Bioimaging (Bristol. Print)最新文献_第2页

Curriculum Deep Reinforcement Learning with Different Exploration Strategies: A Feasibility Study on Cardiac Landmark Detection 不同探索策略的课程深度强化学习:心脏地标检测的可行性研究

Bioimaging (Bristol. Print) Pub Date : 2020-04-18 DOI: 10.5220/0008948900370045

P. Astudillo, P. Mortier, M. Beule, F. Wyffels

{"title":"Curriculum Deep Reinforcement Learning with Different Exploration Strategies: A Feasibility Study on Cardiac Landmark Detection","authors":"P. Astudillo, P. Mortier, M. Beule, F. Wyffels","doi":"10.5220/0008948900370045","DOIUrl":"https://doi.org/10.5220/0008948900370045","url":null,"abstract":"Transcatheter aortic valve implantation (TAVI) is associated with conduction abnormalities and the mechanical interaction between the prosthesis and the atrioventricular (AV) conduction path cause these life-threatening arrhythmias. Pre-operative assessment of the location of the AV conduction path can help to understand the risk of post-TAVI conduction abnormalities. As the AV conduction path is not visible on cardiac CT, the inferior border of the membranous septum can be used as an anatomical landmark. Detecting this border automatically, accurately and efficiently would save operator time and thus benefit pre-operative planning. This preliminary study was performed to identify the feasibility of 3D landmark detection in cardiac CT images with curriculum deep Q-learning. In this study, curriculum learning was used to gradually teach an artificial agent to detect this anatomical landmark from cardiac CT. This agent was equipped with a small field of view and burdened with a large ac tion-space. Moreover, we introduced two novel action-selection strategies: α-decay and action-dropout. We compared these two strategies to the already established e-decay strategy and observed that α-decay yielded the most accurate results. Limited computational resources were used to ensure reproducibility. In order to maximize the amount of patient data, the method was cross-validated with k-folding for all three action-selection strategies. An inter-operator variability study was conducted to assess the accuracy of the method","PeriodicalId":162397,"journal":{"name":"Bioimaging (Bristol. Print)","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-04-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132744197","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2

Object Tracking using CSRT Tracker and RCNN 目标跟踪使用CSRT跟踪和RCNN

Bioimaging (Bristol. Print) Pub Date : 2020-04-18 DOI: 10.5220/0009183802090212

Khurshedjon Farkhodov, Suk-Hwan Lee, Ki-Ryong Kwon

引用次数: 12

Artificial Neural Networks for Quantitative Microwave Breast Imaging 用于定量微波乳腺成像的人工神经网络

Bioimaging (Bristol. Print) Pub Date : 2020-04-18 DOI: 10.5220/0009172802040208

M. Ambrosanio, S. Franceschini, F. Baselice, V. Pascazio

引用次数: 3

Detection and Categorisation of Multilevel High-sensitivity Cardiovascular Biomarkers from Lateral Flow Immunoassay Images via Recurrent Neural Networks 利用递归神经网络从侧流免疫分析图像中检测和分类多级高灵敏度心血管生物标志物

Bioimaging (Bristol. Print) Pub Date : 2020-02-29 DOI: 10.5220/0009117901770183

Min Jing, Donal Mc Laughlin, David Steele, Sara Mc Namee, Brian Mac Namee, P. Cullen, D. Finlay, J. Mclaughlin

{"title":"Detection and Categorisation of Multilevel High-sensitivity Cardiovascular Biomarkers from Lateral Flow Immunoassay Images via Recurrent Neural Networks","authors":"Min Jing, Donal Mc Laughlin, David Steele, Sara Mc Namee, Brian Mac Namee, P. Cullen, D. Finlay, J. Mclaughlin","doi":"10.5220/0009117901770183","DOIUrl":"https://doi.org/10.5220/0009117901770183","url":null,"abstract":": Lateral Flow Immunoassays (LFA) have the potential to provide low cost, rapid and highly efﬁcacious Point-of-Care (PoC) diagnostic testing in resource limited settings. Traditional LFA testing is semi-quantitative based on the calibration curve, which faces challenges in the detection of multilevel high-sensitivity biomarkers due its low sensitivity. This paper proposes a novel framework in which the LFA images are acquired from a designed CMOS reader system under controlled lighting. Unlike most existing approaches based on image intensity, the proposed system does not require detection of region of interest (ROI), instead each row of the LFA image was considered as time series signals. The Long Short-Term Memory (LSTM) network was deployed to classify the LFA data obtained from cardiovascular biomarker, C-Reactive Protein (CRP), at eight concentration levels (within the range 0-5mg/L) that are aligned with clinically actionable categories. The performance under different arrangements for input dimension and parameters were evaluated. The preliminary results show that the proposed LSTM outperforms other popular classiﬁcation methods, which demonstrate the capability of the proposed system to detect high-sensitivity CRP and suggests the potential of applications for early risk assessment of cardiovascular diseases (CVD).","PeriodicalId":162397,"journal":{"name":"Bioimaging (Bristol. Print)","volume":"274 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-02-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134172556","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 3

Automated 3D Labelling of Fibroblasts and Endothelial Cells in SEM-Imaged Placenta using Deep Learning 利用深度学习在sem成像的胎盘中自动标记成纤维细胞和内皮细胞

Bioimaging (Bristol. Print) Pub Date : 2020-02-26 DOI: 10.5220/0008949700460053

Benita Scout Mackay, Sophie Blundell, O. Etter, Yunhui Xie, M. McDonnel, M. Praeger, J. Grant-Jacob, R. Eason, Rohan M. Lewis, B. Mills

引用次数: 4

3D Image Deblur using Point Spread Function Modelling for Optical Projection Tomography 基于点扩展函数建模的光学投影断层扫描三维图像去模糊

Bioimaging (Bristol. Print) Pub Date : 2019-08-20 DOI: 10.5220/0007237700670075

Xiaoqin Tang, G. Lamers, F. Verbeek

引用次数: 1

Exploiting bilateral symmetry in brain lesion segmentation 利用双侧对称性分割脑损伤

Bioimaging (Bristol. Print) Pub Date : 2019-07-18 DOI: 10.5220/0008912101160122

Kevin Raina, U. Yahorau, T. Schmah

{"title":"Exploiting bilateral symmetry in brain lesion segmentation","authors":"Kevin Raina, U. Yahorau, T. Schmah","doi":"10.5220/0008912101160122","DOIUrl":"https://doi.org/10.5220/0008912101160122","url":null,"abstract":"Brain lesions, including stroke and tumours, have a high degree of variability in terms of location, size, intensity and form, making automatic segmentation difficult. We propose an improvement to existing segmentation methods by exploiting the bilateral quasi-symmetry of healthy brains, which breaks down when lesions are present. Specifically, we use nonlinear registration of a neuroimage to a reflected version of itself (\"reflective registration\") to determine for each voxel its homologous (corresponding) voxel in the other hemisphere. A patch around the homologous voxel is added as a set of new features to the segmentation algorithm. To evaluate this method, we implemented two different CNN-based multimodal MRI stroke lesion segmentation algorithms, and then augmented them by adding extra symmetry features using the reflective registration method described above. For each architecture, we compared the performance with and without symmetry augmentation, on the SISS Training dataset of the Ischemic Stroke Lesion Segmentation Challenge (ISLES) 2015 challenge. Using affine reflective registration improves performance over baseline, but nonlinear reflective registration gives significantly better results: an improvement in Dice coefficient of 13 percentage points over baseline for one architecture and 9 points for the other. We argue for the broad applicability of adding symmetric features to existing segmentation algorithms, specifically using nonlinear, template-free methods.","PeriodicalId":162397,"journal":{"name":"Bioimaging (Bristol. Print)","volume":"260 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133653487","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 8

Radiotherapy Support Tools, the Brazilian Project: SIPRAD 放射治疗支持工具，巴西项目:SIPRAD

Bioimaging (Bristol. Print) Pub Date : 2019-02-22 DOI: 10.5220/0007482901370143

D. F. Carvalho, José Antonio Camacho Guerrero, Luis Javier Maldonado Zapata, A. Uscamayta, H. Vale, L. F. Borges, A. C. Bruno, H. Oliveira

{"title":"Radiotherapy Support Tools, the Brazilian Project: SIPRAD","authors":"D. F. Carvalho, José Antonio Camacho Guerrero, Luis Javier Maldonado Zapata, A. Uscamayta, H. Vale, L. F. Borges, A. C. Bruno, H. Oliveira","doi":"10.5220/0007482901370143","DOIUrl":"https://doi.org/10.5220/0007482901370143","url":null,"abstract":"The radiotherapy planning process (teletherapy) is initially performed by the acquisition of Computed Tomography images of the areas of interest to guide a series of health professionals in the work of vector design of regions of interest for protection (risk organs) and radiation (tumors). All these steps are performed using computational tools that extrapolate measurements and scales in the treatment plan. The efficiency of the treatment depends on the recreation of the patient's positioning on the linear accelerator stretcher with the previously acquired tomography images. For this, in this article, we present three modules of the SIPRAD (Information Systems for Radiation Therapy Planning) project. With the name of Radiotherapy Portal it is able to perform a fusion of planar images of the target region, made on the day of treatment, with the digital recreation (DDR - Digital Reconstructed Radiographs) of this radiograph generated from the Tomography of treatment planning, aiming to improve the reproducibility of the positioning that the radiation dose delivered during all the radiotherapy treatment. The second module named by LYRIA PACS RT provides a client/server architecture for storing, distributing and displaying images from any systems using the DICOM RT Struct, Image, Plan and Dose modes. The third module called Contouring is responsible for the training of new radiotherapists.","PeriodicalId":162397,"journal":{"name":"Bioimaging (Bristol. Print)","volume":"48 21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-02-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115341178","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Deep Analysis of CNN Settings for New Cancer Whole-slide Histological Images Segmentation: The Case of Small Training Sets 新型肿瘤全切片组织图像分割CNN设置的深度分析:以小训练集为例

Bioimaging (Bristol. Print) Pub Date : 2019-02-22 DOI: 10.5220/0007406601200128

Sonia Mejbri, C. Franchet, I. Reshma, J. Mothe, P. Brousset, Emmanuel Faure

引用次数: 15

Shape Recognition in High-level Image Representations: Data Preparation and Framework of Recognition Method 高级图像表示中的形状识别:数据准备与识别方法框架

Bioimaging (Bristol. Print) Pub Date : 2019-02-22 DOI: 10.5220/0007579000570064

J. Lazarek, P. Szczepaniak

引用次数: 0