Detail publikace

Deep convolutional neural network-based segmentation and classification of difficult to define metastatic spinal lesions in 3D CT data

CHMELÍK, J. JAKUBÍČEK, R. WALEK, P. JAN, J. OUŘEDNÍČEK, P. LAMBERT, L. AMADORI, E. GAVELLI, G.

Originální název

Deep convolutional neural network-based segmentation and classification of difficult to define metastatic spinal lesions in 3D CT data

Typ

článek v časopise ve Web of Science, Jimp

Jazyk

angličtina

Originální abstrakt

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.

Klíčová slova

CT analysis; spinal metastasis; convolutional neural network; computer aided detection

Autoři

CHMELÍK, J.; JAKUBÍČEK, R.; WALEK, P.; JAN, J.; OUŘEDNÍČEK, P.; LAMBERT, L.; AMADORI, E.; GAVELLI, G.

Vydáno

1. 10. 2018

Nakladatel

Elsevier B.V.

Místo

Amsterdam, The Netherlands

ISSN

1361-8415

Periodikum

MEDICAL IMAGE ANALYSIS

Ročník

49

Číslo

C

Stát

Nizozemsko

Strany od

76

Strany do

88

Strany počet

13

URL

http://dx.doi.org/10.1016/j.media.2018.07.008

BibTex

@article{BUT149034,
  author="CHMELÍK, J. and JAKUBÍČEK, R. and WALEK, P. and JAN, J. and OUŘEDNÍČEK, P. and LAMBERT, L. and AMADORI, E. and GAVELLI, G.",
  title="Deep convolutional neural network-based segmentation and classification of difficult to define metastatic spinal lesions in 3D CT data",
  journal="MEDICAL IMAGE ANALYSIS",
  year="2018",
  volume="49",
  number="C",
  pages="76--88",
  doi="10.1016/j.media.2018.07.008",
  issn="1361-8415",
  url="http://dx.doi.org/10.1016/j.media.2018.07.008"
}