Unsupervised Cerebrovascular Segmentation of TOF-MRA Images Based on Deep Neural Network and Hidden Markov Random Field Model

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.

COSTA: A Multi-Center TOF-MRA Dataset and a Style Self-Consistency Network for Cerebrovascular Segmentation

Time-of-flight magnetic resonance angiography (TOF-MRA) is the least invasive and ionizing radiation-free approach for cerebrovascular imaging, but variations in imaging artifacts across different clinical centers and imaging vendors result in inter-site and inter-vendor heterogeneity, making its accurate and robust cerebrovascular segmentation challenging. Moreover, the limited availability and quality of annotated data pose further challenges for segmentation methods to generalize well to unseen datasets. In this paper, we construct the largest and most diverse TOF-MRA dataset (COSTA) from 8 individual imaging centers, with all the volumes manually annotated. Then we propose a novel network for cerebrovascular segmentation, namely CESAR, with the ability to tackle feature granularity and image style heterogeneity issues. Specifically, a coarse-to-fine architecture is implemented to refine cerebrovascular segmentation in an iterative manner. An automatic feature selection module is proposed to selectively fuse global long-range dependencies and local contextual information of cerebrovascular structures. A style self-consistency loss is then introduced to explicitly align diverse styles of TOF-MRA images to a standardized one. Extensive experimental results on the COSTA dataset demonstrate the effectiveness of our CESAR network against state-of-the-art methods. We have made 6 subsets of COSTA with the source code online available, in order to promote relevant research in the community.

VesselBoost: A Python Toolbox for Small Blood Vessel Segmentation in Human Magnetic Resonance Angiography Data

Magnetic resonance angiography (MRA) performed at ultra-high magnetic field provides a unique opportunity to study the arteries of the living human brain at the mesoscopic level. From this, we can gain new insights into the brain's blood supply and vascular disease affecting small vessels. However, for quantitative characterization and precise representation of human angioarchitecture to, for example, inform blood-flow simulations, detailed segmentations of the smallest vessels are required. Given the success of deep learning-based methods in many segmentation tasks, we here explore their application to high-resolution MRA data, and address the difficulty of obtaining large data sets of correctly and comprehensively labelled data. We introduce VesselBoost, a vessel segmentation package, which utilizes deep learning and imperfect training labels for accurate vasculature segmentation. Combined with an innovative data augmentation technique, which leverages the resemblance of vascular structures, VesselBoost enables detailed vascular segmentations.

TL-MSE2-Net: Transfer learning based nested model for cerebrovascular segmentation with aneurysms

Cerebrovascular (i.e., cerebral vessel) segmentation is essential for diagnosing and treating brain diseases. Convolutional neural network models, such as U-Net, are commonly used for this purpose. Unfortunately, such models may not be entirely satisfactory in dealing with cerebrovascular segmentation with tumors due to the following issues: (1) Relatively small number of clinical datasets from patients obtained through different modalities such as computed tomography (CT) and magnetic resonance imaging (MRI), leading to inadequate training and lack of transferability in the modeling; (2) Insufficient feature extraction caused by less attention to both convolution sizes and cerebral vessel edges. Inspired by the existence of similar features on cerebral vessels between normal subjects and patients, we propose a transfer learning strategy based on a pre-trained nested model called TL-MSE2-Net. This model uses one of the publicly available datasets for cerebrovascular segmentation with aneurysms. To address issue (1), our transfer learning strategy leverages a pre-trained model that uses a large number of datasets from normal subjects, providing a potential solution to the lack of sufficient clinical datasets. To tackle issue (2), we structure the pre-trained model based on 3D U-Net, comprising three blocks: ResMul, DeRes, and REAM. The ResMul and DeRes blocks enhance feature extraction by utilizing multiple convolution sizes to capture multiscale features, and the REAM block increases the weight of the voxels on the edges of the given 3D volume. We evaluated the proposed model on one small private clinical dataset and two publicly available datasets. The experimental results demonstrated that our MSE2-Net framework achieved an average Dice score of 70.81 % and 89.08 % on the two publicly available datasets, outperforming other state-of-the-art methods. Ablation studies were also conducted to validate the effectiveness of each block. The proposed TL-MSE2-Net yielded better results than MSE2-Net on a small private clinical dataset, with increases of 5.52 %, 3.37 %, 6.71 %, and 0.85 % for the Dice score, sensitivity, Jaccard index, and precision, respectively.

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.

Tubular Structure Segmentation via Multi-Scale Reverse Attention Sparse Convolution

Cerebrovascular and airway structures are tubular structures used for transporting blood and gases, respectively, providing essential support for the normal activities of the human body. Accurately segmenting these tubular structures is the basis of morphology research and pathological detection. Nevertheless, accurately segmenting these structures from images presents great challenges due to their complex morphological and topological characteristics. To address this challenge, this paper proposes a framework UARAI based on the U-Net multi-scale reverse attention network and sparse convolution network. The framework utilizes a multi-scale structure to effectively extract the global and deep detail features of vessels and airways. Further, it enhances the extraction ability of fine-edged features by a joint reverse attention module. In addition, the sparse convolution structure is introduced to improve the features' expression ability without increasing the model's complexity. Finally, the proposed training sample cropping strategy reduces the influence of block boundaries on the accuracy of tubular structure segmentation. The experimental findings demonstrate that the UARAI-based metrics, namely Dice and IoU, achieve impressive scores of 90.31% and 82.33% for cerebrovascular segmentation and 93.34% and 87.51% for airway segmentation, respectively. Compared to commonly employed segmentation techniques, the proposed method exhibits remarkable accuracy and robustness in delineating tubular structures such as cerebrovascular and airway structures. These results hold significant promise in facilitating medical image analysis and clinical diagnosis, offering invaluable support to healthcare professionals.

All answers are in the images: A review of deep learning for cerebrovascular segmentation

Cerebrovascular imaging is a common examination. Its accurate cerebrovascular segmentation become an important auxiliary method for the diagnosis and treatment of cerebrovascular diseases, which has received extensive attention from researchers. Deep learning is a heuristic method that encourages researchers to derive answers from the images by driving datasets. With the continuous development of datasets and deep learning theory, it has achieved important success for cerebrovascular segmentation. Detailed survey is an important reference for researchers. To comprehensively analyze the newest cerebrovascular segmentation, we have organized and discussed researches centered on deep learning. This survey comprehensively reviews deep learning for cerebrovascular segmentation since 2015, it mainly includes sliding window based models, U-Net based models, other CNNs based models, small-sample based models, semi-supervised or unsupervised models, fusion based models, Transformer based models, and graphics based models. We organize the structures, improvement, and important parameters of these models, as well as analyze development trends and quantitative assessment. Finally, we have discussed the challenges and opportunities of possible research directions, hoping that our survey can provide researchers with convenient reference.

The automatic evaluation of steno-occlusive changes in time-of-flight magnetic resonance angiography of moyamoya patients using a 3D coordinate attention residual network

Background

Moyamoya disease (MMD) is a rare cerebrovascular occlusive disease with progressive stenosis of the terminal portion of internal cerebral artery (ICA) and its main branches, which can cause complications, such as high risks of disability and increased mortality. Accurate and timely diagnosis may be difficult for physicians who are unfamiliar to MMD. Therefore, this study aims to achieve a preoperative deep-learning-based evaluation of MMD by detecting steno-occlusive changes in the middle cerebral artery or distal ICA areas.

Methods

A fine-tuned deep learning model was developed using a three-dimensional (3D) coordinate attention residual network (3D CA-ResNet). This study enrolled 50 preoperative patients with MMD and 50 controls, and the corresponding time of flight magnetic resonance angiography (TOF-MRA) imaging data were acquired. The 3D CA-ResNet was trained based on sub-volumes and tested using patch-based and subject-based methods. The performance of the 3D CA-ResNet, as evaluated by the area under the curve (AUC) of receiving-operator characteristic, was compared with that of three other conventional 3D networks.

Results

With the resulting network, the patch-based test achieved an AUC value of 0.94 for the 3D CA-ResNet in 480 patches from 10 test patients and 10 test controls, which is significantly higher than the results of the others. The 3D CA-ResNet correctly classified the MMD patients and normal healthy controls, and the vascular lesion distribution in subjects with the disease was investigated by generating a stenosis probability map and 3D vascular structure segmentation.

Conclusions

The results demonstrated the reliability of the proposed 3D CA-ResNet in detecting stenotic areas on TOF-MRA imaging, and it outperformed three other models in identifying vascular steno-occlusive changes in patients with MMD.

Human treelike tubular structure segmentation: A comprehensive review and future perspectives

Various structures in human physiology follow a treelike morphology, which often expresses complexity at very fine scales. Examples of such structures are intrathoracic airways, retinal blood vessels, and hepatic blood vessels. Large collections of 2D and 3D images have been made available by medical imaging modalities such as magnetic resonance imaging (MRI), computed tomography (CT), Optical coherence tomography (OCT) and ultrasound in which the spatial arrangement can be observed. Segmentation of these structures in medical imaging is of great importance since the analysis of the structure provides insights into disease diagnosis, treatment planning, and prognosis. Manually labelling extensive data by radiologists is often time-consuming and error-prone. As a result, automated or semi-automated computational models have become a popular research field of medical imaging in the past two decades, and many have been developed to date. In this survey, we aim to provide a comprehensive review of currently publicly available datasets, segmentation algorithms, and evaluation metrics. In addition, current challenges and future research directions are discussed.

Threshold field painting saves the time for segmentation of minute arteries

PURPOSE

It is often time-consuming to segment fine structures, such as the cerebral arteries from magnetic resonance imaging (MRI). Moreover, extracting anatomically abnormal structures is generally difficult. The segmentation workflow called threshold field painting was tested for its feasibility in morbid minute artery segmentation with special emphasis on time efficiency.

METHODS

Seven patients with meningioma with ten-sided feeding arteries (n = 10) originating from middle meningeal arteries (MMA) were investigated by three experts of the conventional method for segmentation. The MRI time-of-flight sequence was utilized for the segmentation of each procedure. The tasks were accomplished using both the conventional method and the proposed method in random order. The task completion time and usability score were analyzed using the Wilcoxon signed-rank test.

RESULTS

Except for one examinee (P = 0.06), the completion time significantly decreased (both P < 0.01) with the use of the proposed method. The average task completion time among the three examinees for the conventional method was 2.8 times longer than that for the proposed method. The usability score was generally in favor of the proposed method.

CONCLUSION

The normally nonexistent minute arteries, such as the MMA feeders, were deemed more efficiently segmented with the proposed method than with the conventional method. While automatic segmentation might be the ultimate solution, our semiautomatic method incorporating expert knowledge is expected to work as the practical solution.

Nonconvex Nonlocal Tucker Decomposition for 3D Medical Image Super-Resolution

Limited by hardware conditions, imaging devices, transmission efficiency, and other factors, high-resolution (HR) images cannot be obtained directly in clinical settings. It is expected to obtain HR images from low-resolution (LR) images for more detailed information. In this article, we propose a novel super-resolution model for single 3D medical images. In our model, nonlocal low-rank tensor Tucker decomposition is applied to exploit the nonlocal self-similarity prior knowledge of data. Different from the existing methods that use a convex optimization for tensor Tucker decomposition, we use a tensor folded-concave penalty to approximate a nonlocal low-rank tensor. Weighted 3D total variation (TV) is used to maintain the local smoothness across different dimensions. Extensive experiments show that our method outperforms some state-of-the-art (SOTA) methods on different kinds of medical images, including MRI data of the brain and prostate and CT data of the abdominal and dental.

Study of Vertebral Artery Dissection by Ultrasound Superb Microvascular Imaging Based on Deep Neural Network Model

To assess the diagnostic value of ultrasound Superb Microvascular Imaging (SMI) and versus Doppler ultrasound (TCD) for microvascular structure and aerodynamic changes in vertebral artery dissection (VAD). In this paper, we firstly simulate the process of clinician recognition of vertebral artery dissection and propose a combination of a priori shape information of vertebral artery dissection and deep folly convolutional networks (DFCNs) for IVUS. In this paper, 15 patients with vertebral artery dissection confirmed by SMI, digital subtraction angiography (DSA), or computed tomography angiography (CTA) from 2020 to 2021 were selected, and the true and false lumen diameters, peak systolic flow velocity (PSV), end-diastolic flow velocity (EDV) and PSV, EDV, and plasticity index (PI) of the intracranial vertebral artery were measured. Among the 15 patients with VAD, 4 (27%, 4/15) had trauma-induced secondary vertebral artery entrapment and 11 (73%, 11/15) had spontaneous entrapment without a clear cause. According to the structural characteristics of the vessels, there were 11 cases (73%, 11/15) of double-lumen, intramural hematoma, and vertebral artery dissection aneurysm, and 11 cases (73%, 11/15) of V1 segment. SMI not only provides an objective assessment of the vascular morphology and aerodynamic changes in VAD but also, in combination with TCD, can further determine the opening of the traffic branches in the posterior circulation, providing reliable information for the early diagnosis and treatment of microvascular dissection of the vertebral artery.

Relationship between Circle of Willis Variations and Cerebral or Cervical Arteries Stenosis Investigated by Computer Tomography Angiography and Multitask Convolutional Neural Network

Circle of Willis (CoW) is the most critical collateral pathway that supports the redistribution of blood supply in the brain. The variation of CoW is closely correlated with cerebral hemodynamic and cerebral vessel-related diseases. But what is responsible for CoW variation remains unclear. Moreover, the visual evaluation for CoW variation is highly time-consuming. In the present study, based on the computer tomography angiography (CTA) dataset from 255 patients, the correlation between the CoW variations with age, gender, and cerebral or cervical artery stenosis was investigated. A multitask convolutional neural network (CNN) was used to segment cerebral arteries automatically. The results showed the prevalence of variation of the anterior communicating artery (Aco) was higher in the normal senior group than in the normal young group and in females than in males. The changes in the prevalence of variations of individual segments were not demonstrated in the population with stenosis of the afferent and efferent arteries, so the critical factors for variation are related to genetic or physiological factors rather than pathological lesions. Using the multitask CNN model, complete cerebral and cervical arteries could be segmented and reconstructed in 120 seconds, and an average Dice coefficient of 78.2% was achieved. The segmentation accuracy for precommunicating part of anterior cerebral artery and posterior cerebral artery, the posterior communicating arteries, and Aco in CoW was 100%, 99.2%, 94%, and 69%, respectively. Artificial intelligence (AI) can be considered as an adjunct tool for detecting the CoW, particularly related to reducing workload and improving the accuracy of the visual evaluation. The study will serve as a basis for the following research to determine an individual's risk of stroke with the aid of AI.

Learning From Mouse CT-Scan Brain Images To Detect MRA-TOF Human Vasculatures

The earlier studies on brain vasculature semantic segmentation used classical image analysis methods to extract the vascular tree from images. Nowadays, deep learning methods are widely exploited for various image analysis tasks. One of the strong restrictions when dealing with neural networks in the framework of semantic segmentation is the need to dispose of a ground truth segmentation dataset, on which the task will be learned. It may be cumbersome to manually segment the arteries in a 3D volumes (MRA-TOF typically). In this work, we aim to tackle the vascular tree segmentation from a new perspective. Our objective is to build an image dataset from mouse vasculatures acquired using CT-Scans, and enhance these vasculatures in such a way to precisely mimic the statistical properties of the human brain. The segmentation of mouse images is easily automatized thanks to their specific acquisition modality. Thus, such a framework allows to generate the data necessary for the training of a Convolutional Neural Network - i.e. the enhanced mouse images and there corresponding ground truth segmentation - without requiring any manual segmentation procedure. However, in order to generate an image dataset having consistent properties (strong resemblance with MRA images), we have to ensure that the statistical properties of the enhanced mouse images do match correctly the human MRA acquisitions. In this work, we evaluate at length the similarities between the human arteries as acquired on MRA-TOF and the "humanized" mouse arteries produced by our model. Finally, once the model duly validated, we experiment its applicability with a Convolutional Neural Network.

A nested parallel multiscale convolution for cerebrovascular segmentation

PURPOSE

Cerebrovascular segmentation in magnetic resonance imaging (MRI) plays an important role in the diagnosis and treatment of cerebrovascular diseases. Many segmentation frameworks based on convolutional neural networks (CNNs) or U-Net-like structures have been proposed for cerebrovascular segmentation. Unfortunately, the segmentation results are still unsatisfactory, particularly in the small/thin cerebrovascular due to the following reasons: (1) the lack of attention to multiscale features in encoder caused by the convolutions with single kernel size; (2) insufficient extraction of shallow and deep-seated features caused by the depth limitation of transmission path between encoder and decoder; (3) insufficient utilization of the extracted features in decoder caused by less attention to multiscale features.

METHODS

Inspired by U-Net++, we propose a novel 3D U-Net-like framework termed Usception for small cerebrovascular. It includes three blocks: Reduction block, Gap block, and Deep block, aiming to: (1) improve feature extraction ability by grouping different convolution sizes; (2) increase the number of multiscale features in different layers by grouping paths of different depths between encoder and decoder; (3) maximize the ability of decoder in recovering multiscale features from Reduction and Gap block by using convolutions with different kernel sizes.

RESULTS

The proposed framework is evaluated on three public and in-house clinical magnetic resonance angiography (MRA) data sets. The experimental results show that our framework reaches an average dice score of 69.29%, 87.40%, 77.77% on three data sets, which outperform existing state-of-the-art methods. We also validate the effectiveness of each block through ablation experiments.

CONCLUSIONS

By means of the combination of Inception-ResNet and dimension-expanded U-Net++, the proposed framework has demonstrated its capability to maximize multiscale feature extraction, thus achieving competitive segmentation results for small cerebrovascular.

Cerebrovascular segmentation from TOF-MRA based on multiple-U-net with focal loss function

BACKGROUND AND OBJECTIVE

Accurate cerebrovascular segmentation plays an important role in the diagnosis of cerebrovascular diseases. Considering the complexity and uncertainty of doctors' manual segmentation of cerebral vessels, this paper proposed an automatic segmentation algorithm based on Multiple-U-net (M-U-net) to segment cerebral vessel structures from the Time-of-flight Magnetic Resonance Angiography (TOF-MRA) data.

METHODS

First, the TOF-MRA data was normalized by volume and then divided into three groups through slices of axial, coronal and sagittal directions respectively. Three single U-nets were trained by divided dataset. To solve the problem of uneven distribution of positive and negative samples, the focal loss function was adopted in training. After obtaining the prediction results of three single U-nets, the voting feature fusion and the post-processing process based on connected domain analysis would be performed. 95 volumes of TOF-MRA provided by the MIDAS platform were applied to the experiment, among which 20 volumes were treated as the training dataset, 5 volumes were used as the validation dataset and the remaining 70 volumes were divided into 10 groups to test the trained model respectively.

RESULTS

Experiments showed that the proposed M-U-net based algorithm achieved 88.60% and 87.93% Dice Similarity Coefficient (DSC) on the verification dataset and testing dataset, which performed better than any single U-net.

CONCLUSIONS

Compared with other existing algorithms, our algorithm reached the state of the art level. The feature fusion of three single U-nets could effectively complement the segmentation results.

Algorithms for segmenting cerebral time-of-flight magnetic resonance angiograms from volunteers and anemic patients

Purpose: To evaluate six cerebral arterial segmentation algorithms in a set of patients with a wide range of hemodynamic characteristics to determine real-world performance. Approach: Time-of-flight magnetic resonance angiograms were acquired from 33 subjects: normal controls ( N = 11 ), sickle cell disease ( N = 11 ), and non-sickle anemia ( N = 11 ) using a 3 Tesla Philips Achieva scanner. Six segmentation algorithms were tested: (1) Otsu's method, (2) K-means, (3) region growing, (4) active contours, (5) minimum cost path, and (6) U-net machine learning. Segmentation algorithms were tested with two region-selection methods: global, which selects the entire volume; and local, which iteratively tracks the arteries. Five slices were manually segmented from each patient by two readers. Agreement between manual and automatic segmentation was measured using Matthew's correlation coefficient (MCC). Results: Median algorithm segmentation times ranged from 0.1 to 172.9 s for a single angiogram versus 10 h for manual segmentation. Algorithms had inferior performance to inter-observer vessel-based ( p < 0.0001 , MCC = 0.65 ) and voxel-based ( p < 0.0001 , MCC = 0.73 ) measurements. There were significant differences between algorithms ( p < 0.0001 ) and between patients ( p < 0.0042 ). Post-hoc analyses indicated (1) local minimum cost path performed best with vessel-based ( p = 0.0261 , MCC = 0.50 ) and voxel-based ( p = 0.0131 , MCC = 0.66 ) analyses; and (2) higher vessel-based performance in non-sickle anemia ( p = 0.0002 ) and lower voxel-based performance in sickle cell ( p = 0.0422 ) compared with normal controls. All reported MCCs are medians. Conclusions: The best-performing algorithm (local minimum cost path, voxel-based) had 9.59% worse performance than inter-observer agreement but was 3 orders of magnitude faster. Automatic segmentation was non-inferior in patients with sickle cell disease and superior in non-sickle anemia.

Effects of site, cerebral perfusion and degree of cerebral artery stenosis on cognitive function

OBJECTIVE

To investigate the effects of site, cerebral perfusion and degree of cerebral artery stenosis (CAS) on cognitive function.

METHODS

A total of 57 patients with CAS and 53 controls from January 2019 to December 2019 were included. The former group was further divided into different subgroups according to the site, cerebral perfusion and degree of CAS. A series of neuropsychological tests were performed to evaluate the cognitive domains (such as memory, executive function, psychomotor speed, etc.). Rank sum test, t test, Chi-square test and analysis of variance were used for data analysis. Spearman correlation analysis was used to examine the relationship between the site, cerebral perfusion and degree of CAS and all tests' scores.

RESULTS

For patients with CAS who have decreased cerebral perfusion, their global cognitive function, memory, psychomotor speed, executive function and frontal lobe function were significantly impaired (all P < 0.05). There was a significant decrease in global cognitive function, psychomotor speed, memory, executive function and frontal lobe function in patients with anterior circulation stenosis (all P < 0.05). Moderate and severe CAS impaired subjects' global cognitive function, memory, psychomotor speed, executive function and frontal lobe function (all P < 0.05). There was a correlation between the site, cerebral perfusion, the degree of CAS and cognitive function.

CONCLUSION

Global cognitive function, memory, psychomotor speed, frontal lobe function and executive function are impaired in patients with CAS, especially in those with anterior circulatory stenosis, moderate to severe stenosis and low cerebral perfusion.See Video Abstract, http://links.lww.com/WNR/A613.

Fully automatic deep learning trained on limited data for carotid artery segmentation from large image volumes

BACKGROUND

The objectives of this study were to develop a 3D convolutional deep learning framework (CarotidNet) for fully automatic segmentation of carotid bifurcations in computed tomography angiography (CTA) images and to facilitate the quantification of carotid stenosis and risk assessment of stroke.

METHODS

Our pipeline was a two-stage cascade network that included a localization phase and a segmentation phase. The network framework was based on the 3D version of U-Net, but was refined in three ways: (I) by adding residual connections and a deep supervision strategy to cope with the vanishing problem in back-propagation; (II) by adopting dilated convolution in order to strengthen the capacity to capture contextual information; and (III) by establishing a hybrid objective function to address the extreme imbalance between foreground and background voxels.

RESULTS

We trained our networks on 15 cases and evaluated their performance based on 41 cases from the MICCAI Challenge 2009 dataset. A Dice similarity coefficient of 82.3% was achieved for the test cases.

CONCLUSIONS

We developed a carotid segmentation method based on U-Net that can segment tiny carotid bifurcation lumens from very large backgrounds with no manual intervention. This was the first attempt to use deep learning to achieve carotid bifurcation segmentation in 3D CTA images. Our results indicate that deep learning is a promising method for automatically extracting carotid bifurcation lumens.

Topics and trends in artificial intelligence assisted human brain research

Artificial intelligence (AI) assisted human brain research is a dynamic interdisciplinary field with great interest, rich literature, and huge diversity. The diversity in research topics and technologies keeps increasing along with the tremendous growth in application scope of AI-assisted human brain research. A comprehensive understanding of this field is necessary to assess research efficacy, (re)allocate research resources, and conduct collaborations. This paper combines the structural topic modeling (STM) with the bibliometric analysis to automatically identify prominent research topics from the large-scale, unstructured text of AI-assisted human brain research publications in the past decade. Analyses on topical trends, correlations, and clusters reveal distinct developmental trends of these topics, promising research orientations, and diverse topical distributions in influential countries/regions and research institutes. These findings help better understand scientific and technological AI-assisted human brain research, provide insightful guidance for resource (re)allocation, and promote effective international collaborations.