Manual segmentation versus semi-automated segmentation for quantifying vestibular schwannoma volume on MRI

McGrath, Hari; Li, Peichao; Dorent, Reuben; Bradford, Robert; Saeed, Shakeel R.; Bisdas, Sotirios; Ourselin, Sébastien; Shapey, Jonathan; Vercauteren, Tom

doi:10.1007/s11548-020-02222-y

Cited by 40 publications

(28 citation statements)

References 47 publications

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“…Eight articles were not original reports but reviews or editorials [ 88 , 89 , 90 , 91 , 92 , 93 , 94 , 95 ]. Three articles used semi-automatic segmentation techniques [ 96 , 97 , 98 ]. Three articles dealt with the application of ML techniques to brain tumors in dogs [ 99 , 100 , 101 ].…”

Section: Resultsmentioning

confidence: 99%

Machine Learning for the Detection and Segmentation of Benign Tumors of the Central Nervous System: A Systematic Review

Windisch

Koechli

Rogers

et al. 2022

Cancers

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Objectives: To summarize the available literature on using machine learning (ML) for the detection and segmentation of benign tumors of the central nervous system (CNS) and to assess the adherence of published ML/diagnostic accuracy studies to best practice. Methods: The MEDLINE database was searched for the use of ML in patients with any benign tumor of the CNS, and the records were screened according to PRISMA guidelines. Results: Eleven retrospective studies focusing on meningioma (n = 4), vestibular schwannoma (n = 4), pituitary adenoma (n = 2) and spinal schwannoma (n = 1) were included. The majority of studies attempted segmentation. Links to repositories containing code were provided in two manuscripts, and no manuscripts shared imaging data. Only one study used an external test set, which raises the question as to whether some of the good performances that have been reported were caused by overfitting and may not generalize to data from other institutions. Conclusion: Using ML for detecting and segmenting benign brain tumors is still in its infancy. Stronger adherence to ML best practices could facilitate easier comparisons between studies and contribute to the development of models that are more likely to one day be used in clinical practice.

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Section: Resultsmentioning

confidence: 99%

Machine Learning for the Detection and Segmentation of Benign Tumors of the Central Nervous System: A Systematic Review

Windisch

Koechli

Rogers

et al. 2022

Cancers

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“…One of the major limitations of our study is the time required for the volumetric analysis of ITH. The semi-automated segmentation software considerably reduces segmentation time and improves accuracy compared to manual segmentation ( McGrath et al, 2020 ). However, the segmentation procedure still takes considerable amount of time.…”

Section: Discussionmentioning

confidence: 99%

Analysis of inferior nasal turbinate volume in subjects with nasal septum deviation: a retrospective cone beam tomography study

Shetty

Al-Bayatti

Alam

et al. 2022

PeerJ

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Background The association of the linear dimensions of the inferior turbinate hypertrophy with nasal septal deviation has been studied recently. However, the volumetric dimensions provide a more accurate status of the turbinate hypertrophy compared to linear measurements. The aim of this study was to analyze the association of inferior nasal turbinate volume with the degree of nasal septal deviation (NSD). Methods A retrospective evaluation of the cone beam computed tomography (CBCT) scans of 412 patients was carried out to obtain 150 scans which were included in the study. The scans were categorized into three groups. Group 1 comprised of 50 scans of patients with no inferior turbinate hypertrophy (ITH) and no nasal septal deviation. Group 2 comprised of 50 scans of patients with ITH and no NSD; whereas Group 3 included 50 scans of patients with ITH and NSD. The total turbinate volume of inferior turbinates (bilateral) were determined by using Vesalius 3D software (PS-Medtech, Amsterdam, Netherlands). Results The intraclass correlation coefficient (ICC) between the volumetric estimations performed by the two radiologists was 0.82. There were no significant age and gender related changes in the total turbinate volume. Patients in Group 3 had significantly higher (p = 0.001) total turbinate volume compared to Group 2 and Group 1. There was a positive and significant correlation (r = 0.52, p = 0.002) between the degree of septal deviation and total turbinate volume. When the total turbinate volume of the patients with different types of septal deviation was compared in Group 3, a statistically significant difference (p = 0.001) was observed. Regression analysis revealed that the septal deviation angle (SDA) (p = 0.001) had a relationship with total turbinate volume. From the results of the study we can conclude that the total turbinate volume is higher in patients with nasal septal deviation. It can also be concluded that the septal deviation angle has a positive correlation with total turbinate volume. The data obtained from the study can be useful in post-surgical follow up and evaluation of patients with nasal septal deviation and hypertrophied inferior nasal turbinate.

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“…This has been used before in the context of new medical technology. 20 - 23 It scores different task aspects on a graded scale from 0 to 20, including effort, frustration, and performance ( Supplemental Table S2 , available online).…”

Section: Methodsmentioning

confidence: 99%

Super‐resolution Reconstruction MRI Application in Fetal Neck Masses and Congenital High Airway Obstruction Syndrome

et al. 2021

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Objective Reliable airway patency diagnosis in fetal tracheolaryngeal obstruction is crucial to select and plan ex utero intrapartum treatment (EXIT) surgery. We compared the clinical utility of magnetic resonance imaging (MRI) super-resolution reconstruction (SRR) of the trachea, which can mitigate unpredictable fetal motion effects, with standard 2-dimensional (2D) MRI for airway patency diagnosis and assessment of fetal neck mass anatomy. Study Design A single-center case series of 7 consecutive singleton pregnancies with complex upper airway obstruction (2013-2019). Setting A tertiary fetal medicine unit performing EXIT surgery. Methods MRI SRR of the trachea was performed involving rigid motion correction of acquired 2D MRI slices combined with robust outlier detection to reconstruct an isotropic high-resolution volume. SRR, 2D MRI, and paired data were blindly assessed by 3 radiologists in 3 experimental rounds. Results Airway patency was correctly diagnosed in 4 of 7 cases (57%) with 2D MRI as compared with 2 of 7 cases (29%) with SRR alone or paired 2D MRI and SRR. Radiologists were more confident ( P = .026) in airway patency diagnosis when using 2D MRI than SRR. Anatomic clarity was higher with SRR ( P = .027) or paired data ( P = .041) in comparison with 2D MRI alone. Radiologists detected further anatomic details by using paired images versus 2D MRI alone ( P < .001). Cognitive load, as assessed by the NASA Task Load Index, was increased with paired or SRR data in comparison with 2D MRI. Conclusion The addition of SRR to 2D MRI does not increase fetal airway patency diagnostic accuracy but does provide improved anatomic information, which may benefit surgical planning of EXIT procedures.

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Manual segmentation versus semi-automated segmentation for quantifying vestibular schwannoma volume on MRI

Cited by 40 publications

References 47 publications

Machine Learning for the Detection and Segmentation of Benign Tumors of the Central Nervous System: A Systematic Review

Machine Learning for the Detection and Segmentation of Benign Tumors of the Central Nervous System: A Systematic Review

Analysis of inferior nasal turbinate volume in subjects with nasal septum deviation: a retrospective cone beam tomography study

Super‐resolution Reconstruction MRI Application in Fetal Neck Masses and Congenital High Airway Obstruction Syndrome

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