Journal article
An end-end deep learning framework for lesion segmentation on multi-contrast MR images-an exploratory study in a rat model of traumatic brain injury
Medical & Biological Engineering & Computing, Vol.61(3), pp.847-865
2023
PMID: 36624356
Abstract
Traumatic brain injury (TBI) engenders traumatic necrosis and penumbra areas of secondary neural injury which are crucial targets for therapeutic interventions. Segmenting manually areas of ongoing changes like necrosis, edema, hematoma, and inflammation is tedious, error prone, and biased. Using the multi parametric MR data from a rodent model study, we demonstrate the effectiveness of an end end deep learning global attention based UNet (GA UNet) framework for automatic segmentation and quantification of TBI lesions. Longitudinal MR scans (2 h, 1, 3, 7, 14, 30, and 60 days) were performed on eight Sprague Dawley rats after controlled cortical injury was performed. TBI lesion and sub regions segmentation was performed using 3D UNet and GA UNet. Dice statistics (DSI) and Hausdorff distance were calculated to assess the performance. MR scan variations based (bias, noise, blur, ghosting) data augmentation was performed to develop a robust model. Training/validation median DSI for U Net was 0.9368 with T2w and MPRAGE inputs, whereas GA UNet had 0.9537 for the same. Testing accuracies were higher for GA UNet than U Net with a DSI of 0.8232 for the T2w MPRAGE inputs. Longitudinally, necrosis remained constant while oligemia and penumbra decreased, and edema appearing around day 3 which increased with time. GA UNet shows promise for multi contrast MR image based segmentation/quantification of TBI in large cohort studies.
Details
- Title
- An end-end deep learning framework for lesion segmentation on multi-contrast MR images-an exploratory study in a rat model of traumatic brain injury
- Authors
- Bhanu Prakash Kn (Corresponding Author) - Bioinformatics Institute (Singapore)Arvind Cs (Author) - Bioinformatics Institute (Singapore)Abdalla Z Mohamed (Author) - University of QueenslandKrishna Kanth Chitta (Author) - Singapore Bioimaging ConsortiumXuan Vinh To (Author) - University of QueenslandHussein Srour (Author) - University of QueenslandFatima A Nasrallah (Author) - University of Queensland
- Publication details
- Medical & Biological Engineering & Computing, Vol.61(3), pp.847-865
- Publisher
- Springer
- DOI
- 10.1007/s11517-022-02752-4
- ISSN
- 1741-0444
- PMID
- 36624356
- Grant note
- 2014000857/ Motor Accident Insurance Commission (MAIC), Queensland Government, Australia
- Organisation Unit
- Thompson Institute
- Language
- English
- Record Identifier
- 99705698302621
- Output Type
- Journal article
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- Web Of Science research areas
- Computer Science, Interdisciplinary Applications
- Engineering, Biomedical
- Mathematical & Computational Biology
- Medical Informatics
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