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.
Ceranka, JW, Lecouvet, F, De Mey, J & Vandemeulebroucke, J 2020, Computer-aided detection of focal bone metastases from whole-body multi-modal MRI. in HK Hahn & MA Mazurowski (eds), SPIE: Medical Imaging 2020: Computer-Aided Diagnosis. vol. 11314, 113140S, Progress in Biomedical Optics and Imaging - Proceedings of SPIE, vol. 11314, SPIE, pp. 1-7, SPIE Medical Imaging 2020, 15/02/20. https://doi.org/10.1117/12.2549537
Ceranka, J. W., Lecouvet, F., De Mey, J., & Vandemeulebroucke, J. (2020). Computer-aided detection of focal bone metastases from whole-body multi-modal MRI. In H. K. Hahn, & M. A. Mazurowski (Eds.), SPIE: Medical Imaging 2020: Computer-Aided Diagnosis (Vol. 11314, pp. 1-7). Article 113140S (Progress in Biomedical Optics and Imaging - Proceedings of SPIE; Vol. 11314). SPIE. https://doi.org/10.1117/12.2549537
@inproceedings{d527a5c1c1a2494799fc73410c72ba0f,
title = "Computer-aided detection of focal bone metastases from whole-body multi-modal MRI",
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.",
keywords = "computer-aided detection, computer-aided detection, bone metastases, whole-body MRI, advanced prostate cancer, diffusion-weighted MRI",
author = "Ceranka, {Jakub Wladyslaw} and Frederic Lecouvet and {De Mey}, Johan and Jef Vandemeulebroucke",
year = "2020",
month = mar,
day = "16",
doi = "10.1117/12.2549537",
language = "English",
volume = "11314",
series = "Progress in Biomedical Optics and Imaging - Proceedings of SPIE",
publisher = "SPIE",
pages = "1--7",
editor = "Hahn, {Horst K.} and Mazurowski, {Maciej A.}",
booktitle = "SPIE: Medical Imaging 2020",
address = "United States",
note = "SPIE Medical Imaging 2020 ; Conference date: 15-02-2020 Through 20-02-2020",
}