Visual Computing for Industry Biomedicine and Art最新文献_第5页

Vector textures derived from higher order derivative domains for classification of colorectal polyps 基于高阶导数域的矢量纹理用于结直肠息肉的分类

IF 2.8 4区计算机科学

Visual Computing for Industry Biomedicine and Art Pub Date : 2022-06-14 DOI: 10.1186/s42492-022-00108-1

Cao, Weiguo, Pomeroy, Marc J., Liang, Zhengrong, Abbasi, Almas F., Pickhardt, Perry J., Lu, Hongbing

{"title":"Vector textures derived from higher order derivative domains for classification of colorectal polyps","authors":"Cao, Weiguo, Pomeroy, Marc J., Liang, Zhengrong, Abbasi, Almas F., Pickhardt, Perry J., Lu, Hongbing","doi":"10.1186/s42492-022-00108-1","DOIUrl":"https://doi.org/10.1186/s42492-022-00108-1","url":null,"abstract":"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 vector-based (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.","PeriodicalId":29931,"journal":{"name":"Visual Computing for Industry Biomedicine and Art","volume":"21 5","pages":""},"PeriodicalIF":2.8,"publicationDate":"2022-06-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138495077","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Collision-aware interactive simulation using graph neural networks 基于图神经网络的碰撞感知交互仿真

IF 2.8 4区计算机科学

Visual Computing for Industry Biomedicine and Art Pub Date : 2022-06-07 DOI: 10.1186/s42492-022-00113-4

Zhu, Xin, Qian, Yinling, Wang, Qiong, Feng, Ziliang, Heng, Pheng-Ann

引用次数: 0

Robust facial expression recognition system in higher poses 鲁棒的高姿态面部表情识别系统

IF 2.8 4区计算机科学

Visual Computing for Industry Biomedicine and Art Pub Date : 2022-05-16 DOI: 10.1186/s42492-022-00109-0

Owusu, Ebenezer, Appati, Justice Kwame, Okae, Percy

引用次数: 1

Analytical study of two feature extraction methods in comparison with deep learning methods for classification of small metal objects 两种特征提取方法与深度学习方法在小金属物体分类中的对比分析研究

IF 2.8 4区计算机科学

Visual Computing for Industry Biomedicine and Art Pub Date : 2022-05-10 DOI: 10.1186/s42492-022-00111-6

S. Amraee, Maryam Chinipardaz, Mohammadali Charoosaei

引用次数: 5

Correction: DCAU-Net: dense convolutional attention U-Net for segmentation of intracranial aneurysm images 校正:dcaunet:用于颅内动脉瘤图像分割的密集卷积注意U-Net

IF 2.8 4区计算机科学

Visual Computing for Industry Biomedicine and Art Pub Date : 2022-05-08 DOI: 10.1186/s42492-022-00110-7

Wenwen Yuan, Yanjun Peng, Yanfei Guo, Yande Ren, Qianwen Xue

引用次数: 2

Influence of postural changes on haemodynamics in internal carotid artery bifurcation aneurysm using numerical methods 体位变化对颈内动脉分叉动脉瘤血流动力学影响的数值研究

IF 2.8 4区计算机科学

Visual Computing for Industry Biomedicine and Art Pub Date : 2022-04-08 DOI: 10.1186/s42492-022-00107-2

Ballambat, Raghuvir Pai, Zuber, Mohammad, Khader, Shah Mohammed Abdul, Ayachit, Anurag, Ahmad, Kamarul Arifin bin, Vedula, Rajanikanth Rao, Kamath, Sevagur Ganesh, Shuaib, Ibrahim Lutfi

{"title":"Influence of postural changes on haemodynamics in internal carotid artery bifurcation aneurysm using numerical methods","authors":"Ballambat, Raghuvir Pai, Zuber, Mohammad, Khader, Shah Mohammed Abdul, Ayachit, Anurag, Ahmad, Kamarul Arifin bin, Vedula, Rajanikanth Rao, Kamath, Sevagur Ganesh, Shuaib, Ibrahim Lutfi","doi":"10.1186/s42492-022-00107-2","DOIUrl":"https://doi.org/10.1186/s42492-022-00107-2","url":null,"abstract":"Cerebral intracranial aneurysms are serious problems that can lead to stroke, coma, and even death. The effect of blood flow on cerebral aneurysms and their relationship with rupture are unknown. In addition, postural changes and their relevance to haemodynamics of blood flow are difficult to measure in vivo using clinical imaging alone. Computational simulations investigating the detailed haemodynamics in cerebral aneurysms have been developed in recent times not only to understand the progression and rupture but also for clinical evaluation and treatment. In the present study, the haemodynamics of a patient-specific case of a large aneurysm on the left side internal carotid bifurcation (LICA) and no aneurysm on the right side internal carotid bifurcation (RICA) was investigated. The simulation of these patient-specific models using fluid–structure interaction provides a valuable comparison of flow behavior between normal and aneurysm models. The influences of postural changes were investigated during standing, sleeping, and head-down (HD) position. Significant changes in flow were observed during the HD position and quit high arterial blood pressure in the internal carotid artery (ICA) aneurysm model was established when compared to the normal ICA model. The velocity increased abruptly during the HD position by more than four times (LICA and RICA) and wall shear stress by four times (LICA) to ten times (RICA). The complex spiral flow and higher pressures prevailing within the dome increase the risk of aneurysm rupture.","PeriodicalId":29931,"journal":{"name":"Visual Computing for Industry Biomedicine and Art","volume":"22 6","pages":"11"},"PeriodicalIF":2.8,"publicationDate":"2022-04-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138495074","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 3

Acquisition repeatability of MRI radiomics features in the head and neck: a dual-3D-sequence multi-scan study 头部和颈部MRI放射组学特征的获取可重复性:双3d序列多重扫描研究

IF 2.8 4区计算机科学

Visual Computing for Industry Biomedicine and Art Pub Date : 2022-04-01 DOI: 10.1186/s42492-022-00106-3

Cindy Xue, J. Yuan, Yihang Zhou, O. Wong, K. Cheung, S. Yu

引用次数: 6

DCAU-Net: dense convolutional attention U-Net for segmentation of intracranial aneurysm images dcaunet:用于颅内动脉瘤图像分割的密集卷积注意U-Net

IF 2.8 4区计算机科学

Visual Computing for Industry Biomedicine and Art Pub Date : 2022-03-28 DOI: 10.1186/s42492-022-00105-4

Wenwen Yuan, Yanjun Peng, Yanfei Guo, Yande Ren, Qianwen Xue

引用次数: 8

Preoperative prediction of lymph node metastasis using deep learning-based features 基于深度学习特征的术前淋巴结转移预测

IF 2.8 4区计算机科学

Visual Computing for Industry Biomedicine and Art Pub Date : 2022-03-07 DOI: 10.1186/s42492-022-00104-5

R. Cattell, Jia Ying, Lan Lei, Jie Ding, Shenglan Chen, Mario Serrano Sosa, Chuan Huang

引用次数: 9

Skin lesion classification system using a K-nearest neighbor algorithm 基于k近邻算法的皮肤病变分类系统

IF 2.8 4区计算机科学

Visual Computing for Industry Biomedicine and Art Pub Date : 2022-03-01 DOI: 10.1186/s42492-022-00103-6

Hatem, Mustafa Qays

引用次数: 17