A Semiautomatic Method for Multiple Sclerosis Lesion Segmentation on Dual-Echo MR Imaging: Application in a Multicenter Context
| Autor: | Alvino Bisecco, Massimo Filippi, Laura Mancini, S. Ropele, David L. Thomas, Hugo Vrenken, Loredana Storelli, N. De Stefano, Maria A. Rocca, Marco Battaglini, Elisabetta Pagani, Christian Enzinger, Mark A. Horsfield, Antonio Gallo, Paolo Preziosa |
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| Přispěvatelé: | Storelli, L., Pagani, E., Rocca, M. A., Horsfield, M. A., Gallo, Antonio, Bisecco, Alvino, Battaglini, M., De Stefano, N., Vrenken, H., Thomas, D. L., Mancini, L., Ropele, S., Enzinger, C., Preziosa, P., Filippi, M., Physics and medical technology, Amsterdam Neuroscience - Neuroinfection & -inflammation, Storelli, L, Pagani, E, Rocca, Ma, Horsfield, Ma, Gallo, A, Bisecco, A, Battaglini, M, De Stefano, N, Vrenken, H, Thomas, Dl, Mancini, L, Ropele, S, Enzinger, C, Preziosa, P, Filippi, Massimo |
| Rok vydání: | 2016 |
| Předmět: |
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medicine.medical_specialty Mean squared error Lesion Identification business.industry Adult Brain Image processing Pattern recognition Mr imaging 030218 nuclear medicine & medical imaging 03 medical and health sciences 0302 clinical medicine Radiology Nuclear Medicine and Imaging Neurology (clinical) Nuclear Medicine and Imaging Medicine Radiology Nuclear Medicine and imaging Dual echo Segmentation Artificial intelligence Radiology business Multiple sclerosis lesion 030217 neurology & neurosurgery |
| Zdroj: | Storelli, L, Pagani, E, Rocca, M A, Horsfield, M A, Gallo, A, Bisecco, A, Battaglini, M, De Stefano, N, Vrenken, H, Thomas, D L, Mancini, L, Ropele, S, Enzinger, C, Preziosa, P & Filippi, M 2016, ' A Semiautomatic Method for Multiple Sclerosis Lesion Segmentation on Dual-Echo MR Imaging: Application in a Multicenter Context ', American Journal of Neuroradiology, vol. 37, no. 11, pp. 2043-2049 . https://doi.org/10.3174/ajnr.A4874 American Journal of Neuroradiology, 37(11), 2043-2049. American Society of Neuroradiology AJNR Am J Neuroradiol |
| ISSN: | 1936-959X 0195-6108 |
| Popis: | BACKGROUND AND PURPOSE: The automatic segmentation of MS lesions could reduce time required for image processing together with inter- and intraoperator variability for research and clinical trials. A multicenter validation of a proposed semiautomatic method for hyperintense MS lesion segmentation on dual-echo MR imaging is presented. MATERIALS AND METHODS: The classification technique used is based on a region-growing approach starting from manual lesion identification by an expert observer with a final segmentation-refinement step. The method was validated in a cohort of 52 patients with relapsing-remitting MS, with dual-echo images acquired in 6 different European centers. RESULTS: We found a mathematic expression that made the optimization of the method independent of the need for a training dataset. The automatic segmentation was in good agreement with the manual segmentation (dice similarity coefficient = 0.62 and root mean square error = 2 mL). Assessment of the segmentation errors showed no significant differences in algorithm performance between the different MR scanner manufacturers (P > .05). CONCLUSIONS: The method proved to be robust, and no center-specific training of the algorithm was required, offering the possibility for application in a clinical setting. Adoption of the method should lead to improved reliability and less operator time required for image analysis in research and clinical trials in MS. |
| Databáze: | OpenAIRE |
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