Annotation variability remains a substantial challenge in medical image segmentation, stemming from ambiguous imaging boundaries and diverse clinical expertise. Traditional deep learning methods producing single deterministic segmentation predictions often fail to capture these annotator biases. Although recent studies have explored multi-rater segmentation, existing methods typically focus on a single perspective -- either generating a probabilistic ``gold standard'' consensus or preserving expert-specific preferences -- thus struggling to provide a more omni view. In this study, we propose DiffOSeg, a two-stage diffusion-based framework, which aims to simultaneously achieve both consensus-driven (combining all experts' opinions) and preference-driven (reflecting experts' individual assessments) segmentation. Stage I establishes population consensus through a probabilistic consensus strategy, while Stage II captures expert-specific preference via adaptive prompts. Demonstrated on two public datasets (LIDC-IDRI and NPC-170), our model outperforms existing state-of-the-art methods across all evaluated metrics. Source code is available at https://github.com/string-ellipses/DiffOSeg .

Accurate segmentation of orbital bones in facial computed tomography (CT) images is essential for the creation of customized implants for reconstruction of defected orbital bones, particularly challenging due to the ambiguous boundaries and thin structures such as the orbital medial wall and orbital floor. In these ambiguous regions, existing segmentation approaches often output disconnected or under-segmented results. We propose a novel framework that corrects segmentation results by leveraging consensus from multiple diffusion model outputs. Our approach employs a conditional Bernoulli diffusion model trained on diverse annotation patterns per image to generate multiple plausible segmentations, followed by a consensus-driven correction that incorporates position proximity, consensus level, and gradient direction similarity to correct challenging regions. Experimental results demonstrate that our method outperforms existing methods, significantly improving recall in ambiguous regions while preserving the continuity of thin structures. Furthermore, our method automates the manual process of segmentation result correction and can be applied to image-guided surgical planning and surgery.

Fetal lung maturity is a critical indicator for predicting neonatal outcomes and the need for post-natal intervention, especially for pregnancies affected by fetal growth restriction. Intra-voxel incoherent motion analysis has shown promising results for non-invasive assessment of fetal lung development, but its reliance on manual segmentation is time-consuming, thus limiting its clinical applicability. In this work, we present an automated lung maturity evaluation pipeline for diffusion-weighted magnetic resonance images that consists of a deep learning-based fetal lung segmentation model and a model-fitting lung maturity assessment. A 3D nnU-Net model was trained on manually segmented images selected from the baseline frames of 4D diffusion-weighted MRI scans. The segmentation model demonstrated robust performance, yielding a mean Dice coefficient of 82.14%. Next, voxel-wise model fitting was performed based on both the nnU-Net-predicted and manual lung segmentations to quantify IVIM parameters reflecting tissue microstructure and perfusion. The results suggested no differences between the two. Our work shows that a fully automated pipeline is possible for supporting fetal lung maturity assessment and clinical decision-making.

Precise liver segmentation is critical for accurate diagnosis and effective treatment planning, serving as a foundation for medical image analysis. However, existing methods struggle with limited labeled data, poor generalizability, and insufficient integration of anatomical and clinical features. To address these limitations, we propose a novel Few-Shot Segmentation model with Unified Liver Representation (FSS-ULivR), which employs a ResNet-based encoder enhanced with Squeeze-and-Excitation modules to improve feature learning, an enhanced prototype module that utilizes a transformer block and channel attention for dynamic feature refinement, and a decoder with improved attention gates and residual refinement strategies to recover spatial details from encoder skip connections. Through extensive experiments, our FSS-ULivR model achieved an outstanding Dice coefficient of 98.94%, Intersection over Union (IoU) of 97.44% and a specificity of 93.78% on the Liver Tumor Segmentation Challenge dataset. Cross-dataset evaluations further demonstrated its generalizability, with Dice scores of 95.43%, 92.98%, 90.72%, and 94.05% on 3DIRCADB01, Colorectal Liver Metastases, Computed Tomography Organs (CT-ORG), and Medical Segmentation Decathlon Task 3: Liver datasets, respectively. In multi-organ segmentation on CT-ORG, it delivered Dice scores ranging from 85.93% to 94.26% across bladder, bones, kidneys, and lungs. For brain tumor segmentation on BraTS 2019 and 2020 datasets, average Dice scores were 90.64% and 89.36% across whole tumor, tumor core, and enhancing tumor regions. These results emphasize the clinical importance of our model by demonstrating its ability to deliver precise and reliable segmentation through artificial intelligence techniques and engineering solutions, even in scenarios with scarce annotated data.

B-ultrasound results are widely used in early pregnancy loss (EPL) prediction, but there are inevitable intra-observer and inter-observer errors in B-ultrasound results especially in early pregnancy, which lead to inconsistent assessment of embryonic status, and thus affect the judgment of EPL. To address this, we need a rapid and accurate model to predict pregnancy loss in the first trimester. This study aimed to construct an artificial intelligence model to automatically extract biometric parameters from ultrasound videos of early embryos and predict pregnancy loss. This can effectively eliminate the measurement error of B-ultrasound results, accurately predict EPL, and provide decision support for doctors with relatively little clinical experience. A total of 630 ultrasound videos from women with early singleton pregnancies of gestational age between 6 and 10 weeks were used for training. A two-stage artificial intelligence model was established. First, some biometric parameters such as gestational sac areas (GSA), yolk sac diameter (YSD), crown rump length (CRL) and fetal heart rate (FHR), were extract from ultrasound videos by a deep neural network named A3F-net, which is a modified neural network based on U-Net designed by ourselves. Then an ensemble learning model predicted pregnancy loss risk based on these features. Dice, IOU and Precision were used to evaluate the measurement results, and sensitivity, AUC etc. were used to evaluate the predict results. The fetal heart rate was compared with those measured by doctors, and the accuracy of results was compared with other AI models. In the biometric features measurement stage, the precision of GSA, YSD and CRL of A3F-net were 98.64%, 96.94% and 92.83%, it was the highest compared to other 2 models. Bland-Altman analysis did not show systematic deviations between doctors and AI. The mean and standard deviation of the mean relative error between doctors and the AI model was 0.060 ± 0.057. In the EPL prediction stage, the ensemble learning models demonstrated excellent performance, with CatBoost being the best-performing model, achieving a precision of 98.0% and an AUC of 0.969 (95% CI: 0.962-0.975). In this study, a hybrid AI model to predict EPL was established. First, a deep neural network automatically measured the biometric parameters from ultrasound video to ensure the consistency and accuracy of the measurements, then a machine learning model predicted EPL risk to support doctors making decisions. The use of our established AI model in EPL prediction has the potential to assist physicians in making more accurate and timely clinical decision in clinical application.

Accurate X-ray Computed tomography (CT) image segmentation of the abdominal organs is fundamental for diagnosing abdominal diseases, planning cancer treatment, and formulating radiotherapy strategies. However, the existing deep learning based models for three-dimensional (3D) CT image abdominal multi-organ segmentation face challenges, including complex organ distribution, scarcity of labeled data, and diversity of organ structures, leading to difficulties in model training and convergence and low segmentation accuracy. To address these issues, a novel multi-stage training and a deep supervision model based segmentation approach is proposed. It primary integrates multi-stage training, pseudo- labeling technique, and a developed deep supervision model with attention mechanism (DLAU-Net), specifically designed for 3D abdominal multi-organ segmentation. The DLAU-Net enhances segmentation performance and model adaptability through an improved network architecture. The multi-stage training strategy accelerates model convergence and enhances generalizability, effectively addressing the diversity of abdominal organ structures. The introduction of pseudo-labeling training alleviates the bottleneck of labeled data scarcity and further improves the model's generalization performance and training efficiency. Experiments were conducted on a large dataset provided by the FLARE 2023 Challenge. Comprehensive ablation studies and comparative experiments were conducted to validate the effectiveness of the proposed method. Our method achieves an average organ accuracy (AVG) of 90.5% and a Dice Similarity Coefficient (DSC) of 89.05% and exhibits exceptional performance in terms of training speed and handling data diversity, particularly in the segmentation tasks of critical abdominal organs such as the liver, spleen, and kidneys, significantly outperforming existing comparative methods.

To develop a robust group-level brain parcellation method using deep learning based on resting-state functional magnetic resonance imaging (rs-fMRI), aiming to release the model assumptions made by previous approaches. We proposed Brain Deep Embedded Clustering (BDEC), a deep clustering model that employs a loss function designed to maximize inter-class separation and enhance intra-class similarity, thereby promoting the formation of functionally coherent brain regions. Compared to ten widely used brain parcellation methods, the BDEC model demonstrates significantly improved performance in various functional homogeneity metrics. It also showed favorable results in parcellation validity, downstream tasks, task inhomogeneity, and generalization capability. The BDEC model effectively captures intrinsic functional properties of the brain, supporting reliable and generalizable parcellation outcomes. BDEC provides a useful parcellation for brain network analysis and dimensionality reduction of rs-fMRI data, while also contributing to a deeper understanding of the brain's functional organization.

Vascular aging is an important phenotype characterized by structural and geometric remodeling. Some individuals exhibit supernormal vascular aging, associated with improved cardiovascular outcomes; others experience early vascular aging, linked to adverse cardiovascular outcomes. The aorta is the artery that exhibits the most prominent age-related changes; however, the biological mechanisms underlying aortic aging, its genetic architecture, and its relationship with cardiovascular structure, function, and disease states remain poorly understood. We developed sex-specific models to quantify aortic age on the basis of aortic geometric phenotypes derived from 3-dimensional tomographic imaging data in 2 large biobanks: the UK Biobank and the Penn Medicine BioBank. Convolutional neural ne2rk-assisted 3-dimensional segmentation of the aorta was performed in 56 104 magnetic resonance imaging scans in the UK Biobank and 6757 computed tomography scans in the Penn Medicine BioBank. Aortic vascular age index (AVAI) was calculated as the difference between the vascular age predicted from geometric phenotypes and the chronological age, expressed as a percent of chronological age. We assessed associations with cardiovascular structure and function using multivariate linear regression and examined the genetic architecture of AVAI through genome-wide association studies, followed by Mendelian randomization to assess causal associations. We also constructed a polygenic risk score for AVAI. AVAI displayed numerous associations with cardiac structure and function, including increased left ventricular mass (standardized β=0.144 [95% CI, 0.138, 0.149]; <i>P</i><0.0001), wall thickness (standardized β=0.061 [95% CI, 0.054, 0.068]; <i>P</i><0.0001), and left atrial volume maximum (standardized β=0.060 [95% CI, 0.050, 0.069]; <i>P</i><0.0001). AVAI exhibited high genetic heritability (<i>h</i>²=40.24%). We identified 54 independent genetic loci (<i>P</i><5×10^-⁸) associated with AVAI, which further exhibited gene-level associations with the fibrillin-1 (<i>FBN1</i>) and elastin (<i>ELN1</i>) genes. Mendelian randomization supported causal associations between AVAI and atrial fibrillation, vascular dementia, aortic aneurysm, and aortic dissection. A polygenic risk score for AVAI was associated with an increased prevalence of atrial fibrillation, hypertension, aortic aneurysm, and aortic dissection. Early aortic aging is significantly associated with adverse cardiac remodeling and important cardiovascular disease states. AVAI exhibits a polygenic, highly heritable genetic architecture. Mendelian randomization analyses support a causal association between AVAI and cardiovascular diseases, including atrial fibrillation, vascular dementia, aortic aneurysms, and aortic dissection.

Propose and evaluate an automatic approach for predicting the optimal inversion time (TI) for dark and gray blood images for flow-independent dark-blood delayed-enhancement (FIDDLE) acquisition based on free-breathing FIDDLE TI-scout images. In 267 patients, the TI-scout sequence acquired single-shot magnetization-prepared and associated reference images (without preparation) on a 3 T Magnetom Vida and a 1.5 T Magnetom Sola scanner. Data were reconstructed into phase-corrected TI-scout images typically covering TIs from 140 to 440 ms (20 ms increment). A deep learning network was trained to segment the myocardium and blood pool in reference images. These segmentation masks were transferred to the TI-scout images to derive intensity features of myocardium and blood, with which T₁-recovery curves were determined by logarithmic fitting. The optimal TI for dark and gray blood images were derived as linear functions of the TI in which both T₁-curves cross. This TI-prediction pipeline was evaluated in 64 clinical subjects. The pipeline predicted optimal TIs with an average error less than 10 ms compared to manually annotated optimal TIs. The presented approach reliably and automatically predicted optimal TI for dark and gray blood FIDDLE acquisition, with an average error less than the TI increment of the FIDDLE TI-scout sequence.

Cortical lesions (CLs) have emerged as valuable biomarkers in multiple sclerosis (MS), offering high diagnostic specificity and prognostic relevance. However, their routine clinical integration remains limited due to subtle magnetic resonance imaging (MRI) appearance, challenges in expert annotation, and a lack of standardized automated methods. We propose a comprehensive multi-centric benchmark of CL detection and segmentation in MRI. A total of 656 MRI scans, including clinical trial and research data from four institutions, were acquired at 3T and 7T using MP2RAGE and MPRAGE sequences with expert-consensus annotations. We rely on the self-configuring nnU-Net framework, designed for medical imaging segmentation, and propose adaptations tailored to the improved CL detection. We evaluated model generalization through out-of-distribution testing, demonstrating strong lesion detection capabilities with an F1-score of 0.64 and 0.5 in and out of the domain, respectively. We also analyze internal model features and model errors for a better understanding of AI decision-making. Our study examines how data variability, lesion ambiguity, and protocol differences impact model performance, offering future recommendations to address these barriers to clinical adoption. To reinforce the reproducibility, the implementation and models will be publicly accessible and ready to use at https://github.com/Medical-Image-Analysis-Laboratory/ and https://doi.org/10.5281/zenodo.15911797.