Publication detail

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.

Original Title

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

Type

journal article in Web of Science

Language

English

Original Abstract

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.

Keywords

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

Authors

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

Released

1. 10. 2018

Publisher

Elsevier B.V.

Location

Amsterdam, The Netherlands

ISBN

1361-8415

Periodical

MEDICAL IMAGE ANALYSIS

Year of study

49

Number

C

State

Kingdom of the Netherlands

Pages from

76

Pages to

88

Pages count

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"
}