Background: Conventional cardiovascular magnetic resonance (CMR) in paediatric and congenital heart disease uses 2D, breath-hold, balanced steady state free precession (bSSFP) cine imaging for assessment of function and cardiac-gated, respiratory-navigated, static 3D bSSFP whole-heart imaging for anatomical assessment. Our aim is to concatenate a stack 2D free-breathing real-time cines and use Deep Learning (DL) to create an isotropic a fully segmented 3D cine dataset from these images. Methods: Four DL models were trained on open-source data that performed: a) Interslice contrast correction; b) Interslice respiratory motion correction; c) Super-resolution (slice direction); and d) Segmentation of right and left atria and ventricles (RA, LA, RV, and LV), thoracic aorta (Ao) and pulmonary arteries (PA). In 10 patients undergoing routine cardiovascular examination, our method was validated on prospectively acquired sagittal stacks of real-time cine images. Quantitative metrics (ventricular volumes and vessel diameters) and image quality of the 3D cines were compared to conventional breath hold cine and whole heart imaging. Results: All real-time data were successfully transformed into 3D cines with a total post-processing time of <1 min in all cases. There were no significant biases in any LV or RV metrics with reasonable limits of agreement and correlation. There is also reasonable agreement for all vessel diameters, although there was a small but significant overestimation of RPA diameter. Conclusion: We have demonstrated the potential of creating a 3D-cine data from concatenated 2D real-time cine images using a series of DL models. Our method has short acquisition and reconstruction times with fully segmented data being available within 2 minutes. The good agreement with conventional imaging suggests that our method could help to significantly speed up CMR in clinical practice.

Patients with recurrent complaints after total knee arthroplasty may suffer from aseptic implant loosening. Current imaging modalities do not quantify looseness of knee arthroplasty components. A recently developed and validated workflow quantifies the tibial component displacement relative to the bone from CT scans acquired under valgus and varus load. The 3D analysis approach includes segmentation and registration of the tibial component and bone. In the current approach, the semi-automatic segmentation requires user interaction, adding complexity to the analysis. The research question is whether the segmentation step can be fully automated while keeping outcomes indifferent. In this study, different deep-learning (DL) models for fully automatic segmentation are proposed and evaluated. For this, we employ three different datasets for model development (20 cadaveric CT pairs and 10 cadaveric CT scans) and evaluation (72 patient CT pairs). Based on the performance on the development dataset, the final model was selected, and its predictions replaced the semi-automatic segmentation in the current approach. Implant displacement was quantified by the rotation about the screw-axis, maximum total point motion, and mean target registration error. The displacement parameters of the proposed approach showed a statistically significant difference between fixed and loose samples in a cadaver dataset, as well as between asymptomatic and loose samples in a patient dataset, similar to the outcomes of the current approach. The methodological error calculated on a reproducibility dataset showed values that were not statistically significant different between the two approaches. The results of the proposed and current approaches showed excellent reliability for one and three operators on two datasets. The conclusion is that a full automation in knee implant displacement assessment is feasible by utilizing a DL-based segmentation model while maintaining the capability of distinguishing between fixed and loose implants.

Deep learning-based medical image segmentation faces significant challenges arising from limited labeled data and domain shifts. While prior approaches have primarily addressed these issues independently, their simultaneous occurrence is common in medical imaging. A method that generalizes to unseen domains using only minimal annotations offers significant practical value due to reduced data annotation and development costs. In pursuit of this goal, we propose FSDA-DG, a novel solution to improve cross-domain generalizability of medical image segmentation with few single-source domain annotations. Specifically, our approach introduces semantics-guided semi-supervised data augmentation. This method divides images into global broad regions and semantics-guided local regions, and applies distinct augmentation strategies to enrich data distribution. Within this framework, both labeled and unlabeled data are transformed into extensive domain knowledge while preserving domain-invariant semantic information. Additionally, FSDA-DG employs a multi-decoder U-Net pipeline semi-supervised learning (SSL) network to improve domain-invariant representation learning through consistent prior assumption across multiple perturbations. By integrating data-level and model-level designs, FSDA-DG achieves superior performance compared to state-of-the-art methods in two challenging single domain generalization (SDG) tasks with limited annotations. The code is publicly available at https://github.com/yezanting/FSDA-DG.

Brain tumor (BT) is a rapid growth of brain cells. If the BT is not identified and treated in the first stage, it could cause death. Despite several methods and efforts being developed for segmenting and identifying BT, the detection of BT is complicated due to the distinct position of the tumor and its size. To solve such issues, this paper proposes the Conditional Autoregressive Value-at-Risk_Xception Maxout-Network (Caviar_XM-Net) for BT detection utilizing magnetic resonance imaging (MRI) images. The input MRI image gathered from the dataset is denoised using the adaptive bilateral filter (ABF), and tumor region segmentation is done using BFC-MRFNet-RVSeg. Here, the segmentation is done by the Bayesian fuzzy clustering (BFC) and multi-branch residual fusion network (MRF-Net) separately. Subsequently, outputs from both segmentation techniques are combined using the RV coefficient. Image augmentation is performed to boost the quantity of images in the training process. Afterwards, feature extraction is done, where features, like local optimal oriented pattern (LOOP), convolutional neural network (CNN) features, median binary pattern (MBP) with statistical features, and local Gabor XOR pattern (LGXP), are extracted. Lastly, BT detection is carried out by employing Caviar_XM-Net, which is acquired by the assimilation of the Xception model and deep Maxout network (DMN) with the CAViaR approach. Furthermore, the effectiveness of Caviar_XM-Net is examined using the parameters, namely sensitivity, accuracy, specificity, precision, and F1-score, and the corresponding values of 91.59%, 91.36%, 90.83%, 90.99%, and 91.29% are attained. Hence, the Caviar_XM-Net performs better than the traditional methods with high efficiency.

Epicardial adipose tissue represents a metabolically active visceral fat depot that is in direct contact with the left ventricular myocardium. While it is associated with coronary artery disease, little is known regarding its role in aortic stenosis. We sought to investigate the association of epicardial adipose tissue with aortic stenosis severity and progression, myocardial remodelling and function, and mortality in asymptomatic patients with aortic stenosis. In a post hoc analysis of 124 patients with asymptomatic mild-to-severe aortic stenosis participating in a prospective clinical trial, baseline epicardial adipose tissue was quantified on CT angiography using fully automated deep learning-enabled software. Aortic stenosis disease severity was assessed at baseline and 1 year. The primary endpoint was all-cause mortality. Neither epicardial adipose tissue volume nor attenuation correlated with aortic stenosis severity or subsequent disease progression as assessed by echocardiography or CT (p>0.05 for all). Epicardial adipose tissue volume correlated with plasma cardiac troponin concentration (r=0.23, p=0.009), left ventricular mass (r=0.46, p<0.001), ejection fraction (r=-0.28, p=0.002), global longitudinal strain (r=0.28, p=0.017), and left atrial volume (r=0.39, p<0.001). During the median follow-up of 48 (IQR 26-73) months, a total of 23 (18%) patients died. In multivariable analysis, both epicardial adipose tissue volume (HR 1.82, 95% CI 1.10 to 3.03; p=0.021) and plasma cardiac troponin concentration (HR 1.47, 95% CI 1.13 to 1.90; p=0.004) were associated with all-cause mortality, after adjustment for age, body mass index and left ventricular ejection fraction. Patients with epicardial adipose tissue volume >90 mm³ had 3-4 times higher risk of death (adjusted HR 3.74, 95% CI 1.08 to 12.96; p=0.037). Epicardial adipose tissue volume does not associate with aortic stenosis severity or its progression but does correlate with blood and imaging biomarkers of impaired myocardial health. The latter may explain the association of epicardial adipose tissue volume with an increased risk of all-cause mortality in patients with asymptomatic aortic stenosis. gov (NCT02132026).

Deep learning-based semantic segmentation approaches provide an efficient and automated means for cancer diagnosis and monitoring, which is important in clinical applications. However, implementing these approaches outside the experimental environment and using them in real-world applications requires powerful and adequate hardware resources, which are not available in most hospitals, especially in low- and middle-income countries. Consequently, clinical settings will never use most of these algorithms, or at best, their adoption will be relatively limited. To address these issues, some approaches that reduce computational costs were proposed, but they performed poorly and failed to produce satisfactory results. Therefore, finding a method that overcomes these limitations without losing performance is highly challenging. To face this challenge, our study proposes a novel, optimal convolutional neural network-based approach for medical image segmentation that consists of multiple synthesis and analysis paths connected through a series of long skip connections. The design leverages multi-scale convolution, multi-scale feature extraction, downsampling strategies, and feature map fusion methods, all of which have proven effective in enhancing performance. This framework was extensively evaluated against current state-of-the-art architectures on various medical image segmentation tasks, including lung tumors, spleen, and pancreatic tumors. The results of these experiments conclusively demonstrate the efficacy of the proposed approach in outperforming existing state-of-the-art methods across multiple evaluation metrics. This superiority is further enhanced by the framework's ability to minimize the computational complexity and decrease the number of parameters required, resulting in greater segmentation accuracy, faster processing, and better implementation efficiency.

The accurate segmentation of myocardial scars from cardiac MRI is essential for clinical assessment and treatment planning. In this study, we propose a robust deep-learning pipeline for fully automated myocardial scar detection and segmentation by fine-tuning state-of-the-art models. The method explicitly addresses challenges of label noise from semi-automatic annotations, data heterogeneity, and class imbalance through the use of Kullback-Leibler loss and extensive data augmentation. We evaluate the model's performance on both acute and chronic cases and demonstrate its ability to produce accurate and smooth segmentations despite noisy labels. In particular, our approach outperforms state-of-the-art models like nnU-Net and shows strong generalizability in an out-of-distribution test set, highlighting its robustness across various imaging conditions and clinical tasks. These results establish a reliable foundation for automated myocardial scar quantification and support the broader clinical adoption of deep learning in cardiac imaging.

Medical imaging datasets often contain heterogeneous biases ranging from erroneous labels to inconsistent labeling styles. Such biases can negatively impact deep segmentation networks performance. Yet, the identification and characterization of such biases is a particularly tedious and challenging task. In this paper, we introduce HyperSORT, a framework using a hyper-network predicting UNets' parameters from latent vectors representing both the image and annotation variability. The hyper-network parameters and the latent vector collection corresponding to each data sample from the training set are jointly learned. Hence, instead of optimizing a single neural network to fit a dataset, HyperSORT learns a complex distribution of UNet parameters where low density areas can capture noise-specific patterns while larger modes robustly segment organs in differentiated but meaningful manners. We validate our method on two 3D abdominal CT public datasets: first a synthetically perturbed version of the AMOS dataset, and TotalSegmentator, a large scale dataset containing real unknown biases and errors. Our experiments show that HyperSORT creates a structured mapping of the dataset allowing the identification of relevant systematic biases and erroneous samples. Latent space clusters yield UNet parameters performing the segmentation task in accordance with the underlying learned systematic bias. The code and our analysis of the TotalSegmentator dataset are made available: https://github.com/ImFusionGmbH/HyperSORT

The C-arm biplane imaging system, designed for cerebral angiography, detects pathologies like aneurysms using dual rotating detectors for high-precision, real-time vascular imaging. However, accuracy can be affected by source-detector trajectory deviations caused by gravitational artifacts and mechanical instabilities. This study addresses calibration challenges and suggests leveraging interventional devices with radio-opaque markers to optimize C-arm geometry. We propose an online calibration method using image-specific features derived from interventional devices like guidewires and catheters (In the remainder of this paper, the term"catheter" will refer to both catheter and guidewire). The process begins with gantry-recorded data, refined through iterative nonlinear optimization. A machine learning approach detects and segments elongated devices by identifying candidates via thresholding on a weighted sum of curvature, derivative, and high-frequency indicators. An ensemble classifier segments these regions, followed by post-processing to remove false positives, integrating vessel maps, manual correction and identification markers. An interpolation step filling gaps along the catheter. Among the optimized ensemble classifiers, the one trained on the first frames achieved the best performance, with a specificity of 99.43% and precision of 86.41%. The calibration method was evaluated on three clinical datasets and four phantom angiogram pairs, reducing the mean backprojection error from 4.11 ± 2.61 to 0.15 ± 0.01 mm. Additionally, 3D accuracy analysis showed an average root mean square error of 3.47% relative to the true marker distance. This study explores using interventional tools with radio-opaque markers for C-arm self-calibration. The proposed method significantly reduces 2D backprojection error and 3D RMSE, enabling accurate 3D vascular reconstruction.

Magnetic resonance imaging (MRI) is a crucial tool for visualizing orbital structures and detecting eye pathologies. However, manual segmentation of orbital anatomy is challenging due to the complexity and variability of the structures. Recent advancements in deep learning (DL), particularly convolutional neural networks (CNNs), offer promising solutions for automated segmentation in medical imaging. This study aimed to train and evaluate a U-Net-based model for the automated segmentation of key orbital structures. This retrospective study included 117 patients with various orbital pathologies who underwent orbital MRI. Manual segmentation was performed on four anatomical structures: the ocular bulb, ocular tumors, retinal detachment, and the optic nerve. Following the UNet autoconfiguration by nnUNet, we conducted a five-fold cross-validation and evaluated the model's performances using Dice Similarity Coefficient (DSC) and Relative Absolute Volume Difference (RAVD) as metrics. nnU-Net achieved high segmentation performance for the ocular bulb (mean DSC: 0.931) and the optic nerve (mean DSC: 0.820). Segmentation of ocular tumors (mean DSC: 0.788) and retinal detachment (mean DSC: 0.550) showed greater variability, with performance declining in more challenging cases. Despite these challenges, the model achieved high detection rates, with ROC AUCs of 0.90 for ocular tumors and 0.78 for retinal detachment. This study demonstrates nnU-Net's capability for accurate segmentation of orbital structures, particularly the ocular bulb and optic nerve. However, challenges remain in the segmentation of tumors and retinal detachment due to variability and artifacts. Future improvements in deep learning models and broader, more diverse datasets may enhance segmentation performance, ultimately aiding in the diagnosis and treatment of orbital pathologies.