Deep convolutional neural network-based segmentation and classification of difficult to define metastatic spinal lesions in 3D CT data
Language English Country Netherlands Media print-electronic
Document type Journal Article, Research Support, Non-U.S. Gov't
PubMed
30114549
DOI
10.1016/j.media.2018.07.008
PII: S1361-8415(18)30552-8
Knihovny.cz E-resources
- Keywords
- CT analysis, Computer aided detection, Convolutional neural network, Spinal metastasis,
- MeSH
- Middle Aged MeSH
- Humans MeSH
- Spinal Neoplasms diagnostic imaging secondary MeSH
- Neural Networks, Computer * MeSH
- Tomography, X-Ray Computed * MeSH
- Radiographic Image Interpretation, Computer-Assisted methods MeSH
- Aged, 80 and over MeSH
- Aged MeSH
- Imaging, Three-Dimensional * MeSH
- Check Tag
- Middle Aged MeSH
- Humans MeSH
- Male MeSH
- Aged, 80 and over MeSH
- Aged MeSH
- Female MeSH
- Publication type
- Journal Article MeSH
- Research Support, Non-U.S. Gov't MeSH
This paper aims to address the segmentation and classification of lytic 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.
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