Statistische 3D-Formmodelle mit verteilter Erscheinungsmodellierung

Norajitra, Tobias; Engelhardt, Sandy; Held, T.; Al-Maisary, Sameer; Simone, Raffaele De; Meinzer, Hans-Peter; Maier‐Hein, Klaus H.

doi:10.1007/978-3-662-49465-3_12

Cited by 1 publication

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“…Automated segmentation and volumetry of vertebral bodies L3-L5 and dural sac segments L3-S1 was performed applying a previously published shape-based machine learning algorithm [ 40 , 65 ]. This shape-based machine learning algorithm represents an extension of 3D statistical shape models.…”

Section: Methodsmentioning

confidence: 99%

“…Automated segmentation using 3D statistical shape models has been shown to be robust and accurate [ 39 ]. The process involves placing a surface model in the image, followed by an iterative search for landmarks using local image information [ 40 ]. Despite the development of various automated segmentation methods, including heuristically customized appearance models and automatic selection of optimal local features [ 41 ], these methods are limited by their reliance on local image information and the use of heuristic or weak learning methods [ 40 , 42 ].…”

Section: Introductionmentioning

confidence: 99%

“…The process involves placing a surface model in the image, followed by an iterative search for landmarks using local image information [40]. Despite the development of various automated segmentation methods, including heuristically customized appearance models and automatic selection of optimal local features [41], these methods are limited by their reliance on local image information and the use of heuristic or weak learning methods [40,42]. However, models utilizing randomized regression forests have emerged, offering robust learning that consider non-local image information and omni-directional landmark detection, thereby enhancing segmentation accuracy and robustness across various organs without the need for prior model initialization [40,[43][44][45].…”

Section: Introductionmentioning

confidence: 99%

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MRI-Derived Dural Sac and Lumbar Vertebrae 3D Volumetry Has Potential for Detection of Marfan Syndrome

Naas,

Norajitra,

Lückerath

et al. 2024

Diagnostics

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Purpose: To assess the feasibility and diagnostic accuracy of MRI-derived 3D volumetry of lower lumbar vertebrae and dural sac segments using shape-based machine learning for the detection of Marfan syndrome (MFS) compared with dural sac diameter ratios (the current clinical standard). Materials and methods: The final study sample was 144 patients being evaluated for MFS from 01/2012 to 12/2016, of whom 81 were non-MFS patients (46 [67%] female, 36 ± 16 years) and 63 were MFS patients (36 [57%] female, 35 ± 11 years) according to the 2010 Revised Ghent Nosology. All patients underwent 1.5T MRI with isotropic 1 × 1 × 1 mm3 3D T2-weighted acquisition of the lumbosacral spine. Segmentation and quantification of vertebral bodies L3-L5 and dural sac segments L3-S1 were performed using a shape-based machine learning algorithm. For comparison with the current clinical standard, anteroposterior diameters of vertebral bodies and dural sac were measured. Ratios between dural sac volume/diameter at the respective level and vertebral body volume/diameter were calculated. Results: Three-dimensional volumetry revealed larger dural sac volumes (p < 0.001) and volume ratios (p < 0.001) at L3-S1 levels in MFS patients compared with non-MFS patients. For the detection of MFS, 3D volumetry achieved higher AUCs at L3-S1 levels (0.743, 0.752, 0.808, and 0.824) compared with dural sac diameter ratios (0.673, 0.707, 0.791, and 0.848); a significant difference was observed only for L3 (p < 0.001). Conclusion: MRI-derived 3D volumetry of the lumbosacral dural sac and vertebral bodies is a feasible method for quantifying dural ectasia using shape-based machine learning. Non-inferior diagnostic accuracy was observed compared with dural sac diameter ratio (the current clinical standard for MFS detection).

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Section: Methodsmentioning

confidence: 99%