The pathological changes in deep medullary veins (DMVs) have been reported in various diseases. However, accurate modeling and quantification of DMVs remain challenging. We aim to propose and assess an automated approach for modeling and quantifying DMVs at 7 Tesla (7 T) MRI. A multi-echo-input Res-Net was developed for vascular segmentation, and a minimum path loss function was used for modeling and quantifying the geometric parameter of DMVs. Twenty-one patients diagnosed as subcortical vascular dementia (SVaD) and 20 condition matched controls were included in this study. The amplitude and phase images of gradient echo with five echoes were acquired at 7 T. Ten GRE images were manually labeled by two neurologists and compared with the results obtained by our proposed method. Independent samples t test and Pearson correlation were used for statistical analysis in our study, and p value < 0.05 was considered significant. No significant offset was found in centerlines obtained by human labeling and our algorithm (p = 0.734). The length difference between the proposed method and manual labeling was smaller than the error between different clinicians (p < 0.001). Patients with SVaD exhibited fewer DMVs (mean difference = -60.710 ± 21.810, p = 0.011) and higher curvature (mean difference = 0.12 ± 0.022, p < 0.0001), corresponding to their higher Vascular Dementia Assessment Scale-Cog (VaDAS-Cog) scores (mean difference = 4.332 ± 1.992, p = 0.036) and lower Mini-Mental State Examination (MMSE) (mean difference = -3.071 ± 1.443, p = 0.047). The MMSE scores were positively correlated with the numbers of DMVs (r = 0.437, p = 0.037) and were negatively correlated with the curvature (r = -0.426, p = 0.042). In summary, we proposed a novel framework for automated quantifying the morphologic parameters of DMVs. These characteristics of DMVs are expected to help the research and diagnosis of cerebral small vessel diseases with DMV lesions.

Masked AutoEncoder (MAE) has demonstrated significant potential in medical image analysis by reducing the cost of manual annotations. However, MAE and its recent variants are not well-developed for ultrasound images in breast cancer diagnosis, as they struggle to generalize to the task of distinguishing ultrasound breast tumors of varying sizes. This limitation hinders the model's ability to adapt to the diverse morphological characteristics of breast tumors. In this paper, we propose a novel Breast UltraSound Multi-scale Masked AutoEncoder (BUS-M2AE) model to address the limitations of the general MAE. BUS-M2AE incorporates multi-scale masking methods at both the token level during the image patching stage and the feature level during the feature learning stage. These two multi-scale masking methods enable flexible strategies to match the explicit masked patches and the implicit features with varying tumor scales. By introducing these multi-scale masking methods in the image patching and feature learning phases, BUS-M2AE allows the pre-trained vision transformer to adaptively perceive and accurately distinguish breast tumors of different sizes, thereby improving the model's overall performance in handling diverse tumor morphologies. Comprehensive experiments demonstrate that BUS-M2AE outperforms recent MAE variants and commonly used supervised learning methods in breast cancer classification and tumor segmentation tasks.

Considering the radiation hazard of X-ray, safer, more convenient and cost-effective ultrasound methods are gradually becoming new diagnostic approaches for scoliosis. For ultrasound images of spine regions, it is challenging to accurately identify spine regions in images due to relatively small target areas and the presence of a lot of interfering information. Therefore, we developed a novel neural network that incorporates prior knowledge to precisely segment spine regions in ultrasound images. We constructed a dataset of ultrasound images of spine regions for semantic segmentation. The dataset contains 3136 images of 30 patients with scoliosis. And we propose a network model (ICPPNet), which fully utilizes inter-class positional prior knowledge by combining an inter-class positional probability heatmap, to achieve accurate segmentation of target areas. ICPPNet achieved an average Dice similarity coefficient of 70.83% and an average 95% Hausdorff distance of 11.28 mm on the dataset, demonstrating its excellent performance. The average error between the Cobb angle measured by our method and the Cobb angle measured by X-ray images is 1.41 degrees, and the coefficient of determination is 0.9879 with a strong correlation. ICPPNet provides a new solution for the medical image segmentation task with positional prior knowledge between target classes. And ICPPNet strongly supports the subsequent reconstruction of spine models using ultrasound images.

In healthcare sector, magnetic resonance imaging (MRI) images are taken for multiple sclerosis (MS) assessment, classification, and management. However, interpreting an MRI scan requires an exceptional amount of skill because abnormalities on scans are frequently inconsistent with clinical symptoms, making it difficult to convert the findings into effective treatment strategies. Furthermore, MRI is an expensive process, and its frequent utilization to monitor an illness increases healthcare costs. To overcome these drawbacks, this research employs advanced technological approaches to develop a deep learning system for classifying types of MS through clinical brain MRI scans. The major innovation of this model is to influence the convolution network with attention concept and recurrent-based deep learning for classifying the disorder; this also proposes an optimization algorithm for tuning the parameter to enhance the performance. Initially, the total images as 3427 are collected from database, in which the collected samples are categorized for training and testing phase. Here, the segmentation is carried out by adaptive and attentive-based mask regional convolution neural network (AA-MRCNN). In this phase, the MRCNN's parameters are finely tuned with an enhanced pine cone optimization algorithm (EPCOA) to guarantee outstanding efficiency. Further, the segmented image is given to recurrent MobileNet with long short term memory (RM-LSTM) for getting the classification outcomes. Through experimental analysis, this deep learning model is acquired 95.4% for accuracy, 95.3% for sensitivity, and 95.4% for specificity. Hence, these results prove that it has high potential for appropriately classifying the sclerosis disorder.

Obesity is a risk factor for diverticulitis. However, it remains unclear whether visceral fat area, a more precise measurement of abdominal fat, is associated with the risk of diverticulitis. To estimate the risk of incident and recurrent diverticulitis according to visceral fat area. A retrospective cohort study. The Mass General Brigham Biobank. A total of 6654 patients who underwent abdominal CT for clinical indications and had no diagnosis of diverticulitis, IBD, or cancer before the scan were included. Visceral fat area, subcutaneous fat area, and skeletal muscle area were quantified using a deep-learning model applied to abdominal CT. The main exposures were z -scores of body composition metrics normalized by age, sex, and race. Diverticulitis cases were identified using the International Classification of Diseases codes for the primary or admitting diagnosis from the electronic health records. The risks of incident diverticulitis, complicated diverticulitis, and recurrent diverticulitis requiring hospitalization according to quartiles of body composition metrics z -scores were estimated. A higher visceral fat area z -score was associated with an increased risk of incident diverticulitis (multivariable HR comparing the highest vs lowest quartile, 2.09; 95% CI, 1.48-2.95; p for trend <0.0001), complicated diverticulitis (HR, 2.56; 95% CI, 1.10-5.99; p for trend = 0.02), and recurrence requiring hospitalization (HR, 2.76; 95% CI, 1.15-6.62; p for trend = 0.03). The association between visceral fat area and diverticulitis was not materially different among different strata of BMI. Subcutaneous fat area and skeletal muscle area were not significantly associated with diverticulitis. The study population was limited to individuals who underwent CT scans for medical indication. Higher visceral fat area derived from CT was associated with incident and recurrent diverticulitis. Our findings provide insight into the underlying pathophysiology of diverticulitis and may have implications for preventive strategies. See Video Abstract . ANTECEDENTES:La obesidad es un factor de riesgo de la diverticulitis. Sin embargo, sigue sin estar claro si el área de grasa visceral, con medida más precisa de la grasa abdominal esté asociada con el riesgo de diverticulitis.OBJETIVO:Estimar el riesgo de diverticulitis incidente y recurrente de acuerdo con el área de grasa visceral.DISEÑO:Un estudio de cohorte retrospectivo.AJUSTE:El Biobanco Mass General Brigham.PACIENTES:6.654 pacientes sometidos a una TC abdominal por indicaciones clínicas y sin diagnóstico de diverticulitis, enfermedad inflamatoria intestinal o cáncer antes de la exploración.PRINCIPALES MEDIDAS DE RESULTADOS:Se cuantificaron, área de grasa visceral, área de grasa subcutánea y área de músculo esquelético, utilizando un modelo de aprendizaje profundo aplicado a la TC abdominal. Las principales exposiciones fueron puntuaciones z de métricas de composición corporal, normalizadas por edad, sexo y raza. Los casos de diverticulitis se definieron con los códigos ICD para el diagnóstico primario o de admisión de los registros de salud electrónicos. Se estimaron los riesgos de diverticulitis incidente, diverticulitis complicada y diverticulitis recurrente que requiriera hospitalización según los cuartiles de las puntuaciones z de las métricas de composición corporal.RESULTADOS:Una puntuación z más alta del área de grasa visceral se asoció con un mayor riesgo de diverticulitis incidente (HR multivariable que compara el cuartil más alto con el más bajo, 2,09; IC del 95 %, 1,48-2,95; P para la tendencia < 0,0001), diverticulitis complicada (HR, 2,56; IC del 95 %, 1,10-5,99; P para la tendencia = 0,02) y recurrencia que requiriera hospitalización (HR, 2,76; IC del 95 %, 1,15-6,62; P para la tendencia = 0,03). La asociación entre el área de grasa visceral y la diverticulitis no fue materialmente diferente entre los diferentes estratos del índice de masa corporal. El área de grasa subcutánea y el área del músculo esquelético no se asociaron significativamente con la diverticulitis.LIMITACIONES:La población del estudio se limitó a individuos sometidos a tomografías computarizadas por indicación médica.CONCLUSIÓN:Una mayor área de grasa visceral derivada de la tomografía computarizada se asoció con diverticulitis incidente y recurrente. Nuestros hallazgos brindan información sobre la fisiopatología subyacente de la diverticulitis y pueden tener implicaciones para las estrategias preventivas. (Traducción: Dr. Fidel Ruiz Healy ).

Intracranial Hemorrhage (ICH) refers to cerebral bleeding resulting from ruptured blood vessels within the brain. Delayed and inaccurate diagnosis and treatment of ICH can lead to fatality or disability. Therefore, early and precise diagnosis of intracranial hemorrhage is crucial for protecting patients' lives. Automatic segmentation of hematomas in CT images can provide doctors with essential diagnostic support and improve diagnostic efficiency. CT images of intracranial hemorrhage exhibit characteristics such as multi-scale, multi-target, and blurred edges. This paper proposes a Multi-scale and Edge Feature Fusion Network (MEF-Net) to effectively extract multi-scale and edge features and fully fuse these features through a fusion mechanism. The network first extracts the multi-scale features and edge features of the image through the encoder and the edge detection module respectively, then fuses the deep information, and employs the multi-kernel attention module to process the shallow features, enhancing the multi-target recognition capability. Finally, the feature maps from each module are combined to produce the segmentation result. Experimental results indicate that this method has achieved average DICE scores of 0.7508 and 0.7443 in two public datasets respectively, surpassing those of several advanced methods in medical image segmentation currently available. The proposed MEF-Net significantly improves the accuracy of intracranial hemorrhage segmentation.

This article aimed to explore topological uncertainty in medical imaging, particularly in assessing coronary artery calcification using artificial intelligence (AI). Topological uncertainty refers to ambiguities in spatial and structural characteristics of medical features, which can impact the interpretation of coronary plaques. The article discusses the challenges of integrating AI with topological considerations and the need for specialized methodologies beyond traditional performance metrics. It highlights advancements in quantifying topological uncertainty, including the use of persistent homology and topological data analysis techniques. The importance of standardization in methodologies and ethical considerations in AI deployment are emphasized. It also outlines various types of uncertainty in topological frameworks for coronary plaques, categorizing them as quantifiable and controllable or quantifiable and not controllable. Future directions include developing AI algorithms that incorporate topological insights, establishing standardized protocols, and exploring ethical implications to revolutionize cardiovascular care through personalized treatment plans guided by sophisticated topological analysis. Recognizing and quantifying topological uncertainty in medical imaging as AI emerges is critical. Exploring topological uncertainty in coronary artery disease will revolutionize cardiovascular care, promising enhanced precision and personalization in diagnostics and treatment for millions affected by cardiovascular diseases.

The image quality of single-energy CT (SECT) limited the accuracy of automatic segmentation. Dual-energy CT (DECT) may potentially improve automatic segmentation yet the performance and strategy have not been investigated thoroughly. Based on DECT-generated virtual monochromatic images (VMIs), this study proposed a novel deep learning model (MIAU-Net) and evaluated the segmentation performance on the head organs-at-risk (OARs). The VMIs from 40 keV to 190 keV were retrospectively generated at intervals of 10 keV using the DECT of 46 patients. Images with expert delineation were used for training, validation, and testing MIAU-Net for automatic segmentation. Theperformance of MIAU-Net was compared with the existingU-Net, Attention-UNet, nnU-Net and TransFuse methods based on Dice Similarity Coefficient (DSC). Correlationanalysis was performed to evaluate and optimize the impact of different virtual energies on the accuracy of segmentation. Using MIAU-Net, average DSCs across all virtual energy levels were 93.78 %, 81.75 %, 84.46 %, 92.85 %, 94.40 %, and 84.75 % for the brain stem, optic chiasm, lens, mandible, eyes, and optic nerves, respectively, higher than the previous publications using SECT. MIAU-Net achieved the highest average DSC (88.84 %) and the lowest parameters (14.54 M) in all tested models. The results suggested that 60 keV-80 keV is the optimal VMI energy level for soft tissue delineation, while 100 keV is optimal for skeleton segmentation. This work proposed and validated a novel deep learning model for automatic segmentation based on DECT, suggesting potential advantages and OAR-specific optimal energy of using VMIs for automatic delineation.

To validate a liver lesion detection and classification model using staging computed tomography (CT) scans of colorectal cancer (CRC) patients. A UNet-based deep learning model was trained on 272 public liver tumour CT scans and tested on 220 CRC staging CTs acquired from a single institution (2014-2019). Performance metrics included lesion detection rates by size (<10 mm, 10-20 mm, >20 mm), segmentation accuracy (dice similarity coefficient, DSC), volume measurement agreement (Bland-Altman limits of agreement, LOAs; intraclass correlation coefficient, ICC), and classification accuracy (malignant vs benign) at patient and lesion levels (detected lesions only). The model detected 743 out of 884 lesions (84%), with detection rates of 75%, 91.3%, and 96% for lesions <10 mm, 10-20 mm, and >20 mm, respectively. The median DSC was 0.76 (95% CI: 0.72-0.80) for lesions <10 mm, 0.83 (95% CI: 0.79-0.86) for 10-20 mm, and 0.85 (95% CI: 0.82-0.88) for >20 mm. Bland-Altman analysis showed a mean volume bias of -0.12 cm³ (LOAs: -1.68 to +1.43 cm³), and ICC was 0.81. Lesion-level classification showed 99.5% sensitivity, 65.7% specificity, 53.8% positive predictive value (PPV), 99.7% negative predictive value (NPV), and 75.4% accuracy. Patient-level classification had 100% sensitivity, 27.1% specificity, 59.2% PPV, 100% NPV, and 64.5% accuracy. The model demonstrates strong lesion detection and segmentation performance, particularly for sub-centimetre lesions. Although classification accuracy was moderate, the 100% NPV suggests strong potential as a CRC staging screening tool. Future studies will assess its impact on radiologist performance and efficiency.

Artificial intelligence (AI) is transforming fetal brain imaging by addressing key challenges in diagnostic accuracy, efficiency, and data integration in prenatal care. This review explores AI's application in enhancing fetal brain imaging through ultrasound (US) and magnetic resonance imaging (MRI), with a particular focus on multimodal integration to leverage their complementary strengths. By critically analyzing state-of-the-art AI methodologies, including deep learning frameworks and attention-based architectures, this study highlights significant advancements alongside persistent challenges. Notable barriers include the scarcity of diverse and high-quality datasets, computational inefficiencies, and ethical concerns surrounding data privacy and security. Special attention is given to multimodal approaches that integrate US and MRI, combining the accessibility and real-time imaging of US with the superior soft tissue contrast of MRI to improve diagnostic precision. Furthermore, this review emphasizes the transformative potential of AI in fostering clinical adoption through innovations such as real-time diagnostic tools and human-AI collaboration frameworks. By providing a comprehensive roadmap for future research and implementation, this study underscores AI's potential to redefine fetal imaging practices, enhance diagnostic accuracy, and ultimately improve perinatal care outcomes.