eICAB: A novel deep learning pipeline for Circle of Willis multiclass segmentation and analysis

Multi-modal pre-post treatment consistency learning for automatic segmentation and evaluation of the Circle of Willis

The Circle of Willis (CoW) is a crucial vascular structure in the brain, vital for diagnosing vascular diseases. During the acute phase of diseases, CT angiography (CTA) is commonly used to locate occlusions within the CoW quickly. After treatment, MR angiography (MRA) is preferred to visualize postoperative vascular structures, reducing radiation exposure. Clinically, the pre- and post-treatment (P&P-T) changes in the CoW are critical for assessing treatment efficacy. However, previous studies focused on single-modality segmentation, leading to cumulative errors when segmenting CoW in CTA and MRA modalities separately. Thus, it is challenging to differentiate whether changes in the CoW are due to segmentation errors or actual therapeutic effects when evaluating treatment efficacy. To address these challenges, we propose a comprehensive framework integrating the Cross-Modal Semantic Consistency Network (CMSC-Net) for segmentation and the Semantic Consistency Evaluation Network (SC-ENet) for treatment evaluation. Specifically, CMSC-Net includes two key components: the Modality Pair Alignment Module (MPAM), which generates spatially aligned modality pairs (CTA-MRA, MRA-CTA) to mitigate imaging discrepancies, and the Cross-Modal Attention Module (CMAM), which enhances CTA segmentation by leveraging high-resolution MRA features. Additionally, a novel loss function ensures semantic consistency across modalities, supporting stable network convergence. Meanwhile, SC-ENet automates treatment efficacy evaluation by extracting static vascular features and dynamically tracking morphological changes over time. Experimental results show that CTMSC-Net achieves consistent CoW segmentation across modalities, with SC-ENet delivering high-precision treatment evaluation.

Measuring arterial tortuosity in the cerebrovascular system using Time-of-Flight MRI

The Circle of Willis (CW) is a critical cerebrovascular structure that supports collateral blood flow to maintain brain perfusion and compensate for eventual occlusions. Increased tortuosity of high-risk vessels within the CW has been implicated as a marker in the progression of cerebrovascular diseases especially in structures like the internal carotid artery (ICA). This is partly due to age-related plaque deposition or arterial stiffening. Producing reliable tortuosity measurements for vessels segmented from magnetic resonance (MR) time-of-flight (TOF) images requires precise curvature estimation, but existent methods struggle with noisy or sparse segmentation data. We introduce an open-source, end-to-end pipeline that uses unit-speed spline fitting for accurate curvature estimation, generating robust curvature-based tortuosity metrics for the ICA combined with an indicator of spline fit quality. We test this with theoretical data and apply this method to TOF data from 22 participants. We report that our metrics are able to capture tortuosity even under heightened noise constraints and discriminate different types of abnormal arterial coiling. We found that our ICA tortuosity measures correlate positively with age and ultrasound measured carotid artery intima media thickness. This ultimately has important translational implications for being able to reliably generate TOF tortuosity measures and estimate cerebrovascular disease burden. We provide open-source code in a GitHub repository.

A Practical Roadmap to Implementing Deep Learning Segmentation in the Clinical Neuroimaging Research Workflow

BACKGROUND

Thanks to the proliferation of open-source tools, we are seeing an exponential growth of machine-learning applications, and its integration has become more accessible, particularly for segmentation tools in neuroimaging.

METHODS

This article explores a generalized methodology that harnesses these tools and aims/enables to expedite and enhance the reproducibility of clinical research. Herein, critical considerations include hardware, software, neural network training strategies, and data labeling guidelines. More specifically, we advocate an iterative approach to model training and transfer learning, focusing on internal validation and outlier handling early in the labeling process and fine-tuning later.

RESULTS

The iterative refinement process allows experts to intervene and improve model reliability while cutting down on their time spent in manual work. A seamless integration of the final model's predictions into clinical research is proposed to ensure standardized and reproducible results.

CONCLUSIONS

In short, this article provides a comprehensive framework for accelerating research using machine-learning techniques for image segmentation.

Time-of-Flight MRA of Intracranial Aneurysms with Interval Surveillance, Clinical Segmentation and Annotations

Intracranial aneurysms (IAs) are present in 2-6% of the global population and can be catastrophic upon rupture with a mortality rate of 30-50%. IAs are commonly detected through time-of-flight magnetic resonance angiography (TOF-MRA), however, this data is rarely available for research and training purposes. The provision of imaging resources such as TOF-MRA images is imperative to develop new strategies for IA detection, rupture prediction, and surgical training. To support efforts in addressing data availability bottlenecks, we provide an open-access TOF-MRA dataset comprising 63 patients, of which 24 underwent interval surveillance imaging by TOF-MRA. Patient scans were evaluated by a neuroradiologist, providing aneurysm and vessel segmentations, clinical annotations, 3D models, in addition to 3D Slicer software environments containing all this data for each patient. This dataset is the first to provide interval surveillance imaging for supporting the understanding of IA growth and stability. This dataset will support computational and experimental research into IA dynamics and assist surgical and radiology training in IA treatment.

Deep learning-based 3D cerebrovascular segmentation workflow on bright and black blood sequences magnetic resonance angiography

BACKGROUND

Cerebrovascular diseases have emerged as significant threats to human life and health. Effectively segmenting brain blood vessels has become a crucial scientific challenge. We aimed to develop a fully automated deep learning workflow that achieves accurate 3D segmentation of cerebral blood vessels by incorporating classic convolutional neural networks (CNNs) and transformer models.

METHODS

We used a public cerebrovascular segmentation dataset (CSD) containing 45 volumes of 1.5 T time-of-flight magnetic resonance angiography images. We collected data from another private middle cerebral artery (MCA) with lenticulostriate artery (LSA) segmentation dataset (MLD), which encompassed 3.0 T three-dimensional T1-weighted sequences of volumetric isotropic turbo spin echo acquisition MRI images of 107 patients aged 62 ± 11 years (42 females). The workflow includes data analysis, preprocessing, augmentation, model training with validation, and postprocessing techniques. Brain vessels were segmented using the U-Net, V-Net, UNETR, and SwinUNETR models. The model performances were evaluated using the dice similarity coefficient (DSC), average surface distance (ASD), precision (PRE), sensitivity (SEN), and specificity (SPE).

RESULTS

During 4-fold cross-validation, SwinUNETR obtained the highest DSC in each fold. On the CSD test set, SwinUNETR achieved the best DSC (0.853), PRE (0.848), SEN (0.860), and SPE (0.9996), while V-Net achieved the best ASD (0.99). On the MLD test set, SwinUNETR demonstrated good MCA segmentation performance and had the best DSC, ASD, PRE, and SPE for segmenting the LSA.

CONCLUSIONS

The workflow demonstrated excellent performance on different sequences of MRI images for vessels of varying sizes. This method allows doctors to visualize cerebrovascular structures.

CRITICAL RELEVANCE STATEMENT

A deep learning-based 3D cerebrovascular segmentation workflow is feasible and promising for visualizing cerebrovascular structures and monitoring cerebral small vessels, such as lenticulostriate arteries.

KEY POINTS

• The proposed deep learning-based workflow performs well in cerebrovascular segmentation tasks. • Among comparison models, SwinUNETR achieved the best DSC, ASD, PRE, and SPE values in lenticulostriate artery segmentation. • The proposed workflow can be used for different MR sequences, such as bright and black blood imaging.

FFCM-MRF: An accurate and generalizable cerebrovascular segmentation pipeline for humans and rhesus monkeys based on TOF-MRA

PURPOSE

Cerebrovascular segmentation and quantification of vascular morphological features in humans and rhesus monkeys are essential for prevention, diagnosis, and treatment of brain diseases. However, current automated whole-brain vessel segmentation methods are often not generalizable to independent datasets, limiting their usefulness in real-world environments with their heterogeneity in participants, scanners, and species.

MATERIALS AND METHODS

In this study, we proposed an automated, accurate and generalizable segmentation method for magnetic resonance angiography images called FFCM-MRF. This method integrated fast fuzzy c-means clustering and Markov random field optimization by vessel shape priors and spatial constraints. We used a total of 123 human and 44 macaque MRA images scanned at 1.5 T, 3 T, and 7 T MRI from 9 datasets to develop and validate the method.

RESULTS

FFCM-MRF achieved average Dice similarity coefficients ranging from 69.16 % to 89.63 % across multiple independent datasets, with improvements ranging from 3.24 % to 7.3 % compared to state-of-the-art methods. Quantitative analysis showed that FFCM-MRF can accurately segment major arteries in the Circle of Willis at the base of the brain and small distal pial arteries while effectively reducing noise. Test-retest analysis showed that the model yielded high vascular volume and diameter reliability.

CONCLUSIONS

Our results have demonstrated that FFCM-MRF is highly accurate and reliable and largely independent of variations in field strength, scanner platforms, acquisition parameters, and species. The macaque MRA data and user-friendly open-source toolbox are freely available at OpenNeuro and GitHub to facilitate studies of imaging biomarkers for cerebrovascular and neurodegenerative diseases.

Using deep learning for an automatic detection and classification of the vascular bifurcations along the Circle of Willis

Most of the intracranial aneurysms (ICA) occur on a specific portion of the cerebral vascular tree named the Circle of Willis (CoW). More particularly, they mainly arise onto fifteen of the major arterial bifurcations constituting this circular structure. Hence, for an efficient and timely diagnosis it is critical to develop some methods being able to accurately recognize each Bifurcation of Interest (BoI). Indeed, an automatic extraction of the bifurcations presenting the higher risk of developing an ICA would offer the neuroradiologists a quick glance at the most alarming areas. Due to the recent efforts on Artificial Intelligence, Deep Learning turned out to be the best performing technology for many pattern recognition tasks. Moreover, various methods have been particularly designed for medical image analysis purposes. This study intends to assist the neuroradiologists to promptly locate any bifurcation presenting a high risk of ICA occurrence. It can be seen as a Computer Aided Diagnosis scheme, where the Artificial Intelligence facilitates the access to the regions of interest within the MRI. In this work, we propose a method for a fully automatic detection and recognition of the bifurcations of interest forming the Circle of Willis. Several neural networks architectures have been tested, and we thoroughly evaluate the bifurcation recognition rate.

Vessel Distance Mapping: A novel methodology for assessing vascular-induced cognitive resilience

The association between cerebral blood supply and cognition has been widely discussed in the recent literature. One focus of this discussion has been the anatomical variability of the circle of Willis, with morphological differences being present in more than half of the general population. While previous studies have attempted to classify these differences and explore their contribution to hippocampal blood supply and cognition, results have been controversial. To disentangle these previously inconsistent findings, we introduce Vessel Distance Mapping (VDM) as a novel methodology for evaluating blood supply, which allows for obtaining vessel pattern metrics with respect to the surrounding structures, extending the previously established binary classification into a continuous spectrum. To accomplish this, we manually segmented hippocampal vessels obtained from high-resolution 7T time-of-flight MR angiographic imaging in older adults with and without cerebral small vessel disease, generating vessel distance maps by computing the distances of each voxel to its nearest vessel. Greater values of VDM-metrics, which reflected higher vessel distances, were associated with poorer cognitive outcomes in subjects affected by vascular pathology, while this relation was not observed in healthy controls. Therefore, a mixed contribution of vessel pattern and vessel density is proposed to confer cognitive resilience, consistent with previous research findings. In conclusion, VDM provides a novel platform, based on a statistically robust and quantitative method of vascular mapping, for addressing a variety of clinical research questions.