion segmentation on whole body PET/CT. 
Approach:
59 whole body 68Ga-DOTATATE PET/CT images of patients undergoing theranostic treatment of metastatic neuroendocrine tumors were used in this work. A 3D U-Net was trained for lesion segmentation following five-fold cross validation. Uncertainty measures derived from four UQ methods-probability entropy, Monte Carlo dropout, deep ensembles, and test time augmentation-were investigated. Each uncertainty measure was assessed across four quantitative evaluations: (1) its ability to detect artificially degraded image data at low, medium, and high degradation magnitudes; (2) to detect false-positive (FP) predicted regions; (3) to recover false-negative (FN) predicted regions; and (3) to establish correlations with model biomarker extraction and segmentation performance metrics. 
Results: Test time augmentation and probability entropy respectively achieved the highest and lowest degraded image detection at low (AUC=0.54 vs. 0.68), medium (AUC=0.70 vs. 0.82), and high (AUC=0.83 vs. 0.90) degradation magnitudes. For detecting FPs, all UQ methods achieve strong performance, with AUC values ranging narrowly between 0.77 and 0.81. FN region recovery performance was strongest for test time augmentation and weakest for probability entropy. Performance for the correlation analysis was mixed, where the strongest performance was achieved by test time augmentation for SUVtotal capture (ρ=0.57) and segmentation Dice coefficient (ρ=0.72), by Monte Carlo dropout for SUVmean capture (ρ=0.35), and by probability entropy for segmentation cross entropy (ρ=0.96).
Significance: Overall, test time augmentation demonstrated superior uncertainty quantification performance and is recommended for use in metastatic lesion segmentation task. It also offers the advantage of being post hoc and computationally efficient. In contrast, probability entropy performed the worst, highlighting the need for advanced UQ approaches for this task.&#xD.

To evaluate the impact of fluid volume fluctuations quantified with artificial intelligence in optical coherence tomography scans during the maintenance phase and visual outcomes at 12 and 24 months in a real-world, multicentre, national cohort of treatment-naïve neovascular age-related macular degeneration (nAMD) eyes. Demographics, visual acuity (VA) and number of injections were collected using the Fight Retinal Blindness tool. Intraretinal fluid (IRF), subretinal fluid (SRF), pigment epithelial detachment (PED), total fluid (TF) and central subfield thickness (CST) were quantified using the RetinAI Discovery tool. Fluctuations were defined as the SD of within-eye quantified values, and eyes were distributed according to SD quartiles for each biomarker. A total of 452 naïve nAMD eyes were included. Eyes with highest (Q4) versus lowest (Q1) fluid fluctuations showed significantly worse VA change (months 3-12) in IRF -3.91 versus 3.50 letters, PED -4.66 versus 3.29, TF -2.07 versus 2.97 and CST -1.85 versus 2.96 (all p<0.05), but not for SRF 0.66 versus 0.93 (p=0.91). Similar VA outcomes were observed at month 24 for PED -8.41 versus 4.98 (p<0.05), TF -7.38 versus 1.89 (p=0.07) and CST -10.58 versus 3.60 (p<0.05). The median number of injections (months 3-24) was significantly higher in Q4 versus Q1 eyes in IRF 9 versus 8, SRF 10 versus 8 and TF 10 versus 8 (all p<0.05). This multicentre study reports a negative effect in VA outcomes of fluid volume fluctuations during the maintenance phase in specific fluid compartments, suggesting that anatomical and functional treatment response patterns may be fluid-specific.

Minimally invasive surgery involves entering the body through small incisions or natural orifices, using a medical endoscope for observation and clinical procedures. However, traditional endoscopic images often suffer from low texture and uneven illumination, which can negatively impact surgical and diagnostic outcomes. To address these challenges, many researchers have applied deep learning methods to enhance the processing of endoscopic images. This paper proposes a monocular medical endoscopic image depth estimation method based on a window-adaptive asymmetric dual-branch Siamese network. In this network, one branch focuses on processing global image information, while the other branch concentrates on local details. An improved lightweight Squeeze-and-Excitation (SE) module is added to the final layer of each branch, dynamically adjusting the inter-channel weights through self-attention. The outputs from both branches are then integrated using a lightweight cross-attention feature fusion module, enabling cross-branch feature interaction and enhancing the overall feature representation capability of the network. Extensive ablation and comparative experiments were conducted on medical datasets (EAD2019, Hamlyn, M2caiSeg, UCL) and a non-medical dataset (NYUDepthV2), with both qualitative and quantitative results-measured in terms of RMSE, AbsRel, FLOPs and running time-demonstrating the superiority of the proposed model. Additionally, comparisons with CT images show good organ boundary matching capability, highlighting the potential of our method for clinical applications. The key code of this paper is available at: https://github.com/superchongcnn/AttenAdapt_DE .

PCOS (Poly-Cystic Ovary Syndrome) is a multifaceted disorder that often affects the ovarian morphology of women of their reproductive age, resulting in the development of numerous cysts on the ovaries. Ultrasound imaging typically diagnoses PCOS, which helps clinicians assess the size, shape, and existence of cysts in the ovaries. Nevertheless, manual ultrasound image analysis is often challenging and time-consuming, resulting in inter-observer variability. To effectively treat PCOS and prevent its long-term effects, prompt and accurate diagnosis is crucial. In such cases, a prediction model based on deep learning can help physicians by streamlining the diagnosis procedure, reducing time and potential errors. This article proposes a novel integrated approach, QEI-SAM (Quality Enhanced Image - Segment Anything Model), for enhancing image quality and ovarian cyst segmentation for accurate prediction. GAN (Generative Adversarial Networks) and CNN (Convolutional Neural Networks) are the most recent cutting-edge innovations that have supported the system in attaining the expected result. The proposed QEI-SAM model used Enhanced Super Resolution Generative Adversarial Networks (ESRGAN) for image enhancement to increase the resolution, sharpening the edges and restoring the finer structure of the ultrasound ovary images and achieved a better SSIM of 0.938, PSNR value of 38.60 and LPIPS value of 0.0859. Then, it incorporates the Segment Anything Model (SAM) to segment ovarian cysts and achieve the highest Dice coefficient of 0.9501 and IoU score of 0.9050. Furthermore, Convolutional Neural Network - ResNet 50, ResNet 101, VGG 16, VGG 19, AlexNet and Inception v3 have been implemented to diagnose PCOS promptly. Finally, VGG 19 has achieved the highest accuracy of 99.31%.

Late Gadolinium-enhancement in cardiac magnetic resonance imaging (LGE-CMR) is the gold standard for assessing myocardial infarction (MI) size. Texture-based probability mapping (TPM) is a novel machine learning-based analysis of LGE images of myocardial injury. The ability of TPM to assess acute myocardial injury has not been determined. This proof-of-concept study aimed to determine how TPM responds to the dynamic changes in myocardial injury during one-year follow-up after a first-time revascularized acute MI. 41 patients with first-time acute ST-elevation MI and single-vessel occlusion underwent successful PCI. LGE-CMR images were obtained 2 days, 1 week, 2 months, and 1 year following MI. TPM size was compared with manual LGE-CMR based MI size, LV remodeling, and biomarkers. TPM size remained larger than MI by LGE-CMR at all time points, decreasing from 2 days to 2 months (p < 0.001) but increasing from 2 months to 1 year (p < 0.01). TPM correlated strongly with peak Troponin T (p < 0.001) and NT-proBNP (p < 0.001). At 1 week, 2 months, and 1 year, TPM showed a stronger correlation with NT-proBNP than MI size by LGE-CMR. Analyzing all collected pixels from 2 months to 1 year revealed a general increase in pixel scar probability in both the infarcted and non-infarcted regions. This proof-of-concept study suggests that TPM may offer additional insights into myocardial alterations in both infarcted and non-infarcted regions following acute MI. These findings indicate a potential role for TPM in assessing the overall myocardial response to infarction and the subsequent healing and remodeling process.