Correction: Novel stochastic framework for automatic segmentation of human thigh MRI volumes and its applications in spinal cord injured individuals

Mesbah, Samineh; Shalaby, Ahmed; Stills, Sean; Soliman, Ahmed; Willhite, Andrea; Harkema, Susan J.; Rejc, Enrico; El-Baz, Ayman

doi:10.1371/journal.pone.0219810

Cited by 6 publications

(1 citation statement)

References 2 publications

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“…Manual segmentation of MR images is a laborious and timeconsuming process, which therefore limits the total number of patients that can be assessed within reasonable time constraints [81] . In the last two decades many semi-automated and automated methods of segmentation have been developed, most of which are specifically used for brain imaging analysis [82][83][84][85] .…”

Section: Discussionmentioning

confidence: 99%

Masseter muscle volume as a disease marker in adult-onset myotonic dystrophy type 1

Oliwa

Hocking²,

Hamilton³

et al. 2022

Neuromuscular Disorders

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Section: Discussionmentioning

confidence: 99%

Masseter muscle volume as a disease marker in adult-onset myotonic dystrophy type 1

Oliwa

Hocking²,

Hamilton³

et al. 2022

Neuromuscular Disorders

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Automatic quadriceps and patellae segmentation of MRI with cascaded U²‐Net and SASSNet deep learning model

et al. 2021

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Purpose Automatic muscle segmentation is critical for advancing our understanding of human physiology, biomechanics, and musculoskeletal pathologies, as it allows for timely exploration of large multi‐dimensional image sets. Segmentation models are rarely developed/validated for the pediatric model. As such, autosegmentation is not available to explore how muscle architectural changes during development and how disease/pathology affects the developing musculoskeletal system. Thus, we aimed to develop and validate an end‐to‐end, fully automated, deep learning model for accurate segmentation of the rectus femoris and vastus lateral, medialis, and intermedialis using a pediatric database. Methods We developed a two‐stage cascaded deep learning model in a coarse‐to‐fine manner. In the first stage, the U2‐Net roughly detects the muscle subcompartment region. Then, in the second stage, the shape‐aware 3D semantic segmentation method SASSNet refines the cropped target regions to generate the more finer and accurate segmentation masks. We utilized multifeature image maps in both stages to stabilize performance and validated their use with an ablation study. The second‐stage SASSNet was independently run and evaluated with three different cropped region resolutions: the original image resolution, and images downsampled 2× and 4× (high, mid, and low). The relationship between image resolution and segmentation accuracy was explored. In addition, the patella was included as a comparator to past work. We evaluated segmentation accuracy using leave‐one‐out testing on a database of 3D MR images (0.43 × 0.43 × 2 mm) from 40 pediatric participants (age 15.3 ± 1.9 years, 55.8 ± 11.8 kg, 164.2 ± 7.9 cm, 38F/2 M). Results The mid‐resolution second stage produced the best results for the vastus medialis, rectus femoris, and patella (Dice similarity coefficient = 95.0%, 95.1%, 93.7%), whereas the low‐resolution second stage produced the best results for the vastus lateralis and vastus intermedialis (DSC = 94.5% and 93.7%). In comparing the low‐ to mid‐resolution cases, the vasti intermedialis, vastus medialis, rectus femoris, and patella produced significant differences (p = 0.0015, p = 0.0101, p < 0.0001, p = 0.0003) and the vasti lateralis did not (p = 0.2177). The high‐resolution stage 2 had significantly lower accuracy (1.0 to 4.4 dice percentage points) compared to both the mid‐ and low‐resolution routines (p value ranged from < 0.001 to 0.04). The one exception was the rectus femoris, where there was no difference between the low‐ and high‐resolution cases. The ablation study demonstrated that the multifeature is more reliable than the single feature. Conclusions Our successful implementation of this two‐stage segmentation pipeline provides a critical tool for expanding pediatric muscle physiology and clinical research. With a relatively small and variable dataset, our fully automatic segmentation technique produces accuracies that matched or exceeded the current state of the art. The two‐stage segmentation avoids memor...

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Evaluation of U-Net CNN Approaches for Human Neck MRI Segmentation

Suman

Khemchandani

Asikuzzaman

et al. 2020

2020 Digital Image Computing: Techniques and Applications (DICTA)

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Correction: Novel stochastic framework for automatic segmentation of human thigh MRI volumes and its applications in spinal cord injured individuals

Cited by 6 publications

References 2 publications

Masseter muscle volume as a disease marker in adult-onset myotonic dystrophy type 1

Masseter muscle volume as a disease marker in adult-onset myotonic dystrophy type 1

Automatic quadriceps and patellae segmentation of MRI with cascaded U²‐Net and SASSNet deep learning model

Evaluation of U-Net CNN Approaches for Human Neck MRI Segmentation

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Correction: Novel stochastic framework for automatic segmentation of human thigh MRI volumes and its applications in spinal cord injured individuals

Cited by 6 publications

References 2 publications

Masseter muscle volume as a disease marker in adult-onset myotonic dystrophy type 1

Masseter muscle volume as a disease marker in adult-onset myotonic dystrophy type 1

Automatic quadriceps and patellae segmentation of MRI with cascaded U2‐Net and SASSNet deep learning model

Evaluation of U-Net CNN Approaches for Human Neck MRI Segmentation

Contact Info

Product

Resources

About

Automatic quadriceps and patellae segmentation of MRI with cascaded U²‐Net and SASSNet deep learning model