In this paper, we present the VMSE U-Net and VM-Unet CBAM+ model, two cutting-edge deep learning architectures designed to enhance medical image segmentation. Our approach integrates Squeeze-and-Excitation (SE) and Convolutional Block Attention Module (CBAM) techniques into the traditional VM U-Net framework, significantly improving segmentation accuracy, feature localization, and computational efficiency. Both models show superior performance compared to the baseline VM-Unet across multiple datasets. Notably, VMSEUnet achieves the highest accuracy, IoU, precision, and recall while maintaining low loss values. It also exhibits exceptional computational efficiency with faster inference times and lower memory usage on both GPU and CPU. Overall, the study suggests that the enhanced architecture VMSE-Unet is a valuable tool for medical image analysis. These findings highlight its potential for real-world clinical applications, emphasizing the importance of further research to optimize accuracy, robustness, and computational efficiency.

Accurate fetal birth weight (FBW) estimation is essential for optimizing delivery decisions and reducing perinatal mortality. However, clinical methods for FBW estimation are inefficient, operator-dependent, and challenging to apply in cases of complex fetal anatomy. Existing deep learning methods are based on 2D standard ultrasound (US) images or videos that lack spatial information, limiting their prediction accuracy. In this study, we propose the first method for directly estimating FBW from 3D fetal US volumes. Our approach integrates a multi-scale feature fusion network (MFFN) and a synthetic sample-based learning framework (SSLF). The MFFN effectively extracts and fuses multi-scale features under sparse supervision by incorporating channel attention, spatial attention, and a ranking-based loss function. SSLF generates synthetic samples by simply combining fetal head and abdomen data from different fetuses, utilizing semi-supervised learning to improve prediction performance. Experimental results demonstrate that our method achieves superior performance, with a mean absolute error of $166.4\pm155.9$ $g$ and a mean absolute percentage error of $5.1\pm4.6$%, outperforming existing methods and approaching the accuracy of a senior doctor. Code is available at: https://github.com/Qioy-i/EFW.

Recent advancements in deep learning for medical image segmentation are often limited by the scarcity of high-quality training data.While diffusion models provide a potential solution by generating synthetic images, their effectiveness in medical imaging remains constrained due to their reliance on large-scale medical datasets and the need for higher image quality. To address these challenges, we present MedDiff-FT, a controllable medical image generation method that fine-tunes a diffusion foundation model to produce medical images with structural dependency and domain specificity in a data-efficient manner. During inference, a dynamic adaptive guiding mask enforces spatial constraints to ensure anatomically coherent synthesis, while a lightweight stochastic mask generator enhances diversity through hierarchical randomness injection. Additionally, an automated quality assessment protocol filters suboptimal outputs using feature-space metrics, followed by mask corrosion to refine fidelity. Evaluated on five medical segmentation datasets,MedDiff-FT's synthetic image-mask pairs improve SOTA method's segmentation performance by an average of 1% in Dice score. The framework effectively balances generation quality, diversity, and computational efficiency, offering a practical solution for medical data augmentation. The code is available at https://github.com/JianhaoXie1/MedDiff-FT.

Magnetic resonance imaging (MRI) reports are challenging for patients to interpret and may subject patients to unnecessary anxiety. The advent of advanced artificial intelligence (AI) large language models (LLMs), such as GPT-4o, hold promise for translating complex medical information into layman terms. This paper aims to evaluate the accuracy, helpfulness, and readability of GPT-4o in explaining MRI reports of patients with thoracolumbar fractures. MRI reports of 20 patients presenting with thoracic or lumbar vertebral body fractures were obtained. GPT-4o was prompted to explain the MRI report in layman's terms. The generated explanations were then presented to 7 board-certified spine surgeons for evaluation on the reports' helpfulness and accuracy. The MRI report text and GPT-4o explanations were then analyzed to grade the readability of the texts using the Flesch Readability Ease Score (FRES) and Flesch-Kincaid Grade Level (FKGL) Scale. The layman explanations provided by GPT-4o were found to be helpful by all surgeons in 17 cases, with 6 of 7 surgeons finding the information helpful in the remaining 3 cases. ChatGPT-generated layman reports were rated as "accurate" by all 7 surgeons in 11/20 cases (55%). In an additional 5/20 cases (25%), 6 out of 7 surgeons agreed on their accuracy. In the remaining 4/20 cases (20%), accuracy ratings varied, with 4 or 5 surgeons considering them accurate. Review of surgeon feedback on inaccuracies revealed that the radiology reports were often insufficiently detailed. The mean FRES score of the MRI reports was significantly lower than the GPT-4o explanations (32.15, SD 15.89 vs 53.9, SD 7.86; P<.001). The mean FKGL score of the MRI reports trended higher compared to the GPT-4o explanations (11th-12th grade vs 10th-11th grade level; P=.11). Overall helpfulness and readability ratings for AI-generated summaries of MRI reports were high, with few inaccuracies recorded. This study demonstrates the potential of GPT-4o to serve as a valuable tool for enhancing patient comprehension of MRI report findings.

Major Depressive Disorder (MDD) diagnosis mainly relies on subjective self-reporting and clinical assessments. Resting-state functional magnetic resonance imaging (rs-fMRI) and its analysis of Effective Connectivity (EC) offer a quantitative approach to understand the directional interactions between brain regions, presenting a potential objective method for MDD classification. Granger causality analysis was used to extract EC features from a large, multi-site rs-fMRI dataset of MDD patients. The ComBat algorithm was applied to adjust for site differences, while multivariate linear regression was employed to control for age and sex differences. Discriminative EC features for MDD were identified using two-sample t-tests and model-based feature selection, with the LightGBM algorithm being used for classification. The performance and generalizability of the model was evaluated using nested five-fold cross-validation and tested for generalizability on an independent dataset. Ninety-seven EC features belonging to the cerebellum and front-temporal regions were identified as highly discriminative for MDD. The classification model using these features achieved an accuracy of 94.35 %, with a sensitivity of 93.52 % and specificity of 95.25 % in cross-validation. Generalization of the model to an independent dataset resulted in an accuracy of 94.74 %, sensitivity of 90.59 %, and specificity of 96.75 %. The study demonstrates that EC features from rs-fMRI can effectively discriminate MDD from healthy controls, suggesting that EC analysis could be a valuable tool in assisting the clinical diagnosis of MDD. This method shows promise in enhancing the objectivity of MDD diagnosis through the use of neuroimaging biomarkers.

Nonhuman primates, particularly rhesus macaques, have served as crucial animal models for investigating complex brain functions. While previous studies have explored neural activity features in macaques, the gyro-sulcal functional dissociation characteristics are largely unknown. In this study, we employ a deep learning model named one-dimensional convolutional neural network to differentiate resting state functional magnetic resonance imaging signals between gyri and sulci in macaque brains, and further investigate the frequency-specific dissociations between gyri and sulci inferred from the power spectral density of resting state functional magnetic resonance imaging. Experimental results based on a large cohort of 440 macaques from two independent sites demonstrate substantial frequency-specific dissociation between gyral and sulcal signals at both whole-brain and regional levels. The magnitude of gyral power spectral density is significantly larger than that of sulcal power spectral density within the range of 0.01 to 0.1 Hz, suggesting that gyri and sulci may play distinct roles as the global hubs and local processing units for functional activity transmission and interaction in macaque brains. In conclusion, our study has established one of the first power spectrum maps of gyro-sulcal functional activity dissociation in macaque brains, providing a novel perspective for systematically exploring the neural mechanism of functional dissociation in mammalian brains.

Cephalometric Landmark Detection (CLD), i.e. annotating interest points in lateral X-ray images, is the crucial first step of every orthodontic therapy. While CLD has immense potential for automation using Deep Learning methods, carefully crafted contemporary approaches using convolutional neural networks and heatmap prediction do not qualify for large-scale clinical application due to insufficient performance. We propose a novel approach using Vision Transformers (ViTs) with direct coordinate prediction, avoiding the memory-intensive heatmap prediction common in previous work. Through extensive ablation studies comparing our method against contemporary CNN architectures (ConvNext V2) and heatmap-based approaches (Segformer), we demonstrate that ViTs with coordinate prediction achieve superior performance with more than 2 mm improvement in mean radial error compared to state-of-the-art CLD methods. Our results show that while non-adapted CNN architectures perform poorly on the given task, contemporary approaches may be too tailored to specific datasets, failing to generalize to different and especially sparse datasets. We conclude that using general-purpose Vision Transformers with direct coordinate prediction shows great promise for future research on CLD and medical computer vision.

Microbeam Radiation Therapy (MRT) is an innovative radiotherapy modality which uses highly focused synchrotron-generated X-ray microbeams. Current pre-clinical research in MRT mostly rely on Monte Carlo (MC) simulations for dose estimation, which are highly accurate but computationally intensive. Recently, Deep Learning (DL) dose engines have been proved effective in generating fast and reliable dose distributions in different RT modalities. However, relatively few studies compare different models on the same task. This work aims to compare a Graph-Convolutional-Network-based DL model, developed in the context of Very High Energy Electron RT, to the Convolutional 3D U-Net that we recently implemented for MRT dose predictions. The two DL solutions are trained with 3D dose maps, generated with the MC-Toolkit Geant4, in rats used in MRT pre-clinical research. The models are evaluated against Geant4 simulations, used as ground truth, and are assessed in terms of Mean Absolute Error, Mean Relative Error, and a voxel-wise version of the γ-index. Also presented are specific comparisons of predictions in relevant tumor regions, tissues boundaries and air pockets. The two models are finally compared from the perspective of the execution time and size. This study finds that the two models achieve comparable overall performance. Main differences are found in their dosimetric accuracy within specific regions, such as air pockets, and their respective inference times. Consequently, the choice between models should be guided primarily by data structure and time constraints, favoring the graph-based method for its flexibility or the 3D U-Net for its faster execution.