目的 探讨右美托咪定对小儿七氟烷吸入麻醉苏醒期躁动的影响。 方法 选择2011年3月－2012年1月美国麻醉医师协会分级Ⅰ～Ⅱ级、年龄2～8岁、择期行疝囊高位结扎术和隐睾下降固定术患儿40例，随机分为2组，右美托咪定组（A组）和对照组（B组），两组患儿在年龄、体重、手术种类无明显差异。两组患儿均采用面罩8%七氟烷吸入麻醉诱导，开放静脉，给予盐酸戊乙奎醚0.1 mg/kg、顺式阿曲库铵0.15 mg/kg，插入喉罩，麻醉维持根据血压、心率及脑电双频指数调节吸入麻醉药浓度。A组静脉给予1 μg/kg右美托咪定，B组给予同等容量的生理盐水。入室至手术结束时连续观察收缩压、舒张压、心率、血氧饱和度，记录清醒时间、拔除喉罩时间，记录苏醒期并发症的发生数。记录入麻醉恢复室即刻（0 min）、15、30、60、90 min患儿疼痛和躁动评分。 结果 两组患儿在手术时间、清醒时间以及拔除喉罩时间差异无统计学意义（P＞0.05），A组术后入恢复室0、15、30 min疼痛评分和躁动评分均低于B组（P＜0.05），两组患儿围术期均未出现低血压和心动过缓。 结论 右美托咪定用于小儿七氟烷吸入麻醉能够增强术后镇痛，减少苏醒期躁动。
Objective To explore the clinical effect and complications of lumbar plexus block through different approaches on perioperative analgesia in aged proximal femur surgery, and find the best method for analgesia in these patients. Methods From January to December 2015, 150 elderly patients scheduled for proximal femur surgery were randomly divided into three groups: psoas compartment block (PCB group, n=50), Winnie " 3 in 1” block (Winnie group, n=50), and fascia iliaca compartment block (FICB group, n=50). Twelve hours before surgery, guided by ultrasound and nerve stimulator, lumbar plexus blocks were performed in all the patients, then patient-controlled analgesia (the formula and the usage were the same) was done. All patients received epidural anesthesia, and were maintained postoperative analgesia for 72 hours. If Rest Visual Analogue Scale>3 or Initiative Movement Visual Analogue Scale>4, sufentanyl 10 μgi.m. was given. Muscle strength grades and complications were recorded. Anesthetic effect of sensory block of femoral, lateral femoral cutaneous, and obturator nerves were measured and recorded too. Results There were two cases of epidural block, and one case of puncture point bleeding in group PCB; no complication in the other groups was found. There was no remedy for inadequate analgesia in the three groups. Compared with group PCB, the muscle strength grades during postoperative 24–72 hours in group FICB were higher (P<0.05). The successful rate of the block of lateral femoral cutaneous nerves was 64%, 91% and 96% in group Winnie, group PCB and group FICB, respectively, and the differences between the three groups were all statistically significant (P<0.05). The successful rate of the block of obturator nerves in group FICB (62%) was lower than that in group PCB (89%) and Winnie group (84%) (P<0.05). Conclusion Continuous fascia iliaca compartment block on perioperative analgesia in aged proximal femur surgery, with exact effect, less complications and simple operation, is better than the psoas compartment block and Winne " 3 in 1” nerve block.
Optical coherence tomography (OCT) has become a key technique in the diagnosis of coronary artery stenosis, which can identify plaques and vulnerable plaques in the image. Therefore, this technique is of great significance for the diagnosis of coronary heart disease. However, there is still a lack of automatic, multi-region, high-precision segmentation algorithms for coronary OCT images in the current research field. Therefore, this paper proposes a multi-zone, fully automated segmentation algorithm for coronary OCT images based on neutrosophic theory, which achieves high-precision segmentation of fibrous plaques and lipid regions. In this paper, the method of transforming OCT images into T in the area of neutrosophics is redefined based on the membership function, and the segmentation accuracy of fiber plaques is improved. For the segmentation of lipid regions, the algorithm adds homomorphic filter enhancement images, and uses OCT to transform OCT images into I in the field of neutrosophics, and further uses morphological methods to achieve high-precision segmentation. In this paper, 40 OCT images from 9 patients with typical plaques were analyzed and compared with the results of manual segmentation by doctors. Experiments show that the proposed algorithm avoids the over-segmentation and under-segmentation problems of the traditional neutrosophic theory method, and accurately segment the patch area. Therefore, the work of this paper can effectively improve the accuracy of segmentation of plaque for doctors, and assist clinicians in the diagnosis and treatment of coronary heart disease.
Accurate segmentation of pulmonary nodules is an important basis for doctors to determine lung cancer. Aiming at the problem of incorrect segmentation of pulmonary nodules, especially the problem that it is difficult to separate adhesive pulmonary nodules connected with chest wall or blood vessels, an improved random walk method is proposed to segment difficult pulmonary nodules accurately in this paper. The innovation of this paper is to introduce geodesic distance to redefine the weights in random walk combining the coordinates of the nodes and seed points in the image with the space distance. The improved algorithm is used to achieve the accurate segmentation of pulmonary nodules. The computed tomography (CT) images of 17 patients with different types of pulmonary nodules were selected for segmentation experiments. The experimental results are compared with the traditional random walk method and those of several literatures. Experiments show that the proposed method has good accuracy in the segmentation of pulmonary nodule, and the accuracy can reach more than 88% with segmentation time is less than 4 seconds. The results could be used to assist doctors in the diagnosis of benign and malignant pulmonary nodules and improve clinical efficiency.
The segmentation of the intracoronary optical coherence tomography (OCT) images is the basis of the plaque recognition, and it is important to the following plaque feature analysis, vulnerable plaque recognition and further coronary disease aided diagnosis. This paper proposes an algorithm about multi region plaque segmentation based on kernel graph cuts model that realizes accurate segmentation of fibrous, calcium and lipid pool plaques in coronary OCT image, while boundary information has been well reserved. We segmented 20 coronary images with typical plaques in our experiment, and compared the plaque regions segmented by this algorithm to the plaque regions obtained by doctor's manual segmentation. The results showed that our algorithm is accurate to segment the plaque regions. This work has demonstrated that it can be used for reducing doctors' working time on segmenting plaque significantly, reduce subjectivity and differences between different doctors, assist clinician's diagnosis and treatment of coronary artery disease.
In recent years, optical coherence tomography (OCT) has developed into a popular coronary imaging technology at home and abroad. The segmentation of plaque regions in coronary OCT images has great significance for vulnerable plaque recognition and research. In this paper, a new algorithm based on K-means clustering and improved random walk is proposed and Semi-automated segmentation of calcified plaque, fibrotic plaque and lipid pool was achieved. And the weight function of random walk is improved. The distance between the edges of pixels in the image and the seed points is added to the definition of the weight function. It increases the weak edge weights and prevent over-segmentation. Based on the above methods, the OCT images of 9 coronary atherosclerotic patients were selected for plaque segmentation. By contrasting the doctor’s manual segmentation results with this method, it was proved that this method had good robustness and accuracy. It is hoped that this method can be helpful for the clinical diagnosis of coronary heart disease.
[Abstract]Automatic and accurate segmentation of lung parenchyma is essential for assisted diagnosis of lung cancer. In recent years, researchers in the field of deep learning have proposed a number of improved lung parenchyma segmentation methods based on U-Net. However, the existing segmentation methods ignore the complementary fusion of semantic information in the feature map between different layers and fail to distinguish the importance of different spaces and channels in the feature map. To solve this problem, this paper proposes the double scale parallel attention (DSPA) network (DSPA-Net) architecture, and introduces the DSPA module and the atrous spatial pyramid pooling (ASPP) module in the “encoder-decoder” structure. Among them, the DSPA module aggregates the semantic information of feature maps of different levels while obtaining accurate space and channel information of feature map with the help of cooperative attention (CA). The ASPP module uses multiple parallel convolution kernels with different void rates to obtain feature maps containing multi-scale information under different receptive fields. The two modules address multi-scale information processing in feature maps of different levels and in feature maps of the same level, respectively. We conducted experimental verification on the Kaggle competition dataset. The experimental results prove that the network architecture has obvious advantages compared with the current mainstream segmentation network. The values of dice similarity coefficient (DSC) and intersection on union (IoU) reached 0.972 ± 0.002 and 0.945 ± 0.004, respectively. This paper achieves automatic and accurate segmentation of lung parenchyma and provides a reference for the application of attentional mechanisms and multi-scale information in the field of lung parenchyma segmentation.