- 作者列表："Han Z","Wei B","Hong Y","Li T","Cong J","Zhu X","Wei H","Zhang W
:Automated Screening of COVID-19 from chest CT is of emergency and importance during the outbreak of SARS-CoV-2 worldwide in 2020. However, accurate screening of COVID-19 is still a massive challenge due to the spatial complexity of 3D volumes, the labeling difficulty of infection areas, and the slight discrepancy between COVID-19 and other viral pneumonia in chest CT. While a few pioneering works have made significant progress, they are either demanding manual annotations of infection areas or lack of interpretability. In this paper, we report our attempt towards achieving highly accurate and interpretable screening of COVID-19 from chest CT with weak labels. We propose an attention-based deep 3D multiple instance learning (AD3D-MIL) where a patient-level label is assigned to a 3D chest CT that is viewed as a bag of instances. AD3D-MIL can semantically generate deep 3D instances following the possible infection area. AD3D-MIL further applies an attention-based pooling approach to 3D instances to provide insight into each instance's contribution to the bag label. AD3D-MIL finally learns Bernoulli distributions of the bag-level labels for more accessible learning. We collected 460 chest CT examples: 230 CT examples from 79 patients with COVID-19, 100 CT examples from 100 patients with common pneumonia, and 130 CT examples from 130 people without pneumonia. A series of empirical studies show that our algorithm achieves an overall accuracy of 97.9%, AUC of 99.0%, and Cohen kappa score of 95.7%. These advantages endow our algorithm as an efficient assisted tool in the screening of COVID-19.
: 在2020年全球新型冠状病毒肺炎爆发期间，胸部CT自动筛查SARS-CoV-2是紧急和重要的。然而，由于3D体积的空间复杂性、感染区域的标记困难以及胸部CT中新型冠状病毒肺炎与其他新型冠状病毒肺炎之间的微小差异，准确筛查病毒性肺炎仍然是一个巨大的挑战。虽然一些开创性的作品已经取得了重大进展，但它们要么要求对感染区域进行人工注释，要么缺乏可解释性。在本文中，我们报告了我们试图从具有弱标记的胸部CT中实现高度准确和可解释的新型冠状病毒肺炎筛查。我们提出了一种基于注意力的深度3D多实例学习 (AD3D-MIL)，其中患者级别标签被分配给被视为实例袋的3D胸部CT。AD3D-MIL可以在可能的感染区域之后在语义上生成深度3D实例。AD3D-MIL进一步将基于注意力的池化方法应用于3D实例，以提供对每个实例对袋标签的贡献的洞察。AD3D-MIL最终学习了包级标签的Bernoulli分布，以便更容易获得学习。我们收集了460例胸部CT病例: 新型冠状病毒肺炎79例患者的230例CT病例，100例普通肺炎患者的100例CT病例，130例无肺炎患者的130例CT病例。一系列的实证研究表明，我们的算法实现了97.9% 的总体准确度，99.0% 的AUC和95.7% 的Cohen kappa评分。这些优点使我们的算法成为筛选新型冠状病毒肺炎的有效辅助工具。
METHODS:OBJECTIVES:The aim was to evaluate the image quality and sensitivity to artifacts of compressed sensing (CS) acceleration technique, applied to 3D or breath-hold sequences in different clinical applications from brain to knee. METHODS:CS with an acceleration from 30 to 60% and conventional MRI sequences were performed in 10 different applications in 107 patients, leading to 120 comparisons. Readers were blinded to the technique for quantitative (contrast-to-noise ratio or functional measurements for cardiac cine) and qualitative (image quality, artifacts, diagnostic findings, and preference) image analyses. RESULTS:No statistically significant difference in image quality or artifacts was found for each sequence except for the cardiac cine CS for one of both readers and for the wrist 3D proton density (PD)-weighted CS sequence which showed less motion artifacts due to the reduced acquisition time. The contrast-to-noise ratio was lower for the elbow CS sequence but not statistically different in all other applications. Diagnostic findings were similar between conventional and CS sequence for all the comparisons except for four cases where motion artifacts corrupted either the conventional or the CS sequence. CONCLUSIONS:The evaluated CS sequences are ready to be used in clinical daily practice except for the elbow application which requires a lower acceleration. The CS factor should be tuned for each organ and sequence to obtain good image quality. It leads to 30% to 60% acceleration in the applications evaluated in this study which has a significant impact on clinical workflow. KEY POINTS:• Clinical implementation of compressed sensing (CS) reduced scan times of at least 30% with only minor penalty in image quality and no change in diagnostic findings. • The CS acceleration factor has to be tuned separately for each organ and sequence to guarantee similar image quality than conventional acquisition. • At least 30% and up to 60% acceleration is feasible in specific sequences in clinical routine.
METHODS:BACKGROUND:The main surgical techniques for spontaneous basal ganglia hemorrhage include stereotactic aspiration, endoscopic aspiration, and craniotomy. However, credible evidence is still needed to validate the effect of these techniques. OBJECTIVE:To explore the long-term outcomes of the three surgical techniques in the treatment of spontaneous basal ganglia hemorrhage. METHODS:Five hundred and sixteen patients with spontaneous basal ganglia hemorrhage who received stereotactic aspiration, endoscopic aspiration, or craniotomy were reviewed retrospectively. Six-month mortality and the modified Rankin Scale score were the primary and secondary outcomes, respectively. A multivariate logistic regression model was used to assess the effects of different surgical techniques on patient outcomes. RESULTS:For the entire cohort, the 6-month mortality in the endoscopic aspiration group was significantly lower than that in the stereotactic aspiration group (odds ratio (OR) 4.280, 95% CI 2.186 to 8.380); the 6-month mortality in the endoscopic aspiration group was lower than that in the craniotomy group, but the difference was not significant (OR=1.930, 95% CI 0.835 to 4.465). A further subgroup analysis was stratified by hematoma volume. The mortality in the endoscopic aspiration group was significantly lower than in the stereotactic aspiration group in the medium (≥40-<80 mL) (OR=2.438, 95% CI 1.101 to 5.402) and large hematoma subgroup (≥80 mL) (OR=66.532, 95% CI 6.345 to 697.675). Compared with the endoscopic aspiration group, a trend towards increased mortality was observed in the large hematoma subgroup of the craniotomy group (OR=8.721, 95% CI 0.933 to 81.551). CONCLUSION:Endoscopic aspiration can decrease the 6-month mortality of spontaneous basal ganglia hemorrhage, especially in patients with a hematoma volume ≥40 mL.
METHODS:OBJECTIVE:The primary purpose of this study was to evaluate the effectiveness of a three-dimensional (3D) software tool (smart planes) for displaying fetal brain planes, and the secondary purpose was to evaluate its accuracy in performing automatic measurements. MATERIAL AND METHODS:This prospective study included singleton fetuses with a gestational age (GA) greater than 18 weeks. Transabdominal two-dimensional ultrasound (2DUS) and 3D smart planes images were respectively used to obtain the basic planes of the fetal brain, with five parameters measured. The images, by either two-dimensional (2D) manual or 3D automatic operation, were reviewed by two experienced sonographers. The agreements between two measurements were analyzed. RESULTS:A total of 226 cases were included. The rates of successful detection by automatic display were as high as 80%. There was substantial agreement between the measurements of the biparietal diameter, head circumference and transcerebellar diameter, but poor agreement between the measurements of cisterna magna and lateral ventricle width. CONCLUSIONS:Smart Planes might be valuable for the rapid evaluation of fetal brain, because it simplifies the evaluation process. However, the technology requires improvement. In addition, this technology cannot replace the conventional manual US scans; it can only be used as an additional approach.