Accurate segmentation of cerebral arteries on computed tomography angiography (CTA) images is essential for the diagnosis and management of cerebrovascular diseases, including ischemic stroke. This study implemented a deep learning-based U-Net++ model for cerebral artery segmentation in CTA images, focusing on optimizing pruning levels by analyzing the trade-off between segmentation performance and computational cost. Dual-energy CTA and direct subtraction CTA datasets were utilized to segment the internal carotid and vertebral arteries in close proximity to the bone. We implemented four pruning levels (L1-L4) in the U-Net++ model and evaluated the segmentation performance using accuracy, intersection over union, F1-score, boundary F1-score, and Hausdorff distance. Statistical analyses were conducted to assess the significance of segmentation performance differences across pruning levels. In addition, we measured training and inference times to evaluate the trade-off between segmentation performance and computational efficiency. Applying deep supervision improved segmentation performance across all factors. While the L4 pruning level achieved the highest segmentation performance, L3 significantly reduced training and inference times (by an average of 51.56 % and 22.62 %, respectively), while incurring only a small decrease in segmentation performance (7.08 %) compared to L4. These results suggest that L3 achieves an optimal balance between performance and computational cost. This study demonstrates that pruning levels in U-Net++ models can be optimized to reduce computational cost while maintaining effective segmentation performance. By simplifying deep learning models, this approach can improve the efficiency of cerebrovascular segmentation, contributing to faster and more accurate diagnoses in clinical settings.

Clinical data warehouses, which have arisen over the last decade, bring together the medical data of millions of patients and offer the potential to train and validate machine learning models in real-world scenarios. The quality of MRIs collected in clinical data warehouses differs significantly from that generally observed in research datasets, reflecting the variability inherent to clinical practice. Consequently, the use of clinical data requires the implementation of robust quality control tools. By using a substantial number of pre-existing manually labelled T1-weighted MR images (5,500) alongside a smaller set of newly labelled FLAIR images (926), we present a novel semi-supervised adversarial domain adaptation architecture designed to exploit shared representations between MRI sequences thanks to a shared feature extractor, while taking into account the specificities of the FLAIR thanks to a specific classification head for each sequence. This architecture thus consists of a common invariant feature extractor, a domain classifier and two classification heads specific to the source and target, all designed to effectively deal with potential class distribution shifts between the source and target data classes. The primary objectives of this paper were: (1) to identify images which are not proper 3D FLAIR brain MRIs; (2) to rate the overall image quality. For the first objective, our approach demonstrated excellent results, with a balanced accuracy of 89%, comparable to that of human raters. For the second objective, our approach achieved good performance, although lower than that of human raters. Nevertheless, the automatic approach accurately identified bad quality images (balanced accuracy >79%). In conclusion, our proposed approach overcomes the initial barrier of heterogeneous image quality in clinical data warehouses, thereby facilitating the development of new research using clinical routine 3D FLAIR brain images.

To compare clinical ratings and signal-to-noise ratio (SNR) measures of a commercially available Deep Learning-based MRI reconstruction method (T2_(DR)) against conventional T2- turbo spin echo brain MRI (T2_(CN)). 100 consecutive patients with various neurological conditions underwent both T2_(DR) and T2_(CN) on a Siemens Vida 3 T scanner with a 64-channel head coil in the same examination. Acquisition times were 3.33 min for T2_(CN) and 1.04 min for T2_(DR). Four neuroradiologists evaluated overall image quality (OIQ), diagnostic safety (DS), and image artifacts (IA), blinded to the acquisition mode. SNR and SNR_eff (adjusted for acquisition time) were calculated for air, grey- and white matter, and cerebrospinal fluid. The mean patient age was 43.6 years (SD 20.3), with 54 females. The distribution of non-diagnostic ratings did not differ significantly between T2_(CN) and T2_(DR) (IA p = 0.108; OIQ: p = 0.700 and DS: p = 0.652). However, when considering the full spectrum of ratings, significant differences favouring T2_(CN) emerged in OIQ (p = 0.003) and IA (p < 0.001). T2_(CN) had higher SNR (157.9, SD 123.4) than T2_(DR) (112.8, SD 82.7), p < 0.001, but T2_(DR) demonstrated superior SNR_eff (14.1, SD 10.3) compared to T2_(CN) (10.8, SD 8.5), p < 0.001. Our results suggest that while T2_(DR) may be clinically applicable for a diagnostic setting, it does not fully match the quality of high-standard conventional T2_(CN), MRI acquisitions.

To evaluate the role of multimodal magnetic resonance imaging radiomics features in predicting early recurrence of primary central nervous system lymphoma (PCNSL) and to investigate their correlation with patient prognosis. A retrospective analysis was conducted on 145 patients with PCNSL who were treated with high-dose methotrexate-based chemotherapy. Clinical data and MRI images were collected, with tumor regions segmented using ITK-SNAP software. Radiomics features were extracted via Pyradiomics, and predictive models were developed using various machine learning algorithms. The predictive performance of these models was assessed using receiver operating characteristic (ROC) curves. Additionally, Cox regression analysis was employed to identify risk factors associated with progression-free survival (PFS). In the cohort of 145 PCNSL patients (72 recurrence, 73 non-recurrence), clinical characteristics were comparable between groups except for multiple lesion frequency (61.1% vs. 39.7%, p < 0.05) and not receiving consolidation therapy (44.4% vs. 13.7%, p < 0.05). A total of 2392 radiomics features were extracted from CET1 and T2WI MRI sequence. Combining clinical variables, 10 features were retained after the feature selection process. The logistic regression (LR) model exhibited superior predictive performance in the test set to predict PCNSL early relapse, with an area under the curve (AUC) of 0.887 (95 % confidence interval: 0.785-0.988). Multivariate Cox regression identified the Cli-Rad score as an independent prognostic factor for PFS. Significant difference in PFS was observed between high- and low-risk groups defined by Cli-Rad score (8.24 months vs. 24.17 months, p < 0.001). The LR model based on multimodal MRI radiomics and clinical features, can effectively predict early recurrence of PCNSL, while the Cli-Rad score could independently forecast PFS among PCNSL patients.

Schizophrenia is a severe yet treatable mental disorder, and it is diagnosed using a multitude of primary and secondary symptoms. Diagnosis and treatment for each individual depends on the severity of the symptoms. Therefore, there is a need for accurate, personalised assessments. However, the process can be both time-consuming and subjective; hence, there is a motivation to explore automated methods that can offer consistent diagnosis and precise symptom assessments, thereby complementing the work of healthcare practitioners. Machine Learning has demonstrated impressive capabilities across numerous domains, including medicine; the use of Machine Learning in patient assessment holds great promise for healthcare professionals and patients alike, as it can lead to more consistent and accurate symptom estimation. This survey reviews methodologies utilising Machine Learning for diagnosing and assessing schizophrenia. Contrary to previous reviews that primarily focused on binary classification, this work recognises the complexity of the condition and, instead, offers an overview of Machine Learning methods designed for fine-grained symptom estimation. We cover multiple modalities, namely Medical Imaging, Electroencephalograms and Audio-Visual, as the illness symptoms can manifest in a patient's pathology and behaviour. Finally, we analyse the datasets and methodologies used in the studies and identify trends, gaps, as opportunities for future research.

Brain lesion segmentation is challenging in medical image analysis, aiming to delineate lesion regions precisely. Deep learning (DL) techniques have recently demonstrated promising results across various computer vision tasks, including semantic segmentation, object detection, and image classification. This paper offers an overview of recent DL algorithms for brain tumor and stroke segmentation, drawing on literature from 2021 to 2024. It highlights the strengths, limitations, current research challenges, and unexplored areas in imaging-based brain lesion classification based on insights from over 250 recent review papers. Techniques addressing difficulties like class imbalance and multi-modalities are presented. Optimization methods for improving performance regarding computational and structural complexity and processing speed are discussed. These include lightweight neural networks, multilayer architectures, and computationally efficient, highly accurate network designs. The paper also reviews generic and latest frameworks of different brain lesion detection techniques and highlights publicly available benchmark datasets and their issues. Furthermore, open research areas, application prospects, and future directions for DL-based brain lesion classification are discussed. Future directions include integrating neural architecture search methods with domain knowledge, predicting patient survival levels, and learning to separate brain lesions using patient statistics. To ensure patient privacy, future research is anticipated to explore privacy-preserving learning frameworks. Overall, the presented suggestions serve as a guideline for researchers and system designers involved in brain lesion detection and stroke segmentation tasks.

Semi-supervised medical image segmentation is a class of machine learning paradigms for segmentation model training and inference using both labeled and unlabeled medical images, which can effectively reduce the data labeling workload. However, existing consistency semi-supervised segmentation models mainly focus on investigating more complex consistency strategies and lack efficient utilization of volumetric contextual information, which leads to vague or uncertain understanding of the boundary between the object and the background by the model, resulting in ambiguous or even erroneous boundary segmentation results. For this reason, this study proposes a hybrid uncertainty network CUAMT based on contextual information. In this model, a contextual information extraction module CIE is proposed, which learns the connection between image contexts by extracting semantic features at different scales, and guides the model to enhance learning contextual information. In addition, a hybrid uncertainty module HUM is proposed, which guides the model to focus on segmentation boundary information by combining the global and local uncertainty information of two different networks to improve the segmentation performance of the networks at the boundary. In the left atrial segmentation and brain tumor segmentation dataset, validation experiments were conducted on the proposed model. The experiments show that our model achieves 89.84%, 79.89%, and 8.73 on the Dice metric, Jaccard metric, and 95HD metric, respectively, which significantly outperforms several current SOTA semi-supervised methods. This study confirms that the CIE and HUM strategies are effective. A semi-supervised segmentation framework is proposed for medical image segmentation.

Surface-based analysis of the cerebral cortex is ubiquitous in human neuroimaging with MRI. It is crucial for tasks like cortical registration, parcellation, and thickness estimation. Traditionally, such analyses require high-resolution, isotropic scans with good gray-white matter contrast, typically a T1-weighted scan with 1 mm resolution. This requirement precludes application of these techniques to most MRI scans acquired for clinical purposes, since they are often anisotropic and lack the required T1-weighted contrast. To overcome this limitation and enable large-scale neuroimaging studies using vast amounts of existing clinical data, we introduce recon-all-clinical, a novel methodology for cortical reconstruction, registration, parcellation, and thickness estimation for clinical brain MRI scans of any resolution and contrast. Our approach employs a hybrid analysis method that combines a convolutional neural network (CNN) trained with domain randomization to predict signed distance functions (SDFs), and classical geometry processing for accurate surface placement while maintaining topological and geometric constraints. The method does not require retraining for different acquisitions, thus simplifying the analysis of heterogeneous clinical datasets. We evaluated recon-all-clinical on multiple public datasets like ADNI, HCP, AIBL, OASIS and including a large clinical dataset of over 9,500 scans. The results indicate that our method produces geometrically precise cortical reconstructions across different MRI contrasts and resolutions, consistently achieving high accuracy in parcellation. Cortical thickness estimates are precise enough to capture aging effects, independently of MRI contrast, even though accuracy varies with slice thickness. Our method is publicly available at https://surfer.nmr.mgh.harvard.edu/fswiki/recon-all-clinical, enabling researchers to perform detailed cortical analysis on the huge amounts of already existing clinical MRI scans. This advancement may be particularly valuable for studying rare diseases and underrepresented populations where research-grade MRI data is scarce.

Exploring the dependencies between multimodal brain networks and integrating node features to enhance brain disease diagnosis remains a significant challenge. Some work has examined only brain connectivity changes in patients, ignoring important information about radiomics features such as shape and texture of individual brain regions in structural images. To this end, this study proposed a novel deep learning approach to integrate multimodal brain connectome information and regional radiomics features for brain disease diagnosis. A dual-branch autoencoder (ConnectomeAE) based on multimodal brain connectomes was proposed for brain disease diagnosis. Specifically, a matrix of radiomics feature extracted from structural magnetic resonance image (MRI) was used as Rad_AE branch inputs for learning important brain region features. Functional brain network built from functional MRI image was used as inputs to Cycle_AE for capturing brain disease-related connections. By separately learning node features and connection features from multimodal brain networks, the method demonstrates strong adaptability in diagnosing different brain diseases. ConnectomeAE was validated on two publicly available datasets. The experimental results show that ConnectomeAE achieved excellent diagnostic performance with an accuracy of 70.7 % for autism spectrum disorder and 90.5 % for Alzheimer's disease. A comparison of training time with other methods indicated that ConnectomeAE exhibits simplicity and efficiency suitable for clinical applications. Furthermore, the interpretability analysis of the model aligned with previous studies, further supporting the biological basis of ConnectomeAE. ConnectomeAE could effectively leverage the complementary information between multimodal brain connectomes for brain disease diagnosis. By separately learning radiomic node features and connectivity features, ConnectomeAE demonstrated good adaptability to different brain disease classification tasks.

Diffusion-weighted imaging (DWI) is a key modality for studying brain microstructure, but its signals are highly susceptible to noise due to the thermal motion of water molecules and interactions with tissue microarchitecture, leading to significant signal attenuation and a low signal-to-noise ratio (SNR). In this paper, we propose a novel approach, a Cycle-Conditional Diffusion Model (Cycle-CDM) using unpaired data learning, aimed at improving DWI quality and reliability through noise correction. Cycle-CDM leverages a cycle-consistent translation architecture to bridge the domain gap between noise-contaminated and noise-free DWIs, enabling the restoration of high-quality images without requiring paired datasets. By utilizing two conditional diffusion models, Cycle-CDM establishes data interrelationships between the two types of DWIs, while incorporating synthesized anatomical priors from the cycle translation process to guide noise removal. In addition, we introduce specific constraints to preserve anatomical fidelity, allowing Cycle-CDM to effectively learn the underlying noise distribution and achieve accurate denoising. Our experiments conducted on simulated datasets, as well as children and adolescents' datasets with strong clinical relevance. Our results demonstrate that Cycle-CDM outperforms comparative methods, such as U-Net, CycleGAN, Pix2Pix, MUNIT and MPPCA, in terms of noise correction performance. We demonstrated that Cycle-CDM can be generalized to DWIs with head motion when they were acquired using different MRI scannsers. Importantly, the denoised DWI data produced by Cycle-CDM exhibit accurate preservation of underlying tissue microstructure, thus substantially improving their medical applicability.