Automated lung segmentation algorithm for CAD system of thoracic CT

Objective: To design and test the accuracy and efficiency of our lung segmentation algorithm on thoracic CT image in computer-aided diagnostic (CAD) system, especially on the segmentation between left and right lungs. Methods: We put forward the base frame of our lung segmentation firstly. Then, using optimal thresholding and mathematical morphologic methods, we acquired the rough image of lung segmentation. Finally, we presented a fast self-fit segmentation refinement algorithm, adapting to the unsuccessful left-right lung segmentation of thredsholding. Then our algorithm was used to CT scan images of 30 patients and the results were compared with those made by experts. Results: Experiments on clinical 2-D pulmonary images showed the results of our algorithm were very close to the expert’s manual outlines, and it was very effective for the separation of left and right lungs with a successful segmentation ratio 94.8%. Conclusion: It is a practicable fast lung segmentation algorithm for CAD system on thoracic CT image.

An Improved Lung Cancer Segmentation Based on Nature-Inspired Optimization Approaches

The distinction and precise identification of tumor nodules are crucial for timely lung cancer diagnosis andplanning intervention. This research work addresses the major issues pertaining to the field of medical imageprocessing while focusing on lung cancer Computed Tomography (CT) images. In this context, the paper proposesan improved lung cancer segmentation technique based on the strengths of nature-inspired approaches. Thebetter resolution of CT is exploited to distinguish healthy subjects from those who have lung cancer. In thisprocess, the visual challenges of the K-means are addressed with the integration of four nature-inspired swarmintelligent techniques. The techniques experimented in this paper are K-means with Artificial Bee Colony (ABC),K-means with Cuckoo Search Algorithm (CSA), K-means with Particle Swarm Optimization (PSO), and Kmeanswith Firefly Algorithm (FFA). The testing and evaluation are performed on Early Lung Cancer ActionProgram (ELCAP) database. The simulation analysis is performed using lung cancer images set against metrics:precision, sensitivity, specificity, f-measure, accuracy,Matthews Correlation Coefficient (MCC), Jaccard, and Dice.The detailed evaluation shows that the K-means with Cuckoo Search Algorithm (CSA) significantly improved thequality of lung cancer segmentation in comparison to the other optimization approaches utilized for lung cancerimages. The results exhibit that the proposed approach (K-means with CSA) achieves precision, sensitivity, and Fmeasureof 0.942, 0.964, and 0.953, respectively, and an average accuracy of 93%. The experimental results prove thatK-meanswithABC,K-meanswith PSO,K-meanswith FFA, andK-meanswithCSAhave achieved an improvementof 10.8%, 13.38%, 13.93%, and 15.7%, respectively, for accuracy measure in comparison to K-means segmentationfor lung cancer images. Further, it is highlighted that the proposed K-means with CSA have achieved a significantimprovement in accuracy, hence can be utilized by researchers for improved segmentation processes of medicalimage datasets for identifying the targeted region of interest.

Operation room nursing based on humanized nursing mode combined with nitric oxide on rehabilitation effect after lung surgery

BACKGROUND With advancements in the diagnosis and treatment of lung diseases,lung segment surgery has become increasingly common.Postoperative rehabilitation is critical for patient recovery,yet challenges such as complications and adverse outcomes persist.Incorporating humanized nursing modes and novel treatments like nitric oxide inhalation may enhance recovery and reduce postoperative complications.AIM To evaluate the effects of a humanized nursing mode combined with nitric oxide inhalation on the rehabilitation outcomes of patients undergoing lung surgery,focusing on pulmonary function,recovery speed,and overall treatment costs.METHODS A total of 79 patients who underwent lung surgery at a tertiary hospital from March 2021 to December 2021 were divided into a control group(n=39)receiving a routine nursing program and an experimental group(n=40)receiving additional humanized nursing interventions and atomized inhalation of nitric oxide.Key indicators were compared between the two groups alongside an analysis of treatment costs.RESULTS The experimental group demonstrated significant improvements in pulmonary function,reduced average recovery time,and lower total treatment costs compared to the control group.Moreover,the quality of life in the experimental group was significantly better in the 3 months post-surgery,indicating a more effective rehabilitation process.CONCLUSION The combination of humanized nursing mode and nitric oxide inhalation in postoperative care for lung surgery patients significantly enhances pulmonary rehabilitation outcomes,accelerates recovery,and reduces economic burden.This approach offers a promising reference for improving patient care and rehabilitation efficiency following lung surgery.

3D segmentation and visualization of lung and its structures using CT images of the thorax

Computing systems have been playing an important role in various medical fields, notably in image diagnosis. Studies in the field of Computational Vision aim at developing techniques and systems capable of detecting various illnesses automatically. What has been highlighted among the existing exams that allow diagnosis aid and the application of computing systems in parallel is Computed Tomography (CT). CT enables the visualization of internal organs, such as the lung and its structures. Computational Vision systems extract information from the CT images by segmenting the regions of interest, and then recognize and identify details in those images. This work focuses on the segmentation phase of CT lung images with singularity-based techniques. Among these methods are the region growing (RG) technique and its 3D RG variations and the thresholding technique with multi-thresholding. The 3D RG method is applied to lung segmentation and from the 3D RG segments of the lung hilum, the multi-thresholding can segment the blood vessels, lung emphysema and the bones. The results of lung segmentation in this work were evaluated by two pulmonologists. The results obtained showed that these methods can integrate aid systems for medical diagnosis in the pulmonology field.

An algorithm for segmentation of lung ROI by mean-shift clustering combined with multi-scale HESSIAN matrix dot filtering

A new algorithm for segmentation of suspected lung ROI(regions of interest)by mean-shift clustering and multi-scale HESSIAN matrix dot filtering was proposed.Original image was firstly filtered by multi-scale HESSIAN matrix dot filters,round suspected nodular lesions in the image were enhanced,and linear shape regions of the trachea and vascular were suppressed.Then,three types of information,such as,shape filtering value of HESSIAN matrix,gray value,and spatial location,were introduced to feature space.The kernel function of mean-shift clustering was divided into product form of three kinds of kernel functions corresponding to the three feature information.Finally,bandwidths were calculated adaptively to determine the bandwidth of each suspected area,and they were used in mean-shift clustering segmentation.Experimental results show that by the introduction of HESSIAN matrix of dot filtering information to mean-shift clustering,nodular regions can be segmented from blood vessels,trachea,or cross regions connected to the nodule,non-nodular areas can be removed from ROIs properly,and ground glass object(GGO)nodular areas can also be segmented.For the experimental data set of 127 different forms of nodules,the average accuracy of the proposed algorithm is more than 90%.

Concomitant Occurrence of Segmental Neurofibromatosis and Lung Adenocarcinoma

Neurofibromatosis type 1 (NF1) caused by a loss-of functional mutation in NF1 encoding neurofibromin is an autosomal dominant disorder characterized by café-au-lait spots, neurofibromas, intertriginous freckles, and Lisch nodules. Segmental neurofibromatosis (SN) represents a postzygotic mutation and loss of heterozygosity in neurofibromin. SN occurring in the elder persons may be associated with internal malignant tumors. Here, we reported a case of 58-year-old woman with concomitant occurrence of SN and lung adenocarcinoma. The onset of SN in aged persons would be a sign of concomitant occurrence of internal malignant tumors.

Ultrasonographic Segmentation of Fetal Lung with Deep Learning

The morbidity and mortality of the fetus is related closely with the neonatal respiratory morbidity, which was caused by the immaturity of the fetal lung primarily. The amniocentesis has been used in clinics to evaluate the maturity of the fetal lung, which is invasive, expensive and time-consuming. Ultrasonography has been developed to examine the fetal lung quantitatively in the past decades as a non-invasive method. However, the contour of the fetal lung required by existing studies was delineated in manual. An automated segmentation approach could not only improve the objectiveness of those studies, but also offer a quantitative way to monitor the development of the fetal lung in terms of morphological parameters based on the segmentation. In view of this, we proposed a deep learning model for automated fetal lung segmentation and measurement. The model was constructed based on the U-Net. It was trained by 3500 data sets augmented from 250 ultrasound images with both the fetal lung and heart manually delineated, and then tested on 50 ultrasound data sets. With the proposed method, the fetal lung and cardiac area were automatically segmented with the accuracy, average IoU, sensitivity and precision being 0.98, 0.79, 0.881 and 0.886, respectively.

Erratum to: An advanced segmentation using area and boundary tracing technique in extraction of lungs region

Erratum to:J.Cent.South Univ.(2014)21:3811-3820DOI:10.1007/s11771-014-2366-9The original version of this article unfortunately contained three mistakes.The mistakes are corrected as follows:1)The spelling of third author is incorrect.The correct name is Jae-Young PYUN.2)The information of corresponding author is incorrect.The correct information should be Goo-Rak KWON,Professor,PhD;Tel/Fax:+98-711-7264102;E-mail:grkwon@chosun.ac.

CT三维重建联合胸腔镜精准肺段切除术对早期肺腺癌近期结果的影响分析

目的 探讨CT三维重建联合胸腔镜精准肺段切除术对早期肺腺癌近期结果的影响。方法 回顾性分析2020年1—12月间在惠州市中心人民医院接受肺段切除术治疗的早期肺腺癌病人100例的临床资料,根据手术方式的不同分为接受CT三维重建联合胸腔镜精准肺段切除术治疗的精准治疗组(n=41)、接受传统胸腔镜肺段切除手术治疗的传统治疗组(n=59),对比其手术情况相关指标、肺功能参数值、术后并发症发生情况、术后1年复发情况的差异。结果 精准治疗组病人的手术时间(102.31±19.38)min短于传统治疗组(118.64±21.23)min,术中出血量(62.45±8.34)mL少于传统治疗组(77.23±10.21)mL,差异有统计学意义(P<0.05)。精准治疗组、传统治疗组病人的淋巴结清扫个数差异无统计学意义(P>0.05)。术后1个月,精准治疗组病人的用力肺活量(FVC)(3.40±0.56)L、第1秒用力呼气容积(FEV1)(2.31±0.40)L、肺一氧化碳弥散量(DLCO)(17.95±2.68)mL·mmHg-1·min-1水平分别高于传统治疗组的(2.86±0.41)L、(2.08±0.36)L、(14.53±2.10)mL·mmHg-1·min-1,差异有统计学意义(P<0.05)。术后3个月,精准治疗组、传统治疗组病人的FVC、FEV1、DLCO水平差异无统计学意义(P>0.05)。精准治疗组、传统治疗组病人的术后并发症发生率差异无统计学意义(P>0.05)。术后随访1年内,两组病人均未发现复发病例。结论 CT三维重建联合胸腔镜精准肺段切除术可优化手术过程、改善早期肺腺癌病人术后早期的肺功能,可能是一种更为精准可靠的手术模式。

基于CAMU-Net的肺结节分割方法

肺癌作为世界上死亡率最高的癌症之一,严重威胁人类的生命安全,早发现早治疗可以提高患者的生存率.为了准确地分割出肺部CT图像中的肺结节区域,提出一种基于CAM U-Net的肺结节分割方法.在U-Net网络基础上,通过添加通道注意力模块CAM,使网络中的特征聚焦于关键有用的信息,减弱甚至消除无关信息的干扰,进而提升模型的性能.在LIDC-IDRI肺结节公开数据集上的实验结果表明:该算法的交并比、Dice相似系数、准确率、和召回率分别为82.04%、89.24%、88.61%和91.28%.与其他肺结节分割方法相比,该算法具有更好的分割性能.

基于Unet+Attention的胸部CT影像支气管分割算法

目前肺气管分割中,由于CT图像灰度分布复杂,分割目标像素近似,易造成过分割;而且肺气管像素较少,难以得到更多目标特征,造成细小肺气管容易被忽略。针对这些难点,本研究提出结合Unet网络和注意力机制的肺气管分割算法,注意力机制使用的是关注通道域和空间域的卷积块注意力模型(CBAM),该模型提高了气管特征权重。在损失函数方面,针对原始数据中正负样本失衡的问题,引入focal loss损失函数,该函数对标准交叉熵损失函数进行了改进,使难分类样本在训练过程中得到更多关注;最后通过八连通域判断将孤立点去除,保留较大的几个连通域,即最后的肺气管部分。选用由合作医院提供的24组CT影像和43组CTA影像,共计26157张切片图像作为数据集,进行分割实验。结果表明,分割准确率能够达到0.86,过分割率和欠分割率均值为0.28和0.39。经过注意力模块和损失函数的消融实验,在改进前的准确率、过分割率和欠分割率分别为0.81、0.30、0.40,可见其分割效果均不如Unet+Attention方法。与其他常用方法在相同条件下进行比较后,在保证过分割率和欠分割率不变的情况下,所提出的算法得到了最高的准确率,较好地解决了细小气管分割不准确的问题。

SOSNet:一种非对称编码器-解码器结构的非小细胞肺癌CT图像分割模型

非小细胞肺癌严重损害人类健康,早期非小细胞肺癌CT(Computed Tomography)图像中的肿瘤结节体积小,不易发现,极易造成漏诊和误诊.为了精确分割非小细胞肺癌CT图像中的小体积肿瘤结节,本文提出SOSNet(Small Object Segmentation Networks)自动分割模型,利用ResNet(Residual Network)基础层和空洞卷积构造非对称编码器-解码器结构作为分割主网络,利用轴向取反注意力模块ARA(Axial Reverse Attention)逐步擦除背景中对分割有影响的结构,再使用结构细化模块SR(Structure Refinement)对主网络输出的粗略特征图进行结构细化,从而实现非小细胞肺癌肿瘤结节分割.在非小细胞肺癌公开数据集的实验测试表明,本文提出的小目标自动分割模型SOSNet可以有效分割出非小细胞肺癌CT图像中的小体积肿瘤结节,其mDice(mean-Dice)、mIoU(mean Intersection over Union)、Sensitivity、F1、Specificity、平均绝对误差MAE(Mean Absolute Error)均优于当前最先进的小目标分割模型CaraNet(Context Axial Reverse Attention Network).

基于多任务学习的间质性肺病分割算法

间质性肺病(ILD)的分割标签标注成本极高,且现有数据集通常存在样本量较少的问题,导致训练的模型效果较差。针对该问题,提出一种基于多任务学习的ILD分割算法。首先,基于U-Net构建多任务分割模型;其次,使用生成的肺部分割标签作为辅助任务标签进行多任务学习;最后,使用一种自适应调整多任务损失函数权重的方法,平衡主任务和辅助任务的损失。在自构建的ILD数据集上的实验结果表明,多任务分割模型的Dice相似系数(DSC)达到了82.61%,与U-Net相比提升了2.26个百分点。验证了所提算法可以提升ILD的分割性能,协助临床医生进行ILD诊断。

基于Swin Transformer和UNet的肺结节分割方法

肺结节的准确分割是后续良恶性分析和诊断的关键。由于基于卷积神经网络的分割模型受限于局部特征提取特性,忽略了全局特征。因此,本文提出了一种新的肺结节语义分割框架ST-UNet网络,将Swin Transformer嵌入UNet中,构成一种新颖的Swin Transformer和CNN并行的双编码器结构。结果表明:该模型不仅对肺结节的分割具有较好的性能,而且对医生进行肺结节的早期诊断具有重要的临床意义和应用价值。

胸腔镜下肺段切除术与肺楔形切除术治疗早期肺癌的临床效果对比

目的:探讨胸腔镜下肺段切除术与肺楔形切除术治疗早期肺癌的临床效果对比。方法:选取2021年6月—2022年12月九江市第一人民医院收治的60例早期肺癌患者为研究对象,按照随机数字表法分为研究组和对照组,各30例。研究组行胸腔镜下肺段切除术,对照组行胸腔镜下肺楔形切除术。比较两组围手术期相关指标、肺功能指标、复发率和并发症。结果:研究组手术时间长于对照组,住院时间、胸管引流时间均短于对照组,胸腔引流量、术中出血量均少于对照组,淋巴结清扫数目多于对照组,差异均有统计学意义(P<0.05)。术后6个月,研究组用力肺活量(FVC)、第1秒用力呼气容积(FEV1)及最大自主通气量(MVV)水平均大于对照组,差异均有统计学意义(P<0.05)。两组复发率及并发症发生率比较,差异均无统计学意义(P>0.05)。结论:早期肺癌患者采用胸腔镜下肺段切除术能够改善围手术期相关指标和肺功能指标,且安全性良好。

基于多尺度级联注意网络的肺实质分割

针对肺实质分割任务中不同尺度特征的全局上下文信息利用率低、分割精度低、分割细节模糊等问题,提出一种多尺度级联注意网络(multiscale cascaded attention networks,MCANet)。该网络主要由多尺度特征提取网络(multi-scale feature extraction network,MSFENet)、多尺度注意力引导模块(multi-scale attention guidance module,MSAG)、解码特征整合器(decoding feature integrator,DFI)组成。首先,设计MSFENet以提高特征信息在不同通道维度上的空间交互能力,在采样过程中最大限度地保留图像的关键特征,丰富全局上下文信息。然后,设计MSAG提高模型在解码过程中对多尺度特征信息的利用率,并最大限度地融合两种注意力机制的优势。最后设计DFI,重新整合解码器生成的解码特征,以提高模型对边缘信息的分割性能。在LUNA16数据集上对模型性能进行实验验证,得到了0.993的Dice和3.864的HD,实验结果证明了MCANet与其他主流医学分割模型相比有更优异的分割性能,能更准确地分割肺实质。

胸腔镜辅助单孔与多孔肺段切除术治疗早期NSCLC的应用价值

目的探讨胸腔镜下单孔与多孔肺段切除术对早期非小细胞肺癌(Non-small cell lung cancer,NSCLC)患者代谢反应及心肺耐力的影响。方法选取2017年6月-2022年10月南通市第一人民医院早期NSCLC患者92例,根据简单随机数字表法分为多孔组与单孔组,各46例。单孔组采取胸腔镜单孔肺段切除术,多孔组采取胸腔镜多孔肺段切除术。比较两组围术期情况、术前及术后3 d代谢反应指标[视黄醇结合蛋白(Retinol-binding protein,RBP)、转铁蛋白(Transferrin,TRF)、前白蛋白(Prealbumin,PA)]水平、心肺耐力[6 min步行距离(6 min walking distance,6MWT)、疲劳指数、呼气峰流速(Peak expiratory velocity,PEF)、第1 s用力呼气容积(Forced expiratory volume 1 s,FEV1)]和并发症发生率。结果(1)两组手术时长、淋巴结清扫数目比较,差异无统计学意义(P>0.05),单孔组术中失血量、引流量少于多孔组,引流管放置时间、住院时长短于多孔组,差异有统计学意义(P<0.05)。(2)术后3 d两组PA、TRF、RBP水平较术前下降,但单孔组PA、TRF、RBP水平高于多孔组,差异有统计学意义(P<0.05)。(3)术后3 d两组6MWT、PEF、FEV1较术前降低,疲劳指数较术前增高,但单孔组6MWT、PEF、FEV1高于多孔组,疲劳指数低于多孔组,差异有统计学意义(P<0.05)。(4)单孔组并发症发生率(4.35%)低于多孔组(17.39%),差异有统计学意义(P<0.05)。结论采取胸腔镜单孔及多孔肺段切除术治疗早期NSCLC均可取得良好效果,但单孔术式可减少失血量,对代谢状态及心肺耐力影响较小,利于机体功能及早康复,且可降低并发症发生风险。

单孔全胸腔镜解剖性肺段切除术与肺叶切除术治疗非小细胞肺癌的疗效比较

目的比较分析单孔全胸腔镜解剖性肺段切除术与肺叶切除术治疗非小细胞肺癌的疗效。方法回顾性分析2018年6月到2023年6月信阳市中心医院收治的行单孔全胸腔镜解剖性肺段切除术治疗的60例非小细胞肺癌患者(A组)和行单孔全胸腔镜解剖性肺叶切除术治疗的60例非小细胞肺癌患者(B组),比较2组围手术期指标、肺功能指标、炎症指标、并发症发生情况等。结果A组手术时间长于B组,术中出血量、术后12 h引流量、住院时间均低于B组(t=2.272,P=0.025;t=9.660,P<0.001;t=12.703,P<0.001;t=11.706,P<0.001)。A组和B组术后3个月第1秒用力呼气容积(FEV1)、用力肺活量(FVC)、每分钟最大通气量(MVV)均低于术前1 d(P<0.05),且A组术后3个月FVC、FEV1、MVV高于B组(t=5.166,P<0.001;t=4.108,P<0.001;t=5.861,P<0.001)。A组术后1 d、术后3 d肿瘤坏死因子-α(TNF-α)、白介素-1β(IL-1β)、IL-6、C反应蛋白(CRP)均低于B组,A组和B组术后3 d TNF-α、IL-1β、IL-6、CRP均低于术后1 d(P均<0.05)。A组并发症总发生率低于B组(χ^(2)=0.901,P=0.343)。结论单孔全胸腔镜解剖性肺段切除术与肺叶切除术治疗非小细胞肺癌均有较好的预后,并发症发生风险小,且单孔全胸腔镜解剖性肺段切除术对患者肺功能影响更小,炎症反应更轻,术后恢复更快。

单孔胸腔镜下膨胀萎缩法解剖性肺段切除术治疗早期NSCLC的疗效观察

目的探讨单孔胸腔镜下膨胀萎缩法解剖性肺段切除术治疗早期NSCLC的疗效及对患者肺功能的影响。方法选取82例NSCLC患者,根据手术方式差异分为2组。研究组患者40例,采用单孔胸腔镜下改良膨胀萎缩法解剖性肺段切除术治疗;对照组患者42例,采用单孔胸腔镜下肺叶切除术治疗。比较2组患者的临床疗效等差异。结果研究组的住院时间和术中出血量显著少于对照组(P<0.05),而其余围术期指标2组比较无统计学差异(P>0.05)。术后2组的肺功能指标均较术前降低,且对照组降低更甚(P<0.05)。术后2组的血清相关指标均较术前升高,且对照组升高更甚(P<0.05)。术后2组的生活质量均较术前提高,且研究组提高更多(P<0.05)。结论单孔胸腔镜下改良膨胀萎缩法解剖性肺段切除术对早期NSCLC的疗效更佳,不仅可改善其肺功能,还可减轻炎性反应,最终有利于生活质量的提高,值得临床推广应用。