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Stacked hourglass network with a multi-level attention mechanism: where to Look for intervertebral disc labeling

Azad, R ; Sharif University of Technology | 2021

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  1. Type of Document: Article
  2. DOI: 10.1007/978-3-030-87589-3_42
  3. Publisher: Springer Science and Business Media Deutschland GmbH , 2021
  4. Abstract:
  5. Labeling vertebral discs from MRI scans is important for the proper diagnosis of spinal related diseases, including multiple sclerosis, amyotrophic lateral sclerosis, degenerative cervical myelopathy and cancer. Automatic labeling of the vertebral discs in MRI data is a difficult task because of the similarity between discs and bone area, the variability in the geometry of the spine and surrounding tissues across individuals, and the variability across scans (manufacturers, pulse sequence, image contrast, resolution and artefacts). In previous studies, vertebral disc labeling is often done after a disc detection step and mostly fails when the localization algorithm misses discs or has false positive detection. In this work, we aim to mitigate this problem by reformulating the semantic vertebral disc labeling using the pose estimation technique. To do so, we propose a stacked hourglass network with multi-level attention mechanism to jointly learn intervertebral disc position and their skeleton structure. The proposed deep learning model takes into account the strength of semantic segmentation and pose estimation technique to handle the missing area and false positive detection. To further improve the performance of the proposed method, we propose a skeleton-based search space to reduce false positive detection. The proposed method evaluated on spine generic public multi-center dataset and demonstrated better performance comparing to previous work, on both T1w and T2w contrasts. The method is implemented in ivadomed (https://ivadomed.org ). © 2021, Springer Nature Switzerland AG
  6. Keywords:
  7. Diagnosis ; Magnetic resonance imaging ; Medical computing ; Medical imaging ; Neurodegenerative diseases ; Semantics ; Attention mechanisms ; Deep learning ; False positive detection ; Generic database ; Intervertebral disk ; Intervertebral disk labeling ; Labelings ; Multilevels ; Pose-estimation ; Spine generic database ; Musculoskeletal system
  8. Source: 12th International Workshop on Machine Learning in Medical Imaging, MLMI 2021, held in conjunction with 24th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2021, 27 September 2021 through 27 September 2021 ; Volume 12966 LNCS , 2021 , Pages 406-415 ; 03029743 (ISSN); 9783030875886 (ISBN)
  9. URL: https://www.springerprofessional.de/en/stacked-hourglass-network-with-a-multi-level-attention-mechanism/19698554