The choroid plexus (ChP) plays a pivotal role in inflammatory processes that occur in multiple sclerosis (MS). The enlargement of the ChP in relapsing-remitting multiple sclerosis (RRMS) is considered to be an indication of disease activity and has been associated with periventricular remyelination failure. This cross-sectional study aimed to identify the relationship between ChP and periventricular tissue damage which occurs in MS, and to elucidate the role of neuroinflammation in primary progressive multiple sclerosis (PPMS). ChP volume was assessed by a novel deep learning segmentation method based on structural MRI data acquired from two centers. In total, 141 RRMS and 64 PPMS patients were included, along with 75 healthy control subjects. In addition, T1w/FLAIR ratios were calculated within periventricular bands to quantify microstructural tissue damage and to assess its relationship to ChP volume. When compared to healthy controls, ChP volumes were significantly increased in RRMS, but not in patients with PPMS. T1w/FLAIR ratios in the normal appearing white matter (NAWM) showing periventricular gradients were decreased in patients with multiple sclerosis when compared to healthy control subjects and lower T1w/FLAIR ratios radiating out from the lateral ventricles were found in patients with PPMS. A relationship between ChP volume and T1w/FLAIR ratio in NAWM was found within the inner periventricular bands in RRMS patients. A longer duration of disease was associated with larger ChP volumes only in RRMS patients. Enlarged ChP volumes were also significantly associated with reduced cortex volumes and increased lesion volumes in RRMS. Our analysis confirmed that the ChP was significantly enlarged in patients with RRMS, which was related to brain lesion volumes and which suggested a dynamic development as it was associated with disease duration. Plexus enlargement was further associated with periventricular demyelination or tissue damage assessed by T1w/FLAIR ratios in RRMS. Furthermore, we did not find an enlargement of the ChP in patients with PPMS, possibly indicating the reduced involvement of inflammatory processes in the progressive phase of MS. The association between enlarged ChP volumes and cortical atrophy in RRMS highlighted the vulnerability of structures close to the CSF.

Uncertainty assessment of deep learning autosegmentation (DLAS) models can support contour corrections in adaptive radiotherapy (ART), e.g. by utilizing Monte Carlo Dropout (MCD) uncertainty maps. However, poorly calibrated uncertainties at the patient level often render these clinically nonviable. We evaluated population-based and patient-specific DLAS accuracy and uncertainty calibration and propose a patient-specific post-training uncertainty calibration method for DLAS in ART.&#xD;&#xD;Approach. The study included 122 lung cancer patients treated with a low-field MR-linac (80/19/23 training/validation/test cases). Ten single-label 3D-U-Net population-based baseline models (BM) were trained with dropout using planning MRIs (pMRIs) and contours for nine organs-at-riks (OARs) and gross tumor volumes (GTVs). Patient-specific models (PS) were created by fine-tuning BMs with each test patient's pMRI. Model uncertainty was assessed with MCD, averaged into probability maps. Uncertainty calibration was evaluated with reliability diagrams and expected calibration error (ECE). A proposed post-training calibration method rescaled MCD probabilities for fraction images in BM (calBM) and PS (calPS) after fitting reliability diagrams from pMRIs. All models were evaluated on fraction images using Dice similarity coefficient (DSC), 95th percentile Hausdorff distance (HD95) and ECE. Metrics were compared among models for all OARs combined (n=163), and the GTV (n=23), using Friedman and posthoc-Nemenyi tests (α=0.05).&#xD;&#xD;Main results. For the OARs, patient-specific fine-tuning significantly (p<0.001) increased median DSC from 0.78 (BM) to 0.86 (PS) and reduced HD95 from 14mm (BM) to 6.0mm (PS). Uncertainty calibration achieved substantial reductions in ECE, from 0.25 (BM) to 0.091 (calBM) and 0.22 (PS) to 0.11 (calPS) (p<0.001), without significantly affecting DSC or HD95 (p>0.05). For the GTV, BM performance was poor (DSC=0.05) but significantly (p<0.001) improved with PS training (DSC=0.75) while uncertainty calibration reduced ECE from 0.22 (PS) to 0.15 (calPS) (p=0.45).&#xD;&#xD;Significance. Post-training uncertainty calibration yields geometrically accurate DLAS models with well-calibrated uncertainty estimates, crucial for ART applications.

Thoracolumbar stump ribs are one of the essential indicators of thoracolumbar transitional vertebrae or enumeration anomalies. While some studies manually assess these anomalies and describe the ribs qualitatively, this study aims to automate thoracolumbar stump rib detection and analyze their morphology quantitatively. To this end, we train a high-resolution deep-learning model for rib segmentation and show significant improvements compared to existing models (Dice score 0.997 vs. 0.779, p-value < 0.01). In addition, we use an iterative algorithm and piece-wise linear interpolation to assess the length of the ribs, showing a success rate of 98.2%. When analyzing morphological features, we show that stump ribs articulate more posteriorly at the vertebrae (-19.2 +- 3.8 vs -13.8 +- 2.5, p-value < 0.01), are thinner (260.6 +- 103.4 vs. 563.6 +- 127.1, p-value < 0.01), and are oriented more downwards and sideways within the first centimeters in contrast to full-length ribs. We show that with partially visible ribs, these features can achieve an F1-score of 0.84 in differentiating stump ribs from regular ones. We publish the model weights and masks for public use.

To develop a new high-resolution (HR)CT abnormalities quantification tool (CVILDES) for interstitial lung diseases (ILDs) based on the nnU-Net network structure and to determine whether the quantitative parameters derived from this new software could offer a reliable and precise assessment in a clinical setting that is in line with expert visual evaluation. HRCT scans from 83 cases of ILDs and 20 cases of other diffuse lung diseases were labeled section by section by multiple radiologists and were used as training data for developing a deep learning model based on nnU-Net, employing a supervised learning approach. For clinical validation, a cohort including 51 cases of interstitial pneumonia with autoimmune features (IPAF) and 14 cases of idiopathic pulmonary fibrosis (IPF) had CT parenchymal patterns evaluated quantitatively with CVILDES and by visual evaluation. Subsequently, we assessed the correlation of the two methodologies for ILD features quantification. Furthermore, the correlation between the quantitative results derived from the two methods and pulmonary function parameters (DL_CO%, FVC%, and FEV%) was compared. All CT data were successfully quantified using CVILDES. CVILDES-quantified results (total ILD extent, ground-glass opacity, consolidation, reticular pattern and honeycombing) showed a strong correlation with visual evaluation and were numerically close to the visual evaluation results (r = 0.64-0.89, p < 0.0001), particularly for the extent of fibrosis (r = 0.82, p < 0.0001). As judged by correlation with pulmonary function parameters, CVILDES quantification was comparable or even superior to visual evaluation. nnU-Net-based CVILDES was comparable to visual evaluation for ILD abnormalities quantification. Question Visual assessment of ILD on HRCT is time-consuming and exhibits poor inter-observer agreement, making it challenging to accurately evaluate the therapeutic efficacy. Findings nnU-Net-based Computer vision-based ILD evaluation system (CVILDES) accurately segmented and quantified the HRCT features of ILD, and results were comparable to visual evaluation. Clinical relevance This study developed a new tool that has the potential to be applied in the quantitative assessment of ILD.

Orthodontically induced root resorption (OIRR) is difficult to assess accurately using traditional 2D imaging due to distortion and low sensitivity. While CBCT offers more precise 3D evaluation, manual segmentation remains labor-intensive and prone to variability. Recent advances in deep learning enable automatic, accurate tooth segmentation from CBCT images. This study applies deep learning and CBCT technology to quantify OIRR and analyze its risk factors, aiming to improve assessment accuracy, efficiency, and clinical decision-making. This study retrospectively analyzed CBCT scans of 108 orthodontic patients to assess OIRR using deep learning-based tooth segmentation and volumetric analysis. Statistical analysis was performed using linear regression to evaluate the influence of patient-related factors. A significance level of p < 0.05 was considered statistically significant. Root volume significantly decreased after orthodontic treatment (p < 0.001). Age, gender, open (deep) bite, severe crowding, and other factors significantly influenced root resorption rates in different tooth positions. Multivariable regression analysis showed these factors can predict root resorption, explaining 3% to 15.4% of the variance. This study applied a deep learning model to accurately assess root volume changes using CBCT, revealing significant root volume reduction after orthodontic treatment. It found that underage patients experienced less root resorption, while factors like anterior open bite and deep overbite influenced resorption in specific teeth, though skeletal pattern, overjet, and underbite were not significant predictors.

Cardiovascular diseases are the number one cause of death globally, making cardiac magnetic resonance image segmentation a popular research topic. Existing schemas relying on manual user interaction or semi-automatic segmentation are infeasible when dealing thousands of cardiac MRI studies. Thus, we proposed a full automatic and robust algorithm for large-scale cardiac MRI segmentation by combining the advantages of deep learning localization and 3D-ASM restriction. The proposed method comprises several key techniques: 1) a hybrid network integrating CNNs and Transformer as a encoder with the EFG (Edge feature guidance) module (named as CTr-HNs) to localize the target regions of the cardiac on MRI images, 2) initial shape acquisition by alignment of coarse segmentation contours to the initial surface model of 3D-ASM, 3) refinement of the initial shape to cover all slices of MRI in the short axis by complex transformation. The datasets used are from the UK BioBank and the CAP (Cardiac Atlas Project). In cardiac coarse segmentation experiments on MR images, Dice coefficients (Dice), mean contour distances (MCD), and mean Hausdorff distances (HD95) are used to evaluate segmentation performance. In SPASM experiments, Point-to-surface (P2S) distances, Dice score are compared between automatic results and ground truth. The CTr-HNs from our proposed method achieves Dice coefficients (Dice), mean contour distances (MCD), and mean Hausdorff distances (HD95) of 0.95, 0.10 and 1.54 for the LV segmentation respectively, 0.88, 0.13 and 1.94 for the LV myocardium segmentation, and 0.91, 0.24 and 3.25 for the RV segmentation. The overall P2S errors from our proposed schema is 1.45 mm. For endocardium and epicardium, the Dice scores are 0.87 and 0.91 respectively. Our experimental results show that the proposed schema can automatically analyze large-scale quantification from population cardiac images with robustness and accuracy.

Our study presents PNN-UNet as a method for constructing deep neural networks that replicate the planarian neural network (PNN) structure in the context of 3D medical image data. Planarians typically have a cerebral structure comprising two neural cords, where the cerebrum acts as a coordinator, and the neural cords serve slightly different purposes within the organism's neurological system. Accordingly, PNN-UNet comprises a Deep-UNet and a Wide-UNet as the nerve cords, with a densely connected autoencoder performing the role of the brain. This distinct architecture offers advantages over both monolithic (UNet) and modular networks (Ensemble-UNet). Our outcomes on a 3D MRI hippocampus dataset, with and without data augmentation, demonstrate that PNN-UNet outperforms the baseline UNet and several other UNet variants in image segmentation.

Coronary artery disease (CAD) is highly prevalent among pilots due to the nature of their lifestyle, and occupational stresses. CAD is one the most common conditions affecting pilots' medical certification and is frequently nondisclosed by pilots fearing the loss of their certification. Traditional screening methods, such as resting electrocardiograms (EKGs) and functional stress tests, have limitations, especially in detecting non-obstructive CAD. Recent advances in cardiac imaging are challenging the current paradigms of CAD screening and risk assessment protocols, offering tools uniquely suited to address the occupational health challenges faced by pilots. Coronary artery calcium scoring (CACS) has proven valuable in refining risk stratification in asymptomatic individuals. Coronary computed tomography angiography (CCTA), is being increasingly adopted as a superior tool for ruling out CAD in symptomatic individuals, assessing plaque burden as well as morphologically identifying vulnerable plaque. CT-derived fractional flow reserve (CT-FFR) adds a physiologic component to the anatomical prowess of CCTA. Cardiac magnetic resonance imaging (CMR) is now used as a prognosticating tool following a coronary event as well as a stress testing modality. Investigational technologies like pericoronary fat attenuation and artificial intelligence (AI)-enabled plaque quantification hold the promise of enhancing diagnostic accuracy and risk stratification. This review highlights the interplay between occupational demands, regulatory considerations, and the limitations of the traditional modalities for pilot CAD screening and surveillance. We also discuss the potential role of the recent advances in cardiac imaging in optimizing pilot health and flight safety.

The rapid evolution of deep learning has dramatically enhanced the field of medical image segmentation, leading to the development of models with unprecedented accuracy in analyzing complex medical images. Deep learning-based segmentation holds significant promise for advancing clinical care and enhancing the precision of medical interventions. However, these models' high computational demand and complexity present significant barriers to their application in resource-constrained clinical settings. To address this challenge, we introduce Teach-Former, a novel knowledge distillation (KD) framework that leverages a Transformer backbone to effectively condense the knowledge of multiple teacher models into a single, streamlined student model. Moreover, it excels in the contextual and spatial interpretation of relationships across multimodal images for more accurate and precise segmentation. Teach-Former stands out by harnessing multimodal inputs (CT, PET, MRI) and distilling the final predictions and the intermediate attention maps, ensuring a richer spatial and contextual knowledge transfer. Through this technique, the student model inherits the capacity for fine segmentation while operating with a significantly reduced parameter set and computational footprint. Additionally, introducing a novel training strategy optimizes knowledge transfer, ensuring the student model captures the intricate mapping of features essential for high-fidelity segmentation. The efficacy of Teach-Former has been effectively tested on two extensive multimodal datasets, HECKTOR21 and PI-CAI22, encompassing various image types. The results demonstrate that our KD strategy reduces the model complexity and surpasses existing state-of-the-art methods to achieve superior performance. The findings of this study indicate that the proposed methodology could facilitate efficient segmentation of complex multimodal medical images, supporting clinicians in achieving more precise diagnoses and comprehensive monitoring of pathological conditions ( https://github.com/FarihaHossain/TeachFormer ).

Accurate measurement of anterior segment parameters is crucial for diagnosing and managing ophthalmic conditions, such as glaucoma, cataracts, and refractive errors. However, traditional clinical measurement methods are often time-consuming, labor-intensive, and susceptible to inaccuracies. With the growing potential of artificial intelligence in ophthalmic diagnostics, this study aims to develop and evaluate a deep learning model capable of automatically extracting key points and precisely measuring multiple clinically significant anterior segment parameters from ultrasound biomicroscopy (UBM) images. These parameters include central corneal thickness (CCT), anterior chamber depth (ACD), pupil diameter (PD), angle-to-angle distance (ATA), sulcus-to-sulcus distance (STS), lens thickness (LT), and crystalline lens rise (CLR). A data set of 716 UBM anterior segment images was collected from Tianjin Medical University Eye Hospital. YOLOv8 was utilized to segment four key anatomical structures: cornea-sclera, anterior chamber, pupil, and iris-ciliary body-thereby enhancing the accuracy of keypoint localization. Only images with intact posterior capsule lentis were selected to create an effective data set for parameter measurement. Ten keypoints were localized across the data set, allowing the calculation of seven essential parameters. Control experiments were conducted to evaluate the impact of segmentation on measurement accuracy, with model predictions compared against clinical gold standards. The segmentation model achieved a mean IoU of 0.8836 and mPA of 0.9795. Following segmentation, the binary classification model attained an mAP of 0.9719, with a precision of 0.9260 and a recall of 0.9615. Keypoint localization exhibited a Euclidean distance error of 58.73 ± 63.04 μm, improving from the pre-segmentation error of 71.57 ± 67.36 μm. Localization mAP was 0.9826, with a precision of 0.9699, a recall of 0.9642 and an FPS of 32.64. In addition, parameter error analysis and Bland-Altman plots demonstrated improved agreement with clinical gold standards after segmentation. This deep learning approach for UBM image segmentation, keypoint localization, and parameter measurement is feasible, enhancing clinical diagnostic efficiency for anterior segment parameters.