Achieving long-term survival in extensive-stage SCLC: a case report and mini literature review.

Managing extensive-stage SCLC (ES-SCLC) has long been challenging for clinicians and oncologists due to its aggressive nature and poor prognosis. We report a case of a 41-year-old female with ES-SCLC who survived for six years, defying the disease's typically poor prognosis. Through a heavy treatment strategy involving chemotherapy, targeted therapy, and immunotherapy, the patient experienced robust responses and avoided distant metastasis, including brain involvement. The long-term survival case in SCLC highlights the need for further research into personalized strategies and prognostic biomarkers. This case holds significant value for both clinicians and researchers as it challenges the conventional strategies for ES-SCLC and sets the stage for future evidence-based studies aimed at extending survival in SCLC.

Nasal solitary chemosensory cells govern daily rhythm in mouse model of allergic rhinitis.

BACKGROUND: While the daily rhythm of allergic rhinitis (AR) has long been recognized, the molecular mechanism underlying this phenomenon remains enigmatic. OBJECTIVE: We aimed to investigate the role of circadian clock in AR development and to clarify the mechanism by which the daily rhythm of AR is generated. METHODS: AR was induced in mice with ovalbumin. Toluidine blue staining, liquid chromatography-tandem mass spectrometry analysis, real-time quantitative PCR, and immunoblotting were performed with AR and control mice. RESULTS: Ovalbumin-induced AR is diurnally rhythmic and associated with clock gene disruption in nasal mucosa. In particular, Rev-erbα is generally downregulated and its rhythm retained, but with a near-12-hour phase shift. Furthermore, global knockout of core clock gene Bmal1 or Rev-erbα increases the susceptibility of mice to AR and blunts AR rhythmicity. Importantly, nasal solitary chemosensory cells (SCCs) are rhythmically activated, and inhibition of the SCC pathway leads to attenuated AR and a loss of its rhythm. Moreover, rhythmic activation of SCCs is accounted for by diurnal expression of ChAT (an enzyme responsible for the synthesis of acetylcholine) and temporal generation of the neurotransmitter acetylcholine. Mechanistically, Rev-erbα trans-represses Chat through direct binding to a specific response element, generating a diurnal oscillation in this target gene. CONCLUSION: SCCs, under the control of Rev-erbα, are a driver of AR rhythmicity; targeting SCCs should be considered as a new avenue for AR management.

Mitochondria-localizing triphenylphosphine-8-hydroxyquinoline Ru complexes induce ferroptosis and their antitumor evaluation.

Ruthenium complexes are one of the most promising anticancer drugs and ferroptosis is a novel form of regulated cell death, the study on the effect of Ru complexes on ferroptosis is helpful to find more effective antitumor drugs. Here, the synthesis and characterization of two Ru complexes containing 8-hydroxylquinoline and triphenylphosphine as ligands, [Ru(L1) (PPh3)2Cl2] (Ru-1), [Ru(L2) (PPh3)2Cl2] (Ru-2), were reported. Complexes Ru-1 âˆ¼ Ru-2 showed good anticancer activity in Hep-G2 cells. Researches indicated that complexes Ru-1 âˆ¼ Ru-2 could be enriched and appear as red fluorescence in the mitochondria, arouse dysfunction of mitochondria, induce the accumulation of reactive oxygen species (ROS) and lipid peroxidation (LPO), while the morphology of nuclei and cell apoptosis had no significant change. Further experiments proved that GPX4 and Ferritin were down-regulated, which eventually triggered ferroptosis in Hep-G2 cells. Remarkably, Ru-1 showed high inhibitory activity against xenograft tumor growth in vivo (TGIR = 49%). This study shows that the complex Ru-1 could act as a novel drug candidate by triggering cell ferroptosis.

Prognostic value of optical flow ratio for cardiovascular outcomes in patients after percutaneous coronary stent implantation.

Background: The relationship between the optical flow ratio (OFR) and clinical outcomes in patients with coronary artery disease (CAD) after percutaneous coronary stent implantation (PCI) remains unknown. Objective: To examine the correlation between post-PCI OFR and clinical outcomes in patients with CAD following PCI. Methods: Patients who underwent optical coherence tomography (OCT) guided PCI at Guangdong Provincial People's Hospital were retrospectively and continuously enrolled. Clinical data, post-PCI OCT characteristics, and OFR measurements were collected and analyzed to identify predictors of target vessel failure (TVF) after PCI. Results: Among 354 enrolled patients, 26 suffered TVF during a median follow-up of 484 (IQR: 400-774) days. Post-PCI OFR was significantly lower in the TVF group than in the non-TVF group (0.89 vs. 0.93; P = 0.001). In multivariable Cox regression analysis, post-PCI OFR (HR per 0.1 increase: 0.60; 95% CI: 0.41-0.89; P = 0.011), large stent edge dissection (HR: 3.85; 95% CI: 1.51-9.84; P = 0.005) and thin-cap fibroatheroma (TCFA) (HR: 2.95; 95% CI: 1.19-7.35; P = 0.020) in the non-stented segment were independently associated with TVF. In addition, the inclusion of post-PCI OFR to baseline characteristics and post-PCI OCT findings improved the predictive power of the model to distinguish subsequent TVF after PCI (0.838 vs. 0.796; P = 0.028). Conclusion: The post-PCI OFR serves as an independent determinant of risk for TVF in individuals with CAD after PCI. The inclusion of post-PCI OFR assessments, alongside baseline characteristics and post-PCI OCT findings, substantially enhances the capacity to differentiate the subsequent manifestation of TVF in CAD patients following PCI.

A clinically applicable AI system for diagnosis of congenital heart diseases based on computed tomography images.

Congenital heart disease (CHD) is the most common type of birth defect. Without timely detection and treatment, approximately one-third of children with CHD would die in the infant period. However, due to the complicated heart structures, early diagnosis of CHD and its types is quite challenging, even for experienced radiologists. Here, we present an artificial intelligence (AI) system that achieves a comparable performance of human experts in the critical task of classifying 17 categories of CHD types. We collected the first-large CT dataset from three different CT machines, including more than 3750 CHD patients over 14 years. Experimental results demonstrate that it can achieve diagnosis accuracy (86.03%) comparable with junior cardiovascular radiologists (86.27%) in a World Health Organization appointed research and cooperation center in China on most types of CHD, and obtains a higher sensitivity (82.91%) than junior cardiovascular radiologists (76.18%). The accuracy of the combination of our AI system (97.20%) and senior radiologists achieves comparable results to that of junior radiologists and senior radiologists (97.16%) which is the current clinical routine. Our AI system can further provide 3D visualization of hearts to senior radiologists for interpretation and flexible review, surgeons for precise intuition of heart structures, and clinicians for more precise outcome prediction. We demonstrate the potential of our model to be integrated into current clinic practice to improve the diagnosis of CHD globally, especially in regions where experienced radiologists can be scarce.

ImageCAS: A large-scale dataset and benchmark for coronary artery segmentation based on computed tomography angiography images.

Cardiovascular disease (CVD) accounts for about half of non-communicable diseases. Vessel stenosis in the coronary artery is considered to be the major risk of CVD. Computed tomography angiography (CTA) is one of the widely used noninvasive imaging modalities in coronary artery diagnosis due to its superior image resolution. Clinically, segmentation of coronary arteries is essential for the diagnosis and quantification of coronary artery disease. Recently, a variety of works have been proposed to address this problem. However, on one hand, most works rely on in-house datasets, and only a few works published their datasets to the public which only contain tens of images. On the other hand, their source code have not been published, and most follow-up works have not made comparison with existing works, which makes it difficult to judge the effectiveness of the methods and hinders the further exploration of this challenging yet critical problem in the community. In this paper, we propose a large-scale dataset for coronary artery segmentation on CTA images. In addition, we have implemented a benchmark in which we have tried our best to implement several typical existing methods. Furthermore, we propose a strong baseline method which combines multi-scale patch fusion and two-stage processing to extract the details of vessels. Comprehensive experiments show that the proposed method achieves better performance than existing works on the proposed large-scale dataset. The benchmark and the dataset are published at https://github.com/XiaoweiXu/ImageCAS-A-Large-Scale-Dataset-and-Benchmark-for-Coronary-Artery-Segmentation-based-on-CT.

The effects of cardiac structure, valvular regurgitation, and left ventricular diastolic dysfunction on the diagnostic accuracy of Murray law-based quantitative flow ratio.

Objective: The study aimed to investigate the diagnostic accuracy of Murray law-based quantitative flow ratio (µQFR) from a single angiographic view in patients with abnormal cardiac structure, left ventricular diastolic dysfunction, and valvular regurgitation. Background: µQFR is a novel fluid dynamics method for deriving fractional flow reserve (FFR). In addition, current studies of µQFR mainly analyzed patients with normal cardiac structure and function. The accuracy of µQFR when patients had abnormal cardiac structure, left ventricular diastolic dysfunction, and valvular regurgitation has not been clear. Methods: This study retrospectively analyzed 261 patients with 286 vessels that underwent both FFR and µQFR prior to intervention. The cardiac structure and function were measured using echocardiography. Pressure wire-derived FFR ≤0.80 was defined as hemodynamically significant coronary stenosis. Results: µQFR had a moderate correlation with FFR (r = 0.73, p < 0.001), and the Bland-Altman plot presented no difference between the µQFR and FFR (0.006 ± 0.075, p = 0.192). With FFR as the standard, the diagnostic accuracy, sensitivity, specificity, positive predictive value, and negative predictive value of µQFR were 94.06% (90.65-96.50), 82.56% (72.87-89.90), 99.00% (96.44-99.88), 97.26 (89.91-99.30), and 92.96% (89.29-95.44), respectively. The concordance of µQFR/FFR was not associated with abnormal cardiac structure, valvular regurgitation (aortic valve, mitral valve, and tricuspid valve), and left ventricular diastolic function. Coronary hemodynamics showed no difference between normality and abnormality of cardiac structure and left ventricular diastolic function. Coronary hemodynamics demonstrated no difference among valvular regurgitation (none, mild, moderate, or severe). Conclusion: µQFR showed an excellent agreement with FFR. The effect of abnormal cardiac structure, valvular regurgitation, and left ventricular diastolic function did not correlate with the diagnostic accuracy of µQFR. Coronary hemodynamics showed no difference in patients with abnormal cardiac structure, valvular regurgitation, and left ventricular diastolic function.

Graph matching and deep neural networks based whole heart and great vessel segmentation in congenital heart disease.

Congenital heart disease (CHD) is one of the leading causes of mortality among birth defects, and due to significant variations in the whole heart and great vessel, automatic CHD segmentation using CT images has been always under-researched. Even though some segmentation algorithms have been developed in the literature, none perform very well under the complex structure of CHD. To deal with the challenges, we take advantage of deep learning in processing regular structures and graph algorithms in dealing with large variations and propose a framework combining both the whole heart and great vessel segmentation in complex CHD. We benefit from deep learning in segmenting the four chambers and myocardium based on the blood pool, and then we extract the connection information and apply graph matching to determine the categories of all the vessels. Experimental results on 68 3D CT images covering 14 types of CHD illustrate our framework can increase the Dice score by 12% on average compared with the state-of-the-art whole heart and great vessel segmentation method in normal anatomy. We further introduce two cardiovascular imaging specialists to evaluate our results in the standard of the Van Praagh classification system, and achieves well performance in clinical evaluation. All these results may pave the way for the clinical use of our method in the incoming future.

Predictive performance of the perivascular fat attenuation index for interventional antegrade percutaneous coronary intervention for chronic total occlusion.

OBJECTIVES: This study aimed to investigate the association between the perivascular fat attenuation index (FAI) and the success of the antegrade percutaneous coronary intervention (PCI) for chronic total occlusion (CTO). METHODS: This study evaluated patients with only one CTO lesion observed on conventional coronary angiography (CAG) who underwent coronary computed tomography angiography (CCTA) < 1 month before CAG, from 2018 to 2019. The clinical data, CCTA-based CTO lesion morphologic characteristics, and perivascular FAI of CTO lesions were recorded and analysed. RESULTS: In total, 156 patients with CTOs were enrolled in this study. Successful antegrade PCI (A-PCI) was achieved in 105 CTO lesions (67.3%). The perivascular FAI of the failed A-PCI group was significantly lower than the successful A-PCI group (-84.76 ± 10.44 Hounsfield unit (HU) vs. -67.54 ± 9.94 HU; p < 0.001), and the cut-off value determined by the receiver operating characteristic (ROC) curve was -77.50 HU. Multivariable analysis revealed no statistical significance in the clinical data, FAI ≤ -77.50 HU (odds ratio (OR): 33.96), negative remodeling (OR: 4.36), severe calcification degree (OR: 4.43) and occlusion length ≥ 20.25 mm (OR: 3.89) were independent predictors of A-PCI failure. The prediction performance of combining the three morphologic characteristics (severe calcification, occlusion length ≥ 20.25 mm, and negative remodeling) with FAI ≤ -77.50 HU was better than that of the three morphologic characteristics alone (0.93 versus 0.77, p < 0.001). CONCLUSIONS: As a non-invasive index for detecting coronary inflammation, FAI complements indicators based on coronary CTA well and may be helpful for choosing appropriate interventional strategies. KEY POINTS: • Perivascular FAI of CTO was significantly higher in the failed A-PCI group. • The combination of FAI with other morphological predictors showed higher predictive performance of failed A-PCI for CTOs. • FAI is a good complement to indicators based on coronary CTA.

Anatomical attention-based prediction of postoperative pulmonary venous obstruction via CTA images.

Total anomalous pulmonary venous connection (TAPVC) is a rare congenital heart disease, with which some patients suffer from postoperative pulmonary venous obstruction (PPVO), requiring particular follow-up strategies and treatments. PPVO prediction has important clinical significance, while building a PPVO prediction model is challenging due to limited data and class imbalance distribution. Inspired by the anatomical evidence of PPVO, which is related to the structure of the left atrium (LA) and pulmonary vein (PV), we design an effective multi-task network for PPVO classification. The proposed method incorporates clinical priors and merits of the segmentation-based network into the classification task. The features learned from segmenting LA and PV are concatenated into the PPVO classification branch to constrain the learning of discriminative features. Anatomical-guided attention is applied in the aggregation of these features to restrict them focusing on TAPVC-related regions. To deal with the imbalance classification problem of PPVO, a novel classification loss derived by masked class activation map (MCAM) is designed to improve the classification performance. Computed tomography angiography (CTA) images of 146 patients diagnosed with supracardiac TAPVC in Shanghai Children's Medical Center and Guangdong Provincial People's Hospital were enrolled in this work. The comprehensive experiments demonstrate the effectiveness and generalization of our proposed method. The automatic PPVO prediction model shows the potential application in helping clinicians develop follow-up strategies, thereby improving the survival rate of TAPVC patients.

A Novel 3D Visualized Operative Procedure in the Single-Stage Complete Repair With Unifocalization of Pulmonary Atresia With Ventricular Septal Defect and Major Aortopulmonary Collateral Arteries.

Objectives: Pulmonary atresia with ventricular septal defect and major aortopulmonary collateral arteries (PA/VSD/MAPCAs) is a relatively rare, complex, and heterogeneous congenital heart disease. As one of the effective treatments, the midline unifocalization strategy still remains complicated and challenging due to the diverse forms of MAPCAs and pulmonary arteries. The purpose of this study is to summarize our experience of a novel three-dimensional (3D) visualized operative procedure in the single-stage complete repair with unifocalization and to clarify the benefits it may bring to us. Methods: We described our experience of the 3D visualized operative procedure such as 3D printing, virtual reality (VR), and mixed reality (MR) technology in patients with PA/VSD/MAPCAs who underwent a single-stage complete repair with unifocalization. The data from the patients who underwent this procedure (3D group) and those who underwent the conventional procedure (conventional group) were compared. Results: The conventional and 3D groups included 11 patients from September 2011 to December 2017 and 9 from January 2018 to March 2021, respectively. The baseline characteristics such as age, body weight, preoperative saturation, the anatomy of the pulmonary arteries and MAPCAs, the Nakata index, and TNPAI had no statistical significance. All 9 patients in the 3D group were operated only through a median sternotomy, while 8 cases (72.7%) in the conventional group needed another posterolateral thoracotomy (p = 0.001). In the 3D group, the CPB time was shorter (93.2 ± 63.8 vs. 145.1 ± 68.4 min, p = 0.099), and the median pre-CPB time per MAPCAs was significantly shorter [25.7 (14.0, 46.3) vs. 65 (41.3, 75.0) min, p = 0.031]. There was no early death in the 3D group, while there were 3 in the conventional group (0 vs. 27.3%, p = 0.218). Conclusion: The novel 3D visualized operative procedure may help improve the performance of the single-stage complete repair with the midline unifocalization of PA/VSD/MAPCAs and help shorten the dissecting time of the MAPCAs. It may promote the routine and successful application of this strategy in more centers.

Myocardial Segmentation of Cardiac MRI Sequences With Temporal Consistency for Coronary Artery Disease Diagnosis.

Coronary artery disease (CAD) is the most common cause of death globally, and its diagnosis is usually based on manual myocardial (MYO) segmentation of MRI sequences. As manual segmentation is tedious, time-consuming, and with low replicability, automatic MYO segmentation using machine learning techniques has been widely explored recently. However, almost all the existing methods treat the input MRI sequences independently, which fails to capture the temporal information between sequences, e.g., the shape and location information of the myocardium in sequences along time. In this article, we propose a MYO segmentation framework for sequence of cardiac MRI (CMR) scanning images of the left ventricular (LV) cavity, right ventricular (RV) cavity, and myocardium. Specifically, we propose to combine conventional neural networks and recurrent neural networks to incorporate temporal information between sequences to ensure temporal consistency. We evaluated our framework on the automated cardiac diagnosis challenge (ACDC) dataset. The experiment results demonstrate that our framework can improve the segmentation accuracy by up to 2% in the Dice coefficient.

Building a Risk Prediction Model for Postoperative Pulmonary Vein Obstruction via Quantitative Analysis of CTA Images.

Total anomalous pulmonary venous connection (TAPVC) is a rare but mortal congenital heart disease in children and can be repaired by surgical operations. However, some patients may suffer from pulmonary venous obstruction (PVO) after surgery with insufficient blood supply, necessitating special follow-up strategy and treatment. Therefore, it is a clinically important yet challenging problem to predict such patients before surgery. In this paper, we address this issue and propose a computational framework to determine the risk factors for postoperative PVO (PPVO) from computed tomography angiography (CTA) images and build the PPVO risk prediction model. From clinical experiences, such risk factors are likely from the left atrium (LA) and pulmonary vein (PV) of the patient. Thus, 3D models of LA and PV are first reconstructed from low-dose CTA images. Then, a feature pool is built by computing different morphological features from 3D models of LA and PV, and the coupling spatial features of LA and PV. Finally, four risk factors are identified from the feature pool using the machine learning techniques, followed by a risk prediction model. As a result, not only PPVO patients can be effectively predicted but also qualitative risk factors reported in the literature can now be quantified. Finally, the risk prediction model is evaluated on two independent clinical datasets from two hospitals. The model can achieve the AUC values of 0.88 and 0.87 respectively, demonstrating its effectiveness in risk prediction.

Predicting reintervention after thoracic endovascular aortic repair of Stanford type B aortic dissection using machine learning.

OBJECTIVES: To construct models for predicting reintervention after thoracic endovascular aortic repair (TEVAR) of Stanford type B aortic dissection (TBAD). METHODS: A total of 192 TBAD patients who underwent TEVAR were included; 68 (35.4%) had indications for reintervention. Clinical characteristics, aorta characteristics on pre- and postoperative computed tomography angiography, and aorta characteristics on immediate postoperative aortic digital subtraction angiography were collected. The least absolute shrinkage and selection operator (LASSO) regression was applied to identify the risk factors for reintervention. Eight classifiers were used for modeling. The models were trained on 100 train-validation random splits with a ratio of 2:1. The performance was evaluated by the receiver operating characteristic curve. RESULTS: Seven predictors of reintervention were identified, including maximum false lumen diameter, aortic diameter measured at the level of approximately 15 mm distal to the left subclavian artery, aortic diameter measured at the level of the diaphragm, false lumen diameter measured at the level of the celiac artery, number of bare-metal and covered stents, number of bare-metal stents, and residual perfusion of the false lumen. Logistic regression (LR) yielded the highest performance, with an area under the curve of 0.802. A nomogram built for clinical use showed good calibration. The cutoff value for dividing patients into low- and high-risk subgroups was 0.413. Kaplan-Meier curves showed that the overall survival of high-risk patients was significantly shorter than that of low-risk patients (both p < 0.05). CONCLUSION: Our nomogram could predict the reintervention after TEVAR in patients with TBAD, which may facilitate patient selection and surveillance strategies. KEY POINTS: • Seven risk factors of reintervention after TEVAR of TBAD were identified for modeling. • Logistic regression performed best in predicting reintervention with an AUC of 0.802. • Patients with a high risk of reintervention had shorter OS than those with a low risk.

VR and 3D Printing for Preop Planning of Left Ventricular Myxoma in a Child.

We report a highly rare case of a large left ventricular myxoma with left ventricular outflow tract obstruction in a previously healthy, asymptomatic 7-year-old male patient. For preoperative planning, we used end-diastolic computed tomography data to model the patient's cardiovascular structures and then generated a virtual reality model and a 3-dimensional-printed model. They helped the surgeon completely manage the details of the surgery. The mass was completely resected in one piece uneventfully. Histopathologic examination confirmed the diagnosis of myxoma. We believe that 3-dimensional technologies may be effective if the traditional modalities were insufficient in those rare, complex, and heterogeneous cases.

Puerariae lobatae radix protects against UVB-induced skin aging via antagonism of REV-ERBα in mice.

Puerariae lobatae radix (PLR) is a wildly used herbal medicine. Here we aimed to assess the PLR efficacy against UVB (ultraviolet-B)-induced skin aging and to determine the mechanisms thereof. We found a significant protective effect of PLR (topical application) on UVB-induced skin aging in mice, as evidenced by reduced skin wrinkles, epidermal thickness, and MDA (malondialdehyde) content as well as increased levels of HYP (hydroxyproline) and SOD (superoxide dismutase) in the skin. In the meantime, Mmp-1, p21 and p53 levels were decreased in the skin of PLR-treated mice. Anti-aging effects of PLR were also confirmed in L929 cells. Furthermore, PLR up-regulated skin expression of BMAL1, which is a known regulator of aging by promoting Nrf2 and antioxidant enzymes. Consistently, Nrf2 and several genes (i.e., Prdx6, Sod1, and Sod2) encoding antioxidant enzymes in the skin were increased in PLR-treated mice. Moreover, based on Gal4 chimeric assay, Bmal1 reporter gene and expression assays, we identified PLR as an antagonist of REV-ERBα that can increase Bmal1 expression. Intriguingly, loss of Rev-erbα protected mice against UVB-induced skin aging and abrogated the protective effect of PLR. In conclusion, PLR acts as an antagonist of REV-ERBα and promotes the expression of BMAL1 to protect against skin aging in mice.

Machine Learning to Predict Long-Term Cardiac-Relative Prognosis in Patients With Extra-Cardiac Vascular Disease.

Aim: Patients with ischemic stroke (IS), transient ischemic attack (TIA), and/or peripheral artery disease (PAD) represent a population with an increased risk of coronary artery disease. Prognostic risk assessment to identify those with the highest risk that may benefit from more intensified treatment remains challenging. To explore the feasibility and capability of machine learning (ML) to predict long-term adverse cardiac-related prognosis in patients with IS, TIA, and/or PAD. Methods: We analyzed 636 consecutive patients with a history of IS, TIA, and/or PAD. All patients underwent a coronary CT angiography (CCTA) scan. Thirty-five clinical data and 34 CCTA metrics underwent automated feature selection for ML model boosting. The clinical outcome included all-cause mortality (ACM) and major adverse cardiac events (MACE) (ACM, unstable angina requiring hospitalization, non-fatal myocardial infarction (MI), and revascularization 90 days after the index CCTA). Results: During the follow-up of 3.9 ± 1.6 years, 21 patients had unstable angina requiring hospitalization, eight had a MI, 23 had revascularization and 13 deaths. ML demonstrated a significant higher area-under-curve compared with the modified Duke index (MDI), segment stenosis score (SSS), segment involvement score (SIS), and Framingham risk score (FRS) for the prediction of ACM (ML:0.92 vs. MDI:0.66, SSS:0.68, SIS:0.67, FRS:0.51, all P < 0.001) and MACE (ML:0.84 vs. MDI:0.82, SSS:0.76, SIS:0.73, FRS:0.53, all P < 0.05). Conclusion: Among the patients with IS, TIA, and/or PAD, ML demonstrated a better capability of predicting ACM and MCAE than clinical scores and CCTA metrics.

Pyramid-Net: Intra-layer Pyramid-Scale Feature Aggregation Network for Retinal Vessel Segmentation.

Retinal vessel segmentation plays an important role in the diagnosis of eye-related diseases and biomarkers discovery. Existing works perform multi-scale feature aggregation in an inter-layer manner, namely inter-layer feature aggregation. However, such an approach only fuses features at either a lower scale or a higher scale, which may result in a limited segmentation performance, especially on thin vessels. This discovery motivates us to fuse multi-scale features in each layer, intra-layer feature aggregation, to mitigate the problem. Therefore, in this paper, we propose Pyramid-Net for accurate retinal vessel segmentation, which features intra-layer pyramid-scale aggregation blocks (IPABs). At each layer, IPABs generate two associated branches at a higher scale and a lower scale, respectively, and the two with the main branch at the current scale operate in a pyramid-scale manner. Three further enhancements including pyramid inputs enhancement, deep pyramid supervision, and pyramid skip connections are proposed to boost the performance. We have evaluated Pyramid-Net on three public retinal fundus photography datasets (DRIVE, STARE, and CHASE-DB1). The experimental results show that Pyramid-Net can effectively improve the segmentation performance especially on thin vessels, and outperforms the current state-of-the-art methods on all the adopted three datasets. In addition, our method is more efficient than existing methods with a large reduction in computational cost. We have released the source code at https://github.com/JerRuy/Pyramid-Net.

ImageTBAD: A 3D Computed Tomography Angiography Image Dataset for Automatic Segmentation of Type-B Aortic Dissection.

Type-B Aortic Dissection (TBAD) is one of the most serious cardiovascular events characterized by a growing yearly incidence, and the severity of disease prognosis. Currently, computed tomography angiography (CTA) has been widely adopted for the diagnosis and prognosis of TBAD. Accurate segmentation of true lumen (TL), false lumen (FL), and false lumen thrombus (FLT) in CTA are crucial for the precise quantification of anatomical features. However, existing works only focus on only TL and FL without considering FLT. In this paper, we propose ImageTBAD, the first 3D computed tomography angiography (CTA) image dataset of TBAD with annotation of TL, FL, and FLT. The proposed dataset contains 100 TBAD CTA images, which is of decent size compared with existing medical imaging datasets. As FLT can appear almost anywhere along the aorta with irregular shapes, segmentation of FLT presents a wide class of segmentation problems where targets exist in a variety of positions with irregular shapes. We further propose a baseline method for automatic segmentation of TBAD. Results show that the baseline method can achieve comparable results with existing works on aorta and TL segmentation. However, the segmentation accuracy of FLT is only 52%, which leaves large room for improvement and also shows the challenge of our dataset. To facilitate further research on this challenging problem, our dataset and codes are released to the public (Dataset, 2020).

The Hemodynamics of Patent Ductus Arteriosus in Patients after Central Shunt Operation.

A central shunt (CS) was an important surgery of systemic-to-pulmonary shunt (SPS) for the treatment of complex congenital heart diseases with decreased pulmonary blood flow (CCHDs-DPBF). There was no clear conclusion on how to deal with unclosed patent ductus arteriosus (PDA) during CS surgery. This study expanded the knowledge base on PDA by exploring the influence of the closing process of the PDA on the hemodynamic parameters for the CS model. The initial three-dimensional (3D) geometry was reconstructed based on the patient's computed tomography (CT) data. Then, a CS configuration with three typical pulmonary artery (PA) dysplasia structures and different sizes of PDA was established. The three-element windkessel (3WK) multiscale coupling model was used to define boundary conditions for transient simulation through computational fluid dynamics (CFD). The results showed that the larger size of PDA led to a greater systemic-to-pulmonary shunt ratio (Q S/A), and the flow ratio of the left pulmonary artery (LPA) to right pulmonary artery (RPA) (Q L/R) was more close to 1, while both the proportion of high wall shear stress (WSS) areas and power loss decreased. The case of PDA nonclosure demonstrates that the aortic oxygen saturation (Sao2) increased, while the systemic oxygen delivery (Do2) decreased. In general, for the CS model with three typical PA dysplasia, the changing trends of hemodynamic parameters during the spontaneous closing process of PDA were roughly identical, and nonclosure of PDA had a series of hemodynamic advantages, but a larger PDA may cause excessive PA perfusion and was not conducive to reducing cyanosis symptoms.