Raab, Florian and Malloni, Wilhelm and Wein, Simon and Greenlee, Mark W. and Lang, Elmar W. (2023) Investigation of an efficient multi-modal convolutional neural network for multiple sclerosis lesion detection. SCIENTIFIC REPORTS, 13 (1): 21154. ISSN 2045-2322,
Full text not available from this repository. (Request a copy)Abstract
In this study, an automated 2D machine learning approach for fast and precise segmentation of MS lesions from multi-modal magnetic resonance images (mmMRI) is presented. The method is based on an U-Net like convolutional neural network (CNN) for automated 2D slice-based-segmentation of brain MRI volumes. The individual modalities are encoded in separate downsampling branches without weight sharing, to leverage the specific features. Skip connections input feature maps to multi-scale feature fusion (MSFF) blocks at every decoder stage of the network. Those are followed by multi-scale feature upsampling (MSFU) blocks which use the information about lesion shape and location. The CNN is evaluated on two publicly available datasets: The ISBI 2015 longitudinal MS lesion segmentation challenge dataset containing 19 subjects and the MICCAI 2016 MSSEG challenge dataset containing 15 subjects from various scanners. The proposed multi-input 2D architecture is among the top performing approaches in the ISBI challenge, to which open-access papers are available, is able to outperform state-of-the-art 3D approaches without additional post-processing, can be adapted to other scanners quickly, is robust against scanner variability and can be deployed for inference even on a standard laptop without a dedicated GPU.
| Item Type: | Article |
|---|---|
| Uncontrolled Keywords: | SEGMENTATION; |
| Subjects: | 000 Computer science, information & general works > 004 Computer science 100 Philosophy & psychology > 150 Psychology |
| Divisions: | Human Sciences > Institut für Psychologie > Lehrstuhl für Psychologie I (Allgemeine Psychologie I und Methodenlehre) - Prof. Dr. Mark W. Greenlee Informatics and Data Science |
| Depositing User: | Dr. Gernot Deinzer |
| Date Deposited: | 19 Apr 2024 14:01 |
| Last Modified: | 19 Apr 2024 14:01 |
| URI: | https://pred.uni-regensburg.de/id/eprint/61204 |
Actions (login required)
 |
View Item |
