Identification of cortical arteries and veins in awake mice using two-photon microscopy.

Distinguishing arteries from veins in the cerebral cortex is critical for studying hemodynamics under pathophysiological conditions, which plays an important role in the diagnosis and treatment of various vessel-related diseases. However, due to the complexity of the cerebral vascular network, it is challenging to identify arteries and veins in vivo. Here, we demonstrate an artery-vein separation method that employs a combination of multiple scanning modes of two-photon microscopy and a custom-designed stereoscopic fixation device for mice. In this process, we propose a novel method for determining the line scanning direction, which allows us to determine the blood flow directions. The vasculature branches have been identified using an optimized z-stack scanning mode, followed by the separation of blood vessel types according to the directions of blood flow and branching patterns. Using this strategy, the penetrating arterioles and penetrating venules in awake mice could be accurately identified and the type of cerebral thrombus has been also successfully isolated without any empirical knowledge or algorithms. Our research presents a new, more accurate, and efficient method for cortical artery-vein separation in awake mice, providing a useful strategy for the application of two-photon microscopy in the study of cerebrovascular pathophysiology.

Transformer-based 3D U-Net for pulmonary vessel segmentation and artery-vein separation from CT images.

Transformer-based methods have led to the revolutionizing of multiple computer vision tasks. Inspired by this, we propose a transformer-based network with a channel-enhanced attention module to explore contextual and spatial information in non-contrast (NC) and contrast-enhanced (CE) computed tomography (CT) images for pulmonary vessel segmentation and artery-vein separation. Our proposed network employs a 3D contextual transformer module in the encoder and decoder part and a double attention module in skip connection to effectively finish high-quality vessel and artery-vein segmentation. Extensive experiments are conducted on the in-house dataset and the ISICDM2021 challenge dataset. The in-house dataset includes 56 NC CT scans with vessel annotations and the challenge dataset consists of 14 NC and 14 CE CT scans with vessel and artery-vein annotations. For vessel segmentation, Dice is 0.840 for CE CT and 0.867 for NC CT. For artery-vein separation, the proposed method achieves a Dice of 0.758 of CE images and 0.602 of NC images. Quantitative and qualitative results demonstrated that the proposed method achieved high accuracy for pulmonary vessel segmentation and artery-vein separation. It provides useful support for further research associated with the vascular system in CT images. The code is available at https://github.com/wuyanan513/Pulmonary-Vessel-Segmentation-and-Artery-vein-Separation .

Learning multi-view and centerline topology connectivity information for pulmonary artery-vein separation.

BACKGROUND: Automatic pulmonary artery-vein separation has considerable importance in the diagnosis and treatment of lung diseases. However, insufficient connectivity and spatial inconsistency have always been the problems of artery-vein separation. METHODS: A novel automatic method for artery-vein separation in CT images is presented in this work. Specifically, a multi-scale information aggregated network (MSIA-Net) including multi-scale fusion blocks and deep supervision, is proposed to learn the features of artery-vein and aggregate additional semantic information, respectively. The proposed method integrates nine MSIA-Net models for artery-vein separation, vessel segmentation, and centerline separation tasks along with axial, coronal, and sagittal multi-view slices. First, the preliminary artery-vein separation results are obtained by the proposed multi-view fusion strategy (MVFS). Then, centerline correction algorithm (CCA) is used to correct the preliminary results of artery-vein separation by the centerline separation results. Finally, the vessel segmentation results are utilized to reconstruct the artery-vein morphology. In addition, weighted cross-entropy and dice loss are employed to solve the class imbalance problem. RESULTS: We constructed 50 manually labeled contrast-enhanced computed CT scans for five-fold cross-validation, and experimental results demonstrated that our method achieves superior segmentation performance of 97.7%, 85.1%, and 84.9% on ACC, Pre, and DSC, respectively. Additionally, a series of ablation studies demonstrate the effectiveness of the proposed components. CONCLUSION: The proposed method can effectively solve the problem of insufficient vascular connectivity and correct the spatial inconsistency of artery-vein.

In vivo identification of arteries and veins using two-photon excitation elastin autofluorescence.

Distinguishing arteries from veins in vivo has a great significance in clinical practices and preclinical studies. Optical imaging methods such as two-photon microscopy can provide high-resolution morphological information of tissue and are therefore extremely suitable for imaging small blood vessels. However, few optical imaging methods allow in vivo identification of arteries and veins merely utilizing the autofluorescence signal of blood vessels. In this report, we found the arterial wall generates a remarkably stronger two-photon excitation autofluorescence (TPEA) signal compared with the venous wall based on BALB/c mice. According to histological analysis and fluorescence characteristic measurement, the contrast signal is confirmed to be from elastin fibers. Employing this unique feature, we propose an objective and effective artery-vein separation strategy that considers the presence of the elastin-TPEA border as the indicator of arteries. Using this strategy, the arterial and venous networks of the dorsal skin and cerebral cortex of BALB/c mice are demonstrated to be excellently mapped and accurately separated in vivo without depending on any exogenous contrast agent, empirical knowledge, and algorithm. This study may provide a novel technique for mapping arterial and venous networks for anatomic research as well as an extra aid to basic researches on the mechanism, diagnosis, and treatment of blood vessel-related diseases.

Statistical modeling and knowledge-based segmentation of cerebral artery based on TOF-MRA and MR-T1.

BACKGROUND AND OBJECTIVE: For cerebrovascular segmentation from time-of-flight (TOF) magnetic resonance angiography (MRA), the focused issues are segmentation accuracy, vascular network coverage ratio, and cerebral artery and vein (CA/CV) separation. Therefore, cerebral artery segmentation is a challenging work, while a complete solution is lacking so far. METHODS: The preprocessing of skull-stripping and Hessian-based feature extraction is first implemented to acquire an indirect prior knowledge of vascular distribution and shape. Then, a novel intensity- and shape-based Markov statistical modeling is proposed for complete cerebrovascular segmentation, where our low-level process employs a Gaussian mixture model to fit the intensity histogram of the skull-stripped TOF-MRA data, while our high-level process employs the vascular shape prior to construct the energy function. To regularize the individual data processes, Markov regularization parameter is automatically estimated by using a machine-learning algorithm. Further, cerebral artery and vein (CA/CV) separation is explored with a series of morphological logic operations, which are based on a direct priori knowledge on the relationship of arteriovenous topology and brain tissues in between TOF-MRA and MR-T1. RESULTS: We employed 109 sets of public datasets from MIDAS for qualitative and quantitative assessment. The Dice similarity coefficient, false negative rate (FNR), and false positive rate (FPR) of 0.933, 0.158, and 0.091% on average, as well as CA/CV separation results with the agreement, FNR, and FPR of 0.976, 0.041, and 0.022 on average. For clinical visual assessment, our methods can segment various sizes of the vessel in different contrast region, especially performs better on vessels of small size in low contrast region. CONCLUSION: Our methods obtained satisfying results in visual and quantitative evaluation. The proposed method is capable of accurate cerebrovascular segmentation and efficient CA/CV separation. Further, it can stimulate valuable clinical applications on the computer-assisted cerebrovascular intervention according to the neurosurgeon's recommendation.

Additive value of split-bolus single-phase CT scan protocol for preoperative assessment of lung cancer patients referred for video-assisted thoracic surgery.

We aimed to assess the additive value of the split-bolus single-phase computed tomography (CT) scan protocol to preoperatively assess patients with lung cancer, who were referred for video-assisted thoracic surgery, when compared to a standard staging CT protocol. We included 160 patients with lung cancer who underwent a split-bolus single-phase CT scan protocol (split-bolus protocol), which can acquire whole-body staging CT and pulmonary artery-vein separation CT angiography (PA-PV CTA) in a single acquisition and 160 patients who underwent whole-body staging CT (standard protocol). We compared the quality of the staging CT images of hepatic parenchyma, portal vein, and hepatic vein between both protocols. We also investigated image quality on PA-PV CTA images in the split-bolus protocol and recorded the number of patients that underwent the 3D PA-PV CTA imaging process. The split-bolus protocol for staging CT images demonstrated a slightly higher enhancement with regard to the hepatic parenchyma (p = 0.007) and hepatic vein (p = 0.006) than the standard protocol. There was no significant difference in the quality of the staging CT images between both protocols (p = 0.067). The mean CT number for the main pulmonary artery and the left atrium for the PA-PV CTA images in the split-bolus protocol were 289.1 HU and 172.8 HU, respectively. Among the images associated with the split-bolus protocol, 98.1% were of appropriate quality for 3D PA-PV CTA imaging. The split-bolus protocol is a dose-efficient protocol to acquire the staging CT and PA-PV CTA images in a single session and provides sufficient image quality for preoperative assessment in patients with lung cancer.

Healthy Lung Vessel Morphology Derived From Thoracic Computed Tomography.

Knowledge of the lung vessel morphology in healthy subjects is necessary to improve our understanding about the functional network of the lung and to recognize pathologic deviations beyond the normal inter-subject variation. Established values of normal lung morphology have been derived from necropsy material of only very few subjects. In order to determine morphologic readouts from a large number of healthy subjects, computed tomography pulmonary angiography (CTPA) datasets, negative for pulmonary embolism, and other thoracic pathologies, were analyzed using a fully-automatic, in-house developed artery/vein separation algorithm. The number, volume, and tortuosity of the vessels in a diameter range between 2 and 10 mm were determined. Visual inspection of all datasets was used to exclude subjects with poor image quality or inadequate artery/vein separation from the analysis. Validation of the algorithm was performed manually by a radiologist on randomly selected subjects. In 123 subjects (men/women: 55/68), aged 59 ± 17 years, the median overlap between visual inspection and fully-automatic segmentation was 94.6% (69.2-99.9%). The median number of vessel segments in the ranges of 8-10, 6-8, 4-6, and 2-4 mm diameter was 9, 34, 134, and 797, respectively. Number of vessel segments divided by the subject's lung volume was 206 vessels/L with arteries and veins contributing almost equally. In women this vessel density was about 15% higher than in men. Median arterial and venous volumes were 1.52 and 1.54% of the lung volume, respectively. Tortuosity was best described with the sum-of-angles metric and was 142.1 rad/m (138.3-144.5 rad/m). In conclusion, our fully-automatic artery/vein separation algorithm provided reliable measures of pulmonary arteries and veins with respect to age and gender. There was a large variation between subjects in all readouts. No relevant dependence on age, gender, or vessel type was observed. These data may provide reference values for morphometric analysis of lung vessels.

Automated integer programming based separation of arteries and veins from thoracic CT images.

Automated computer-aided analysis of lung vessels has shown to yield promising results for non-invasive diagnosis of lung diseases. To detect vascular changes which affect pulmonary arteries and veins differently, both compartments need to be identified. We present a novel, fully automatic method that separates arteries and veins in thoracic computed tomography images, by combining local as well as global properties of pulmonary vessels. We split the problem into two parts: the extraction of multiple distinct vessel subtrees, and their subsequent labeling into arteries and veins. Subtree extraction is performed with an integer program (IP), based on local vessel geometry. As naively solving this IP is time-consuming, we show how to drastically reduce computational effort by reformulating it as a Markov Random Field. Afterwards, each subtree is labeled as either arterial or venous by a second IP, using two anatomical properties of pulmonary vessels: the uniform distribution of arteries and veins, and the parallel configuration and close proximity of arteries and bronchi. We evaluate algorithm performance by comparing the results with 25 voxel-based manual reference segmentations. On this dataset, we show good performance of the subtree extraction, consisting of very few non-vascular structures (median value: 0.9%) and merged subtrees (median value: 0.6%). The resulting separation of arteries and veins achieves a median voxel-based overlap of 96.3% with the manual reference segmentations, outperforming a state-of-the-art interactive method. In conclusion, our novel approach provides an opportunity to become an integral part of computer aided pulmonary diagnosis, where artery/vein separation is important.