| Full text | |
| Author(s): |
Ramos, Jonathan S.
;
Cazzolato, Mirela T.
;
Linares, Oscar C.
;
Maciel, Jamilly G.
;
Menezes-Reis, Rafael
;
Azevedo-Marques, Paulo M.
;
Nogueira-Barbosa, Marcello H.
;
Traina Junior, Caetano
;
Traina, Agma J. M.
Total Authors: 9
|
| Document type: | Journal article |
| Source: | MAGNETIC RESONANCE IMAGING; v. 109, p. 13-pg., 2024-03-22. |
| Abstract | |
Accurate and efficient segmenting of vertebral bodies, muscles, and discs is crucial for analyzing various spinal diseases. However, traditional methods are either laborious and time-consuming (manual segmentation) or require extensive training data (fully automatic segmentation). FastCleverSeg, our proposed semi-automatic segmentation approach, addresses those limitations by significantly reducing user interaction while maintaining high accuracy. First, we reduce user interaction by requiring the manual annotation of only two or three slices. Next, we automatically Estimate the Annotation on Intermediary Slices (EANIS) using traditional computer vision/graphics concepts. Finally, our proposed method leverages improved voxel weight balancing to achieve fast and precise volumetric segmentation in the segmentation process. Experimental evaluations on our assembled diverse MRI databases comprising 179 patients (60 male, 119 female), demonstrate a remarkable 25 ms (30 ms standard deviation) processing time and a significant reduction in user interaction compared to existing approaches. Importantly, FastCleverSeg maintains or surpasses the segmentation quality of competing methods, achieving a Dice score of 94%. This invaluable tool empowers physicians to efficiently generate reliable ground truths, expediting the segmentation process and paving the way for future integration with deep learning approaches. In turn, this opens exciting possibilities for future fully automated spine segmentation. (AU) | |
| FAPESP's process: | 16/17078-0 - Mining, indexing and visualizing Big Data in clinical decision support systems (MIVisBD) |
| Grantee: | Agma Juci Machado Traina |
| Support Opportunities: | Research Projects - Thematic Grants |
| FAPESP's process: | 18/06228-7 - Detection of patterns and anomalies in medical data using Mathematical Modeling |
| Grantee: | Bruno Squizato Faiçal |
| Support Opportunities: | Scholarships in Brazil - Post-Doctoral |
| FAPESP's process: | 17/23780-2 - Content-based retrieval of medical images to aid the clinical decision using radiomics |
| Grantee: | Jonathan da Silva Ramos |
| Support Opportunities: | Scholarships in Brazil - Doctorate |
| FAPESP's process: | 18/24414-2 - A framework for integration of feature extraction techniques and complex databases for MIVisBD |
| Grantee: | Mirela Teixeira Cazzolato |
| Support Opportunities: | Scholarships in Brazil - Technical Training Program - Technical Training |