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Ramos JS, Cazzolato MT, Linares OC, Maciel JG, Menezes-Reis R, Azevedo-Marques PM, Nogueira-Barbosa MH, Traina Júnior C, Traina AJM. Fast and accurate 3-D spine MRI segmentation using FastCleverSeg. Magn Reson Imaging 2024; 109:134-146. [PMID: 38508290 DOI: 10.1016/j.mri.2024.03.021] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2024] [Revised: 03/13/2024] [Accepted: 03/16/2024] [Indexed: 03/22/2024]
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.
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Affiliation(s)
- Jonathan S Ramos
- Computer Science Department, Federal University of Rondônia (DACC/UNIR), 364 BR, 76801-059, Rondônia, Brazil; Institute of Mathematics and Computer Sciences, University of Sao Paulo (ICMC/USP), 400 Trabalhador Saocarlense Avenue, 13566-590 São Carlos, São Paulo, Brazil.
| | - Mirela T Cazzolato
- Institute of Mathematics and Computer Sciences, University of Sao Paulo (ICMC/USP), 400 Trabalhador Saocarlense Avenue, 13566-590 São Carlos, São Paulo, Brazil
| | - Oscar C Linares
- Institute of Mathematics and Computer Sciences, University of Sao Paulo (ICMC/USP), 400 Trabalhador Saocarlense Avenue, 13566-590 São Carlos, São Paulo, Brazil
| | - Jamilly G Maciel
- Ribeirao Preto Medical School, University of Sao Paulo (FMRP/USP), 3900 Bandeirantes Avenue, 695014 Ribeirão Preto, São Paulo, Brazil
| | - Rafael Menezes-Reis
- Ribeirao Preto Medical School, University of Sao Paulo (FMRP/USP), 3900 Bandeirantes Avenue, 695014 Ribeirão Preto, São Paulo, Brazil
| | - Paulo M Azevedo-Marques
- Ribeirao Preto Medical School, University of Sao Paulo (FMRP/USP), 3900 Bandeirantes Avenue, 695014 Ribeirão Preto, São Paulo, Brazil
| | - Marcello H Nogueira-Barbosa
- Ribeirao Preto Medical School, University of Sao Paulo (FMRP/USP), 3900 Bandeirantes Avenue, 695014 Ribeirão Preto, São Paulo, Brazil
| | - Caetano Traina Júnior
- Institute of Mathematics and Computer Sciences, University of Sao Paulo (ICMC/USP), 400 Trabalhador Saocarlense Avenue, 13566-590 São Carlos, São Paulo, Brazil
| | - Agma J M Traina
- Institute of Mathematics and Computer Sciences, University of Sao Paulo (ICMC/USP), 400 Trabalhador Saocarlense Avenue, 13566-590 São Carlos, São Paulo, Brazil
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