Automated segmentation of multiple sclerosis (MS) lesions using multicontrast magnetic resonance (MR) images improves efficiency and reproducibility compared to manual delineation, with deep learning (DL) methods achieving state-of-the-art performance. However, these DL-based methods have yet to simultaneously optimize in-domain accuracy and out-of-domain generalization when trained on a single source with limited data, or their performance has been unsatisfactory. To fill this gap, we propose a method called UNISELF, which achieves high accuracy within a single training domain while demonstrating strong generalizability across multiple out-of-domain test datasets. UNISELF employs a novel test-time self-ensembled lesion fusion to improve segmentation accuracy, and leverages test-time instance normalization (TTIN) of latent features to address domain shifts and missing input contrasts. Trained on the ISBI 2015 longitudinal MS segmentation challenge training dataset, UNISELF ranks among the best-performing methods on the challenge test dataset. Additionally, UNISELF outperforms all benchmark methods trained on the same ISBI training data across diverse out-of-domain test datasets with domain shifts and missing contrasts, including the public MICCAI 2016 and UMCL datasets, as well as a private multisite dataset. These test datasets exhibit domain shifts and/or missing contrasts caused by variations in acquisition protocols, scanner types, and imaging artifacts arising from imperfect acquisition. Our code is available at https://github.com/uponacceptance.

The distance between the mandibular third molar (M3) and the mandibular canal (MC) is a key factor in assessing the risk of injury to the inferior alveolar nerve (IAN). However, existing deep learning systems have not yet been able to accurately quantify the M3-MC distance in 3D space. The aim of this study was to develop and validate a deep learning-based system for accurate measurement of M3-MC spatial relationships in cone-beam computed tomography (CBCT) images and to evaluate its accuracy against conventional methods. We propose an innovative approach for low-resource environments, using DeeplabV3 + for semantic segmentation of CBCT-extracted 2D images, followed by multi-category 3D reconstruction and visualization. Based on the reconstruction model, we applied the KD-Tree algorithm to measure the spatial minimum distance between M3 and MC. Through internal validation with randomly selected CBCT images, we compared the differences between the AI system, conventional measurement methods on the CBCT, and the gold standard measured by senior experts. Statistical analysis was performed using one-way ANOVA with Tukey HSD post-hoc tests (p < 0.05), employing multiple error metrics for comprehensive evaluation. One-way ANOVA revealed significant differences among measurement methods. Subsequent Tukey HSD post-hoc tests showed significant differences between the AI reconstruction model and conventional methods. The measurement accuracy of the AI system compared to the gold standard was 0.19 for mean error (ME), 0.18 for mean absolute error (MAE), 0.69 for mean square error (MSE), 0.83 for root mean square error (RMSE), and 0.96 for coefficient of determination (R²) (p < 0.01). These results indicate that the proposed AI system is highly accurate and reliable in M3-MC distance measurement and provides a powerful tool for preoperative risk assessment of M3 extraction.

Conventional approaches to material decomposition in spectral CT face challenges related to precise algorithm calibration across imaged conditions and low signal quality caused by variable object size and reduced dose. In this proof-of-principle study, a deep learning approach to multi-material decomposition was developed to quantify iodine, gadolinium, and calcium in spectral CT. A dual-phase network architecture was trained using synthetic datasets containing computational models of cylindrical and virtual patient phantoms. Classification and quantification performance was evaluated across a range of patient size and dose parameters. The model was found to accurately classify (accuracy: cylinders - 98%, virtual patients - 97%) and quantify materials (mean absolute percentage difference: cylinders - 8-10%, virtual patients - 10-15%) in both datasets. Performance in virtual patient phantoms improved as the hybrid training dataset included a larger contingent of virtual patient phantoms (accuracy: 48% with 0 virtual patients to 97% with 8 virtual patients). For both datasets, the algorithm was able to maintain strong performance under challenging conditions of large patient size and reduced dose. This study shows the validity of a deep-learning based approach to multi-material decomposition trained with in-silico images that can overcome the limitations of conventional material decomposition approaches.

Intracerebral hemorrhage (ICH) is a life-threatening condition caused by bleeding in the brain, with high mortality rates, particularly in the acute phase. Accurate diagnosis through medical image segmentation plays a crucial role in early intervention and treatment. However, existing segmentation methods, such as region-growing, clustering, and deep learning, face significant limitations when applied to complex images like ICH, especially in multi-threshold image segmentation (MTIS). As the number of thresholds increases, these methods often become computationally expensive and exhibit degraded segmentation performance. To address these challenges, this paper proposes an Elite-Adaptive-Turbulent Hiking Optimization Algorithm (EATHOA), an enhanced version of the Hiking Optimization Algorithm (HOA), specifically designed for high-dimensional and multimodal optimization problems like ICH image segmentation. EATHOA integrates three novel strategies including Elite Opposition-Based Learning (EOBL) for improving population diversity and exploration, Adaptive k-Average-Best Mutation (AKAB) for dynamically balancing exploration and exploitation, and a Turbulent Operator (TO) for escaping local optima and enhancing the convergence rate. Extensive experiments were conducted on the CEC2017 and CEC2022 benchmark functions to evaluate EATHOA's global optimization performance, where it consistently outperformed other state-of-the-art algorithms. The proposed EATHOA was then applied to solve the MTIS problem in ICH images at six different threshold levels. EATHOA achieved peak values of PSNR (34.4671), FSIM (0.9710), and SSIM (0.8816), outperforming recent methods in segmentation accuracy and computational efficiency. These results demonstrate the superior performance of EATHOA and its potential as a powerful tool for medical image analysis, offering an effective and computationally efficient solution for the complex challenges of ICH image segmentation.

Efficient and accurate preoperative assessment of the right-sided heart structural complex (RSHSc) is crucial for planning transcatheter tricuspid valve replacement (TTVR). However, current manual methods remain time-consuming and inconsistent. To address this unmet clinical need, this study aimed to develop and validate TRI-PLAN, the first fully automated, deep learning (DL)-based framework for pre-TTVR assessment. A total of 140 preprocedural computed tomography angiography (CTA) scans (63,962 slices) from patients with severe tricuspid regurgitation (TR) at two high-volume cardiac centers in China were retrospectively included. The patients were divided into a training cohort (n = 100), an internal validation cohort (n = 20), and an external validation cohort (n = 20). TRI-PLAN was developed by a dual-stage right heart assessment network (DRA-Net) to segment the RSHSc and localize the tricuspid annulus (TA), followed by automated measurement of key anatomical parameters and right ventricular ejection fraction (RVEF). Performance was comprehensively evaluated in terms of accuracy, interobserver benchmark comparison, clinical usability, and workflow efficiency. TRI-PLAN achieved expert-level segmentation accuracy (volumetric Dice 0.952/0.955; surface Dice 0.934/0.940), precise localization (standard deviation 1.18/1.14 mm), excellent measurement agreement (ICC 0.984/0.979) and reliable RVEF evaluation (R = 0.97, bias<5 %) across internal and external cohorts. In addition, TRI-PLAN obtained a direct acceptance rate of 80 % and reduced total assessment time from 30 min manually to under 2 min (>95 % time saving). TRI-PLAN provides an accurate, efficient, and clinically applicable solution for pre-TTVR assessment, with strong potential to streamline TTVR planning and enhance procedural outcomes.

Conventional systems supporting percutaneous radiofrequency ablation (PRFA) have faced difficulties in ensuring safe and accurate puncture due to issues inherent to the medical images used and organ displacement caused by patients' respiration. To address this problem, this study proposes a semi-automatic puncture robot system that integrates real-time ultrasound (US) images with computed tomography (CT) images. The purpose of this paper is to evaluate the system's usefulness through a pilot clinical experiment involving participants. For the clinical experiment using the proposed system, an improved U-net model based on fivefold cross-validation was constructed. Following the workflow of the proposed system, the model was trained using US images acquired from patients with robotic arms. The average Dice coefficient for the entire validation dataset was confirmed to be 0.87. Therefore, the model was implemented in the robotic system and applied to clinical experiment. A clinical experiment was conducted using the robotic system equipped with the developed AI model on five adult male and female participants. The centroid distances between the point clouds from each modality were evaluated in the 3D US-CT fusion process, assuming the blood vessel centerline represents the overall structural position. The results of the centroid distances showed a minimum value of 0.38 mm, a maximum value of 4.81 mm, and an average of 1.97 mm. Although the five participants had different CP classifications and the derived US images exhibited individual variability, all centroid distances satisfied the ablation margin of 5.00 mm considered in PRFA, suggesting the potential accuracy and utility of the robotic system for puncture navigation. Additionally, the results suggested the potential generalization performance of the AI model trained with data acquired according to the robotic system's workflow.

Existing Lung Cancer Diagnosis (LCD) models have difficulty in detecting early-stage lung cancer due to the asymptomatic nature of the disease which leads to an increased death rate of patients. Therefore, it is important to diagnose lung disease at an early stage to save the lives of affected persons. Hence, the research work aims to develop an efficient lung disease diagnosis using deep learning techniques for the early and accurate detection of lung cancer. This is achieved by. Initially, the proposed model collects the mandatory CT images from the standard benchmark datasets. Then, the lung cancer segmentation is done by using the development of Hybrid Convolution (2D/3D)-based Adaptive DenseUnet with Attention mechanism (HC-ADAM). The Hybrid Sewing Training with Spider Monkey Optimization (HSTSMO) is introduced to optimize the parameters in the developed HC-ADAM segmentation approach. Finally, the dissected lung nodule imagery is considered for the lung cancer classification stage, where the Hybrid Adaptive Dilated Networks with Attention mechanism (HADN-AM) are implemented with the serial cascading of ResNet and Long Short Term Memory (LSTM) for attaining better categorization performance. The accuracy, precision, and F1-score of the developed model for the LIDC-IDRI dataset are 96.3%, 96.38%, and 96.36%, respectively.

BackgroundMagnetic Resonance Imaging (MRI) is a cornerstone in diagnosing brain tumors. However, the complex nature of these tumors makes accurate segmentation in MRI images a demanding task.ObjectiveAccurate brain tumor segmentation remains a critical challenge in medical image analysis, with early detection crucial for improving patient outcomes.MethodsTo develop and evaluate a novel UNet-based architecture for improved brain tumor segmentation in MRI images. This paper presents a novel UNet-based architecture for improved brain tumor segmentation. The UNet model architecture incorporates Leaky ReLU activation, batch normalization, and regularization to enhance training and performance. The model consists of varying numbers of layers and kernel sizes to capture different levels of detail. To address the issue of class imbalance in medical image segmentation, we employ focused loss and generalized Dice (GDL) loss functions.ResultsThe proposed model was evaluated on the BraTS'2020 dataset, achieving an accuracy of 99.64% and Dice coefficients of 0.8984, 0.8431, and 0.8824 for necrotic core, edema, and enhancing tumor regions, respectively.ConclusionThese findings demonstrate the efficacy of our approach in accurately predicting tumors, which has the potential to enhance diagnostic systems and improve patient outcomes.

Cross-domain Few-shot Medical Image Segmentation (CD-FSMIS) is a potential solution for segmenting medical images with limited annotation using knowledge from other domains. The significant performance of current CD-FSMIS models relies on the heavily training procedure over other source medical domains, which degrades the universality and ease of model deployment. With the development of large visual models of natural images, we propose a training-free CD-FSMIS model that introduces the Multi-center Adaptive Uncertainty-aware Prompting (MAUP) strategy for adapting the foundation model Segment Anything Model (SAM), which is trained with natural images, into the CD-FSMIS task. To be specific, MAUP consists of three key innovations: (1) K-means clustering based multi-center prompts generation for comprehensive spatial coverage, (2) uncertainty-aware prompts selection that focuses on the challenging regions, and (3) adaptive prompt optimization that can dynamically adjust according to the target region complexity. With the pre-trained DINOv2 feature encoder, MAUP achieves precise segmentation results across three medical datasets without any additional training compared with several conventional CD-FSMIS models and training-free FSMIS model. The source code is available at: https://github.com/YazhouZhu19/MAUP.

Cold-Start Active Learning (CSAL) aims to select informative samples for annotation without prior knowledge, which is important for improving annotation efficiency and model performance under a limited annotation budget in medical image analysis. Most existing CSAL methods rely on Self-Supervised Learning (SSL) on the target dataset for feature extraction, which is inefficient and limited by insufficient feature representation. Recently, pre-trained Foundation Models (FMs) have shown powerful feature extraction ability with a potential for better CSAL. However, this paradigm has been rarely investigated, with a lack of benchmarks for comparison of FMs in CSAL tasks. To this end, we propose MedCAL-Bench, the first systematic FM-based CSAL benchmark for medical image analysis. We evaluate 14 FMs and 7 CSAL strategies across 7 datasets under different annotation budgets, covering classification and segmentation tasks from diverse medical modalities. It is also the first CSAL benchmark that evaluates both the feature extraction and sample selection stages. Our experimental results reveal that: 1) Most FMs are effective feature extractors for CSAL, with DINO family performing the best in segmentation; 2) The performance differences of these FMs are large in segmentation tasks, while small for classification; 3) Different sample selection strategies should be considered in CSAL on different datasets, with Active Learning by Processing Surprisal (ALPS) performing the best in segmentation while RepDiv leading for classification. The code is available at https://github.com/HiLab-git/MedCAL-Bench.