CT肝脏偶发病灶管理--美国放射学院(ACR)偶发病变委员会白皮书

美国放射学院(American College of Radiology,ACR)偶发病变委员会(Incidental Findings Committee,IFC)发布了关于肝脏CT偶发病灶的管理建议。这些建议是对ACR 2010年胰腺、肾上腺、肾脏及肝脏偶发病变管理白皮书中肝脏部分的更新。该方案由肝脏亚组委员会(包括5位腹部影像学专家,1位肝病学专家和1位肝胆外科医生)制定。这些建议结合已发表的文献及专家意见,并最终达成专家共识。该方案主要基于患者特征及影像学特征对肝脏病灶进行分类,评估终点为某种良性诊断或具体的随访建议。该方案适用于大多数病理或临床情况,但并非所有情况均适用。该方案旨在通过提供肝脏偶发病灶管理意见以提升医疗质量。

Vector textures derived from higher order derivative domains for classification of colorectal polyps

Textures have become widely adopted as an essential tool for lesion detection and classification through analysis of the lesion heterogeneities.In this study,higher order derivative images are being employed to combat the challenge of the poor contrast across similar tissue types among certain imaging modalities.To make good use of the derivative information,a novel concept of vector texture is firstly introduced to construct and extract several types of polyp descriptors.Two widely used differential operators,i.e.,the gradient operator and Hessian operator,are utilized to generate the first and second order derivative images.These derivative volumetric images are used to produce two angle-based and two vectorbased(including both angle and magnitude)textures.Next,a vector-based co-occurrence matrix is proposed to extract texture features which are fed to a random forest classifier to perform polyp classifications.To evaluate the performance of our method,experiments are implemented over a private colorectal polyp dataset obtained from computed tomographic colonography.We compare our method with four existing state-of-the-art methods and find that our method can outperform those competing methods over 4%-13%evaluated by the area under the receiver operating characteristics curves.

Multi-scale characterizations of colon polyps via computed tomographic colonography

Texture features have played an essential role in the field of medical imaging for computer-aided diagnosis.The gray-level co-occurrence matrix(GLCM)-based texture descriptor has emerged to become one of the most successful feature sets for these applications.This study aims to increase the potential of these features by introducing multi-scale analysis into the construction of GLCM texture descriptor.In this study,we first introduce a new parameter-stride,to explore the definition of GLCM.Then we propose three multi-scaling GLCM models according to its three parameters,(1)learning model by multiple displacements,(2)learning model by multiple strides(LMS),and(3)learning model by multiple angles.These models increase the texture information by introducing more texture patterns and mitigate direction sparsity and dense sampling problems presented in the traditional Haralick model.To further analyze the three parameters,we test the three models by performing classification on a dataset of 63 large polyp masses obtained from computed tomography colonoscopy consisting of 32 adenocarcinomas and 31 benign adenomas.Finally,the proposed methods are compared to several typical GLCM-texture descriptors and one deep learning model.LMS obtains the highest performance and enhances the prediction power to 0.9450 with standard deviation 0.0285 by area under the curve of receiver operating characteristics score which is a significant improvement.