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Proceedings Paper

Computer-aided detection of focal bone metastases from whole-body multi-modal MRI
Author(s): Jakub Ceranka; Frédéric Lecouvet; Johan de Mey; Jef Vandemeulebroucke
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Paper Abstract

The confident detection and monitoring of metastatic bone disease remains one of the major unfulfilled needs in oncology. Whole-body MRI offers excellent resolution and sensitivity for the detection of neoplastic cells within the bone marrow using so-called anatomical sequences. In combination with whole-body diffusion-weighted functional sequences, it has shown a great potential in the assessment of patient tumor involvement. However, metastatic bone disease can lead to a large amount of bone lesions spread across the skeleton, making it impractical and labor demanding to manually delineate by a radiologist. Computer-aided detection could alleviate the workflow, enabling automatic, accurate and reproducible study of the patient tumor load. In this paper, we propose a fully automated computer-aided detection system for bone metastases composed of two steps. First, whole-body multi-modal MR image preprocessing is performed consisting of intra- and inter-modality image spatial registration, intensity standardization and atlas-based segmentation of the skeleton. The second stage detects the metastases candidates using random forest voxel classification algorithm. The system is evaluated on the dataset of 6 male advanced prostate cancer patients with metastases to the bone using a leave-one-patient-out cross-validation with manual segmentation of the metastases as the reference standard. The proposed system showed metastases detection sensitivity of 0.74 with a median false positive rate of 9.67. In clinical workflow the system could potentially be used as the initial screening and treatment response assessment tool for whole-body multi-modal MRI of any advanced cancer with metastases to the bone

Paper Details

Date Published: 16 March 2020
PDF: 7 pages
Proc. SPIE 11314, Medical Imaging 2020: Computer-Aided Diagnosis, 113140S (16 March 2020); doi: 10.1117/12.2549537
Show Author Affiliations
Jakub Ceranka, Vrije Univ. Brussel (Belgium)
IMEC (Belgium)
Frédéric Lecouvet, Univ. Catholique de Louvain (Belgium)
Johan de Mey, Univ. Ziekenhuis Brussel (Belgium)
Jef Vandemeulebroucke, Vrije Univ. Brussel (Belgium)
IMEC (Belgium)

Published in SPIE Proceedings Vol. 11314:
Medical Imaging 2020: Computer-Aided Diagnosis
Horst K. Hahn; Maciej A. Mazurowski, Editor(s)

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