您好，欢迎访问中国农业科学院 机构知识库！

全部

  • 全部
  • 标题
  • 学者
  • 机构
  • 关键词

Automated Assessment of Renal Calculi in Serial Computed Tomography Scans

文献类型： 会议论文

第一作者： Pritam Mukherjee

作者： Pritam Mukherjee 1 ; Sungwon Lee 1 ; Perry J. Pickhardt 2 ; Ronald M. Summers 1 ;

作者机构： 1.Imaging Biomarkers and Computer-Aided Diagnosis Laboratory, Department of Radiology and Imaging Sciences, National Institutes of Health Clinical Center

2.Department of Radiology, School of Medicine and Public Health, The University of Wisconsin

关键词： Renal calculi;Serial assessment;Deep learning;Registration

会议名称： International Workshop on Applications of Medical Artificial Intelligence

主办单位：

页码： 39-48

摘要： An automated pipeline is developed for the serial assessment of renal calculi using computed tomography (CT) scans obtained at multiple time points. This retrospective study included 722 scans from 330 patients chosen from 8544 asymptomatic patients who underwent two or more CTC (CT colonography) or non-enhanced abdominal CT scans between 2004 and 2016 at a single medical center. A pre-trained deep learning (DL) model was used to segment the kidneys and the calculi on the CT scans at each time point. Based on the output of the DL, 330 patients were identified as having a stone candidate on at least one time point. Then, for every patient in this group, the kidneys from different time points were registered to each other, and the calculi present at multiple time points were matched to each other using proximity on the registered scans. The automated pipeline was validated by having a blinded radiologist assess the changes manually. New graph-based metrics are introduced in order to evaluate the performance of our pipeline. Our method shows high fidelity in tracking changes in renal calculi over multiple time points.

分类号： tp18-53

  • 相关文献

[1]Learning Functional Embedding of Genes Governed by Pair-wised Labels. Cao, Jingjun,Wu, Zhengli,Ye, Wenting,Wang, Haohan. 2017

[2]A Miniature Handheld Automatic Detected Device for Register. Zhang Tiefeng,Wang Jun,Sun Zhihui,Zhao Bo,Lu Guotao. 2011

[3]Near-infrared reflectance Spectroscopy-based methods for phytase registration in feed industry. Yang, Haifeng,Lv, Xiaowen,Wang, Jing,Li, Junguo,Li, Hui,Qin, Yuchang.

[4]Determination of strain fields in porous shape memory alloys usingmicro computed tomography. Therese Bormann,Sebastian Friess,Michael de Wild,Ralf Schumacher,Georg Schulz,Bert Muller. 2010

[5]3D Bone Mineral Density Distribution and Shape Reconstruction of the Proximal Femur from a Single Simulated DXA Image: An In Vitro Study. Tristan Whitmarsh,Ludovic Humbert,Mathieu De Craene,Luis Miguel Del Rio Barquero,Karl Pritscher,Rainer Schubert, Felix Eckstein,Rainer Schubert, Felix Eckstein,Thomas Link,Alejandro F. Frangi. 2010

[6]Sleep Apnea Severity Estimation from Respiratory Related Movements Using Deep Learning. Maziar Hafezi,Nasim Montazeri,Kaiyin Zhu,Hisham Alshaer,Azadeh Yadollahi,Babak Taati. 2019

[7]Class Imbalance Correction for Improved Universal Lesion Detection and Tagging in CT. Peter D. Erickson,Tejas Sudharshan Mathai,Ronald M. Summers. 2022

[8]An Intelligent Network Planning Algorithm for Emergency Communication with Deep Learning. Zongzhe Nie,Wei Zhu,Ruopeng Yang,Yu Wang. 2019

[9]Research on the Key Techniques of Semantic Mining of Information Digest in the Field of Agricultural Major Crops Based on Deep Learning. Ping Qian,Hao G. J. M. Gong,Yunpeng Cui. 2019

[10]DEEP LEARNING WITH ORTHOGONAL VOLUMETRIC HED SEGMENTATION AND 3D SURFACE RECONSTRUCTION MODEL OF PROSTATE MRI. Ruida Cheng,Nathan Lay,Francesca Mertan,Baris Turkbey,Holger R. Roth,Le Lu,William Gandler,Evan S. McCreedy,Thomas Pohida,Peter Choyke,Matthew J. McAuliffe,Ronald M. Summers. 2017

[11]Mapping Rice Area Using Sentinel-1 SAR Data and Deep Learning. Guozhuang Shen,Chenwei Nie. 2023

[12]PSPU-Net for Automatic Short Axis Cine MRI Segmentation of Left and Right Ventricles. Manuel Perez-Pelegri,Irene Del-Canto,Jose V. Monmeneu,Vicente Bodi,Maria P. Lopez-Lereu,David Moratal,Silvia Ruiz-Espana. 2020

[13]Detection of Lymph Nodes in T2 MRI Using Neural Network Ensembles. Zhiyong Lu,Ronald M. Summers,Tejas Sudharshan Mathai,Sungwon Lee,Daniel C. Elton,Thomas C. Shen,Yifan Peng. 2021

[14]W-net: Deep Convolutional Network with Gray-Level Co-occurrence Matrix and Hybrid Loss Function for Hyperspectral Image Classification. Jinchao Jiao,Changqing Yin,Fei Teng. 2023

[15]Integration of Raman Spectroscopy, On-Line Microscopic Imaging and Deep Learning-Based Image Analysis for Real-Time Monitoring of Cell Culture Process. Xiaoli Wang,Guangzheng Zhou,Xue Zhong Wang. 2025

[16]Drug Synergistic Combinations Predictions via Large-Scale Pre-training and Graph Structure Learning. Zhihang Hu,Qinze Yu,Yucheng Guo,Taifeng Wang,Irwin King,Xin Gao,Le Song,Yu Li. 2023

[17]Radiotherapy Dose Optimization via Clinical Knowledge Based Reinforcement Learning. Paul Dubois,Paul-Henry Cournede,Nikos Paragios,Pascal Fenoglietto. 2024

[18]On The Local Geometry of the Pet Reconstruction Problem. Ramy Merabet,Florent Sureau,Alain Trouvé. 2024

[19]Learning with Fixed Point Condition for Convergent PNP PET Reconstruction. Marion Savanier,Claude Comtat,Florent Sureau. 2024

[20]Automated Fruit Classification Based on Deep Learning Utilizing Yolov8. Christine Dewi,Oksam M Kamlasi,Gunjan Chhabra,Guowei Dai,Keshav Kaushik,Inam Ullah Khan. 2023

作者其他论文 更多>>
置顶
购物车
反馈

© 京ICP备09089781号-29 主办单位：中国农业科学院

学术资源： 中国农业科学院 农业专业知识服务系统 农科联盟资源共建共享平台 中国农业科技文献与信息服务平台 中国农业期刊集成服务平台