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.

Department of Radiology 1st Faculty of Medicine Charles University and General University Hospital Prague Prague U Nemocnice 499 2 128 08 Czechia

Faculty of Electrical Engineering and Communication Department of Biomedical Engineering Brno University of Technology Brno Technicka 3082 12 616 00 Czechia

Istituto Scientifico Romagnolo per lo Studio e la Cura dei Tumori Meldola FC Via Piero Maroncelli 40 470 14 Italy

Philips Healthcare AE Eindhoven High Tech Campus 34 5656 Netherlands; Department of Medical Imaging St Anne's University Hospital Brno and Faculty of Medicine Masaryk University Brno Brno Pekarska 663 53 656 91 Czechia

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