Joint Paraspinal Muscle Segmentation and Inter-rater Labeling Variability Prediction with Multi-task TransUNet

Parinaz, Roshanzamir,; Rivaz, Hassan; Ahn, Joshua; Mirza, Hamza; Naghdi, Neda; Anstruther, Meagan; Battié, Michele C.; Fortin, Maryse; Xiao, Yiming

doi:10.1007/978-3-031-16749-2_12

Cited by 4 publications

(2 citation statements)

References 14 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Not only will consistency in segmentation protocols aid in comparing studies and clinical populations, but researchers utilizing the same software to perform segmentations will also allow for easier comparisons between studies. With more researchers using the same segmentation software and protocols, a database can be built to eventually lead towards automated segmentation [23,24]. Compared to manual segmentation, automated segmentation is faster, easier, and may allow for easier access to results, especially if it can be integrated to clinical settings.…”

Section: Future Directionsmentioning

confidence: 99%

PILLAR: ParaspInaL muscLe segmentAtion pRoject - a comprehensive online resource to guide manual segmentation of paraspinal muscles from Magnetic Resonance Imaging

Anstruther¹,

Rossini²,

Zhang³

et al. 2023

Preprint

Self Cite

View full text Add to dashboard Cite

Background – There is an increasing interest in assessing paraspinal morphology and composition in relation to low back pain (LBP). However, variations in methods and segmentation protocols contribute to the inconsistent findings in the literature. We present an on-line resource, the ParaspInaL muscLe segmentAtion pRoject (PILLAR , https://projectpillar.github.io/), to provide a detailed description and visual guide of a segmentation protocol by using the publicly available ITK-SNAP software and discuss related challenges when performing paraspinal lumbar muscles segmentations from magnetic resonance imaging (MRI). Methods – T2-weighted and corresponding fat-water IDEAL axial MRI from 3 males and 3 females (2 chronic LBP and 1 control for each sex) were used to demonstrate our segmentation protocol for each lumbar paraspinal muscle (erector spinae, lumbar multifidus, quadratus lumborum and psoas) and lumbar spinal level (L1-L5). Results – Proper segmentation requires an understanding of the anatomy of paraspinal lumbar muscles and the variations in paraspinal muscle morphology and composition due to age, sex, and the presence of LBP or related spinal pathologies. Other challenges in segmentation includes the presence and variations of intramuscular and epimuscular fat, and side-to-side asymmetry. Conclusion – The growing interest to assess the lumbar musculature and its role in the development and recurrence of LBP prompted the need for comprehensive and easy-to-follow resources, such as the PILLAR project to reduce inconsistencies in segmentation protocols. Standardizing manual muscle measurements from MRI will facilitate comparisons between studies while the field is progressively moving towards the automatization of paraspinal muscle measurements for large cohort studies.

show abstract

Section: Future Directionsmentioning

confidence: 99%

PILLAR: ParaspInaL muscLe segmentAtion pRoject - a comprehensive online resource to guide manual segmentation of paraspinal muscles from Magnetic Resonance Imaging

Anstruther¹,

Rossini²,

Zhang³

et al. 2023

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

“…1 However, unlike brain MRIs, for which morphometric analyses have been widely employed to study related diseases and natural aging, intersubject affine registration using conventional similarity metrics (e.g., cross-correlation) for paraspinal muscle MRIs is more challenging due to highly complex image content, and high anatomical variability. 1,2 Thus, Landmark-based registration is helpful to tackle this issue. 3,4 However, the more common approach, manual identification of anatomical landmarks, is still a highly time-consuming task and can be subject to inter-and intra-rater inconsistency.…”

Section: Introductionmentioning

confidence: 99%

Uncertainty-aware transformer model for anatomical landmark detection in paraspinal muscle MRIs

Salari¹,

Rasoulian²,

Battié³

et al. 2023

Medical Imaging 2023: Image Processing

Self Cite

View full text Add to dashboard Cite

Anatomical landmark identification is crucial in the registration of medical images in a wide range of clinical applications. Various machine and deep learning (DL) techniques have been proposed to annotate anatomical landmarks automatically. However, very few have taken advantage of the more recent Transformer models that do not suffer from inductive bias of convolutional neural networks as well as incorporating uncertainty assessments. This paper proposes a novel technique based on the Swin Transformer V2 (Swin-V2) with an uncertainty quantification module for robust anatomical landmark detection in paraspinal muscle MRIs to facilitate muscle morphometric analysis for low back pain (LBP). Specifically, we employ statistical measurements from stochastic sampling using Monte Carlo (MC) dropouts as uncertainty metrics for automatic landmark detection. The proposed method was trained and validated using 350 axial MRI slices of paraspinal muscles at the mid-disc spinal levels commonly associated with LBP. We demonstrate that the selected uncertainty metrics are correlated with automatic landmark detection errors and, in addition, can be used as inputs to grade the quality of the identified landmarks with a lightweight random forest algorithm for a more straightforward interpretation.

show abstract

How Inter-rater Variability Relates to Aleatoric and Epistemic Uncertainty: A Case Study with Deep Learning-Based Paraspinal Muscle Segmentation

Roshanzamir,

Rivaz,

Ahn

et al. 2023

Uncertainty for Safe Utilization of Machine Learning in Medical Imaging

View full text Add to dashboard Cite

Joint Paraspinal Muscle Segmentation and Inter-rater Labeling Variability Prediction with Multi-task TransUNet

Cited by 4 publications

References 14 publications

PILLAR: ParaspInaL muscLe segmentAtion pRoject - a comprehensive online resource to guide manual segmentation of paraspinal muscles from Magnetic Resonance Imaging

PILLAR: ParaspInaL muscLe segmentAtion pRoject - a comprehensive online resource to guide manual segmentation of paraspinal muscles from Magnetic Resonance Imaging

Uncertainty-aware transformer model for anatomical landmark detection in paraspinal muscle MRIs

How Inter-rater Variability Relates to Aleatoric and Epistemic Uncertainty: A Case Study with Deep Learning-Based Paraspinal Muscle Segmentation

Contact Info

Product

Resources

About