| CHMELIK, Jiri, Roman JAKUBICEK, Petr WALEK, Jiri JAN, Petr OUŘEDNÍČEK, Lukas LAMBERT, Elena AMADORI and Giampaolo GAVELLI. Deep convolutional neural network-based segmentation and classification of difficult to define metastatic spinal lesions in 3D CT data. Medical Image Analysis. AMSTERDAM: ELSEVIER SCIENCE BV, 2018, vol. 49, OCT 2018, p. 76-88. ISSN 1361-8415. Available from: https://dx.doi.org/10.1016/j.media.2018.07.008. |
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@article{1505597,
author = {Chmelik, Jiri and Jakubicek, Roman and Walek, Petr and Jan, Jiri and Ouředníček, Petr and Lambert, Lukas and Amadori, Elena and Gavelli, Giampaolo},
article_location = {AMSTERDAM},
article_number = {OCT 2018},
doi = {http://dx.doi.org/10.1016/j.media.2018.07.008},
keywords = {CT analysis; Spinal metastasis; Convolutional neural network; Computer aided detection},
language = {eng},
issn = {1361-8415},
journal = {Medical Image Analysis},
title = {Deep convolutional neural network-based segmentation and classification of difficult to define metastatic spinal lesions in 3D CT data},
volume = {49},
year = {2018}
}
TY - JOUR
ID - 1505597
AU - Chmelik, Jiri - Jakubicek, Roman - Walek, Petr - Jan, Jiri - Ouředníček, Petr - Lambert, Lukas - Amadori, Elena - Gavelli, Giampaolo
PY - 2018
TI - Deep convolutional neural network-based segmentation and classification of difficult to define metastatic spinal lesions in 3D CT data
JF - Medical Image Analysis
VL - 49
IS - OCT 2018
SP - 76-88
EP - 76-88
PB - ELSEVIER SCIENCE BV
SN - 13618415
KW - CT analysis
KW - Spinal metastasis
KW - Convolutional neural network
KW - Computer aided detection
N2 - This paper aims to address the segmentation and classification of lyric and sclerotic metastatic lesions that are difficult to define by using spinal 3D Computed Tomography (CT) images obtained from highly pathologically affected cases. As the lesions are ill-defined and consequently it is difficult to find relevant image features that would enable detection and classification of lesions by classical methods of texture and shape analysis, the problem is solved by automatic feature extraction provided by a deep Convolutional Neural Network (CNN). Our main contributions are: (i) individual CNN architecture, and pre-processing steps that are dependent on a patient data and a scan protocol - it enables work with different types of CT scans; (ii) medial axis transform (MAT) post-processing for shape simplification of segmented lesion candidates with Random Forest (RF) based meta-analysis; and (iii) usability of the proposed method on whole-spine CTs (cervical, thoracic, lumbar), which is not treated in other published methods (they work with thoracolumbar segments of spine only). Our proposed method has been tested on our own dataset annotated by two mutually independent radiologists and has been compared to other published methods. This work is part of the ongoing complex project dealing with spine analysis and spine lesion longitudinal studies. (C) 2018 Elsevier B.V. All rights reserved.
ER -
CHMELIK, Jiri, Roman JAKUBICEK, Petr WALEK, Jiri JAN, Petr OUŘEDNÍČEK, Lukas LAMBERT, Elena AMADORI and Giampaolo GAVELLI. Deep convolutional neural network-based segmentation and classification of difficult to define metastatic spinal lesions in 3D CT data. \textit{Medical Image Analysis}. AMSTERDAM: ELSEVIER SCIENCE BV, 2018, vol.~49, OCT 2018, p.~76-88. ISSN~1361-8415. Available from: https://dx.doi.org/10.1016/j.media.2018.07.008.
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