A CONVOLUTIONAL NEURAL NETWORK APPROACH TO AUTOMATED LUNG BOUNDING BOX ESTIMATION FROM COMPUTED TOMOGRAPHY SCANS

被引：0
|
作者：
Hatt, Charles R. [1 ,3 ]
Ram, Sundaresh [1 ,2 ]
Galban, Craig J. [1 ,2 ]
机构：
[1] Univ Michigan, Dept Radiol, Ctr Mol Imaging, Ann Arbor, MI 48109 USA
[2] Univ Michigan, Dept Biomed Engn, Ann Arbor, MI 48109 USA
[3] Imbio LLC, Minneapolis, MN 55413 USA
来源：
2019 IEEE DATA SCIENCE WORKSHOP (DSW) | 2019年
关键词：
Computed Tomography; Convolutional Neural Network; Chronic Obstructive Pulmonary Disease; Computer Vision; Image Processing;
D O I：
10.1109/dsw.2019.8755594
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
In this work, a convolutional neural network (CNN) based method for automated lung boundary estimation from computed tomography (CT) scans is presented and validated. The CNN model was trained to regress the locations of the superior and inferior borders of the lungs from multiple tissue-specific 2D projections of thoracic CT images. The model utilized a DenseNet architecture and was trained and evaluated on CT images from the COPDGene study. The median (95th percentile) localization error was 2.51 (11.18) for the inferior border and 1.52 (7.21) for the superior border of the lungs.
引用
收藏
页码：213 / 216
页数：4
相关论文
共 50 条
  • [1] Lung Cancer Detection from Computed Tomography (CT) Scans using Convolutional Neural Network
    Khumancha, M. Bikromjit
    Barai, Aarti
    Rao, C. B. Rama
    [J]. 2019 10TH INTERNATIONAL CONFERENCE ON COMPUTING, COMMUNICATION AND NETWORKING TECHNOLOGIES (ICCCNT), 2019,
  • [2] Convolutional Neural Networks for Lung Cancer Screening in Computed Tomography (CT) Scans
    Rao, Prajwal
    Pereira, Nishal Ancelette
    Srinivasan, Raghuram
    [J]. PROCEEDINGS OF THE 2016 2ND INTERNATIONAL CONFERENCE ON CONTEMPORARY COMPUTING AND INFORMATICS (IC3I), 2016, : 489 - 493
  • [3] Convolutional neural network algorithms for computed tomography scans of chronic rhinosinusitis subtypes
    Gudis, David A.
    [J]. INTERNATIONAL FORUM OF ALLERGY & RHINOLOGY, 2021, 11 (02) : 210 - 210
  • [4] Convolutional neural network-based kidney volume estimation from low-dose unenhanced computed tomography scans
    Mueller, Lukas
    Tibyampansha, Dativa
    Mildenberger, Peter
    Panholzer, Torsten
    Jungmann, Florian
    Halfmann, Moritz C.
    [J]. BMC MEDICAL IMAGING, 2023, 23 (01)
  • [5] Convolutional neural network-based kidney volume estimation from low-dose unenhanced computed tomography scans
    Lukas Müller
    Dativa Tibyampansha
    Peter Mildenberger
    Torsten Panholzer
    Florian Jungmann
    Moritz C. Halfmann
    [J]. BMC Medical Imaging, 23
  • [6] Stroke classification from computed tomography scans using 3D convolutional neural network
    Neethi, A. S.
    Niyas, S.
    Kannath, Santhosh Kumar
    Mathew, Jimson
    Anzar, Ajimi Mol
    Rajan, Jeny
    [J]. BIOMEDICAL SIGNAL PROCESSING AND CONTROL, 2022, 76
  • [7] Deep convolutional neural networks for the automated segmentation of malignant pleural mesothelioma on computed tomography scans
    Gudmundsson, Eyjolfur
    Straus, Christopher M.
    Armato, Samuel G., III
    [J]. JOURNAL OF MEDICAL IMAGING, 2018, 5 (03)
  • [8] A deep Residual U-Net convolutional neural network for automated lung segmentation in computed tomography images
    Khanna, Anita
    Londhe, Narendra D.
    Gupta, S.
    Semwal, Ashish
    [J]. BIOCYBERNETICS AND BIOMEDICAL ENGINEERING, 2020, 40 (03) : 1314 - 1327
  • [9] Automated COVID-19 Grading with Convolutional Neural Networks in Computed Tomography Scans: A Systematic Comparison
    Vente, Coen De
    Boulogne, Luuk H.
    Venkadesh, Kiran Vaidhya
    Sital, Cheryl
    Lessmann, Nikolas
    Jacobs, Colin
    Sanchez, Clara I.
    Ginneken, Bram Van
    [J]. IEEE Transactions on Artificial Intelligence, 2022, 3 (02): : 129 - 138
  • [10] Convolutional neural network evaluation of over-scanning in lung computed tomography
    Colevray, M.
    Tatard-Leitman, V. M.
    Gouttard, S.
    Douek, P.
    Boussel, L.
    [J]. DIAGNOSTIC AND INTERVENTIONAL IMAGING, 2019, 100 (03) : 177 - 183
12345