Reference standard for the evaluation of automatic segmentation algorithms: Quantification of inter observer variability of manual delineation of prostate contour on MRI

Molière, Sébastien; Hamzaoui, Dimitri; Granger, Benjamin; Montagne, Sarah; Allera, Alexandre; Ezziane, Malek; Luzurier, Anna; Quint, Raphaelle; Kalai, Mehdi; Ayache, Nicholas; Delingette, Hervé; Renard-Penna, Raphaële

doi:10.1016/j.diii.2023.08.001

Diagnostic and Interventional Imaging

2024

DOI: 10.1016/j.diii.2023.08.001

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Reference standard for the evaluation of automatic segmentation algorithms: Quantification of inter observer variability of manual delineation of prostate contour on MRI

Sébastien Molière,

Dimitri Hamzaoui,

Benjamin Granger

et al.

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Neural network segmentation of disc volume from magnetic resonance images and the effect of degeneration and spinal level

Markhali,

Peloquin,

Meadows

et al. 2024

JOR Spine

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BackgroundMagnetic resonance imaging (MRI) noninvasively quantifies disc structure but requires segmentation that is both time intensive and susceptible to human error. Recent advances in neural networks can improve on manual segmentation. The aim of this study was to establish a method for automatic slice‐wise segmentation of 3D disc volumes from subjects with a wide range of age and degrees of disc degeneration. A U‐Net convolutional neural network was trained to segment 3D T1‐weighted spine MRI.MethodsLumbar spine MRIs were acquired from 43 subjects (23–83 years old) and manually segmented. A U‐Net architecture was trained using the TensorFlow framework. Two rounds of model tuning were performed. The performance of the model was measured using a validation set that did not cross over from the training set. The model version with the best Dice similarity coefficient (DSC) was selected in each tuning round. After model development was complete and a final U‐Net model was selected, performance of this model was compared between disc levels and degeneration grades.ResultsPerformance of the final model was equivalent to manual segmentation, with a mean DSC = 0.935 ± 0.014 for degeneration grades I–IV. Neither the manual segmentation nor the U‐Net model performed as well for grade V disc segmentation. Compared with the baseline model at the beginning of round 1, the best model had fewer filters/parameters (75%), was trained using only slices with at least one disc‐labeled pixel, applied contrast stretching to its input images, and used a greater dropout rate.ConclusionThis study successfully trained a U‐Net model for automatic slice‐wise segmentation of 3D disc volumes from populations with a wide range of ages and disc degeneration. The final trained model is available to support scientific use.

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Neural network segmentation of disc volume from magnetic resonance images and the effect of degeneration and spinal level

Markhali,

Peloquin,

Meadows

et al. 2024

JOR Spine

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show abstract

Evaluation of automated prostate segmentation: The complex issue of the optimal number of expert segmentations

Rouvière

2024

Diagnostic and Interventional Imaging

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Achieving accurate prostate auto-segmentation on CT in the absence of MR imaging

Duan,

Tegtmeier,

Vargas

et al. 2025

Radiotherapy and Oncology

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Reference standard for the evaluation of automatic segmentation algorithms: Quantification of inter observer variability of manual delineation of prostate contour on MRI

Cited by 5 publications

References 50 publications

Neural network segmentation of disc volume from magnetic resonance images and the effect of degeneration and spinal level

Neural network segmentation of disc volume from magnetic resonance images and the effect of degeneration and spinal level

Evaluation of automated prostate segmentation: The complex issue of the optimal number of expert segmentations

Achieving accurate prostate auto-segmentation on CT in the absence of MR imaging

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