Automated selection of computed tomography display parameters using neural networks

被引:0
|
作者
Zhang, D [1 ]
Neu, SC [1 ]
Valentino, DJ [1 ]
机构
[1] Univ Calif Los Angeles, Dept Biol Sci, Los Angeles, CA 90095 USA
来源
MEDICAL IMAGING: 2001: IMAGE PROCESSING, PTS 1-3 | 2001年 / 4322卷
关键词
neural networks; computed tomography (CT); automated window/level;
D O I
10.1117/12.431084
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
A collection of artificial neural networks (ANN's) was trained to identify simple anatomical structures in a set of Xray computed tomography (CT) images. These neural networks learned to associate a point in an image with the anatomical structure containing the point by using the image pixels located on the horizontal and vertical lines that ran through the point. The neural networks were integrated into a computer software tool whose function is to select an index into a list of CT window/level values from the location of the, user's mouse cursor. Based upon the anatomical structure selected by the user, the software tool automatically adjusts the image display to optimally view the structure.
引用
收藏
页码:1912 / 1917
页数:2
相关论文
共 50 条
  • [21] COMPUTED TOMOGRAPHY DISPLAY
    EYLER, WR
    FIGLEY, MM
    RADIOLOGY, 1976, 119 (02) : 487 - 488
  • [22] Automated femur segmentation from computed tomography images using a deep neural network
    Bjornsson, P. A.
    Helgason, B.
    Palsson, H.
    Sigurdsson, S.
    Gudnason, V.
    Ellingsen, L. M.
    MEDICAL IMAGING 2021: BIOMEDICAL APPLICATIONS IN MOLECULAR, STRUCTURAL, AND FUNCTIONAL IMAGING, 2021, 11600
  • [23] Development of an automated solder inspection system with neural network using oblique computed tomography
    Teramoto, Atsushi
    Murakoshi, Takayuki
    Tsuzaka, Masatoshi
    Fujita, Hiroshi
    IPACK 2007: PROCEEDINGS OF THE ASME INTERPACK CONFERENCE 2007, VOL 1, 2007, : 453 - 457
  • [24] Automated Pulmonary Nodule Classification in Computed Tomography Images Using a Deep Convolutional Neural Network Trained by Generative Adversarial Networks
    Onishi, Yuya
    Teramoto, Atsushi
    Tsujimoto, Masakazu
    Tsukamoto, Tetsuya
    Saito, Kuniaki
    Toyama, Hiroshi
    Imaizumi, Kazuyoshi
    Fujita, Hiroshi
    BIOMED RESEARCH INTERNATIONAL, 2019, 2019
  • [25] Automated detection and labelling of teeth and small edentulous regions on cone-beam computed tomography using convolutional neural networks
    Gerhardt, Mauricio do Nascimento
    Fontenele, Rocharles Cavalcante
    Leite, Andre Ferreira
    Lahoud, Pierre
    Van Gerven, Adriaan
    Willems, Holger
    Smolders, Andreas
    Beznik, Thomas
    Jacobs, Reinhilde
    JOURNAL OF DENTISTRY, 2022, 122
  • [26] Emphysema subtyping on thoracic computed tomography scans using deep neural networks
    Xie, Weiyi
    Jacobs, Colin
    Charbonnier, Jean-Paul
    Slebos, Dirk Jan
    van Ginneken, Bram
    SCIENTIFIC REPORTS, 2023, 13 (01):
  • [27] Emphysema subtyping on thoracic computed tomography scans using deep neural networks
    Weiyi Xie
    Colin Jacobs
    Jean-Paul Charbonnier
    Dirk Jan Slebos
    Bram van Ginneken
    Scientific Reports, 13
  • [28] Cervical spine fracture detection in computed tomography using convolutional neural networks
    Golla, Alena-Kathrin
    Lorenz, Cristian
    Buerger, Christian
    Lossau, Tanja
    Klinder, Tobias
    Mutze, Sven
    Arndt, Holger
    Spohn, Frederik
    Mittmann, Marlene
    Goelz, Leonie
    PHYSICS IN MEDICINE AND BIOLOGY, 2023, 68 (11):
  • [29] Detection and diagnosis of colitis on computed tomography using deep convolutional neural networks
    Liu, Jiamin
    Wang, David
    Lu, Le
    Wei, Zhuoshi
    Kim, Lauren
    Turkbey, Evrim B.
    Sahiner, Berkman
    Petrick, Nicholas A.
    Summers, Ronald M.
    MEDICAL PHYSICS, 2017, 44 (09) : 4630 - 4642
  • [30] Scan Quality Estimation for Industrial Computed Tomography Using Convolutional Neural Networks
    Kaufmann, Manuel
    Volland, Vivien
    Chen, Yifei
    Effenberger, Ira
    Veyhl, Christoph
    FIFTEENTH INTERNATIONAL CONFERENCE ON QUALITY CONTROL BY ARTIFICIAL VISION, 2021, 11794