Assessment of fully-automated atlas-based segmentation of novel oral mucosal surface organ-at-risk

Dean, Jamie; Welsh, Liam; McQuaid, D.; Wong, Kee H.; Aleksić, Aleksandar; Dunne, Emma; Islam, Mohammad Rafiqul; Patel, A.; Patel, Priyanka; Petkar, Imran; Phillips, I.; Sham, J.; Newbold, Kate; Bhide, Shreerang A.; Harrington, Kevin; Gulliford, S.; Nutting, Christopher M.

doi:10.1016/j.radonc.2016.02.022

Cited by 16 publications

(16 citation statements)

References 28 publications

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“…For the quantitative evaluation, indexes suggested by TG-132 AAPM (that reviews current approaches and solutions for image registration, both rigid and deformable, in radiotherapy provides recommendations for quality assurance and quality control of these clinical processes) and other suggestions in the literature were used to validate automatic contours compared with the manual contours (gold standard) [15,[20][21][22].…”

Section: Qualitative and Quantitative Validation Of Auto-contouring Rmentioning

confidence: 99%

Hierarchical clustering applied to automatic atlas based segmentation of 25 cardiac sub-structures

Maffei

Fiorini

Aluisio³

et al. 2020

Physica Medica

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Section: Qualitative and Quantitative Validation Of Auto-contouring Rmentioning

confidence: 99%

Hierarchical clustering applied to automatic atlas based segmentation of 25 cardiac sub-structures

Maffei

Fiorini

Aluisio³

et al. 2020

Physica Medica

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“…The development of computational tools to automatically generate OAR contours can reduce the time and effort required for HNC contouring and plan development, as well as inter-observer contour variations. Specifically, organ auto-segmentation has been extensively studied [ 7 – 10 ] using both CT and MR image datasets [ 11 , 12 ]. One approach, atlas-based auto-segmentation (ABAS) [ 13 , 14 ], is a traditional method for organ contouring and various factors can affect segmentation performance.…”

Section: Introductionmentioning

confidence: 99%

Deep learning vs. atlas-based models for fast auto-segmentation of the masticatory muscles on head and neck CT images

Chen

Dyer

et al. 2020

Radiat Oncol

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Background: Impaired function of masticatory muscles will lead to trismus. Routine delineation of these muscles during planning may improve dose tracking and facilitate dose reduction resulting in decreased radiation-related trismus. This study aimed to compare a deep learning model with a commercial atlas-based model for fast autosegmentation of the masticatory muscles on head and neck computed tomography (CT) images. Material and methods: Paired masseter (M), temporalis (T), medial and lateral pterygoid (MP, LP) muscles were manually segmented on 56 CT images. CT images were randomly divided into training (n = 27) and validation (n = 29) cohorts. Two methods were used for automatic delineation of masticatory muscles (MMs): Deep learning autosegmentation (DLAS) and atlas-based auto-segmentation (ABAS). The automatic algorithms were evaluated using Dice similarity coefficient (DSC), recall, precision, Hausdorff distance (HD), HD95, and mean surface distance (MSD). A consolidated score was calculated by normalizing the metrics against interobserver variability and averaging over all patients. Differences in dose (ΔDose) to MMs for DLAS and ABAS segmentations were assessed. A paired t-test was used to compare the geometric and dosimetric difference between DLAS and ABAS methods. Results: DLAS outperformed ABAS in delineating all MMs (p < 0.05). The DLAS mean DSC for M, T, MP, and LP ranged from 0.83 ± 0.03 to 0.89 ± 0.02, the ABAS mean DSC ranged from 0.79 ± 0.05 to 0.85 ± 0.04. The mean value for recall, HD, HD95, MSD also improved with DLAS for auto-segmentation. Interobserver variation revealed the highest variability in DSC and MSD for both T and MP, and the highest scores were achieved for T by both automatic algorithms. With few exceptions, the mean ΔD98%, ΔD95%, ΔD50%, and ΔD2% for all structures were below 10% for DLAS and ABAS and had no detectable statistical difference (P > 0.05). DLAS based contours had dose endpoints more closely matched with that of the manually segmented when compared with ABAS. Conclusions: DLAS auto-segmentation of masticatory muscles for the head and neck radiotherapy had improved segmentation accuracy compared with ABAS with no qualitative difference in dosimetric endpoints compared to manually segmented contours.

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“…The Dean et al model was based on delineation standard that does not accurately represent the mucosal surface and may explain the lack of improvement with spatial dosimetric data. Dean et al recently created and validated a novel automated contouring technique for oral mucosa that would be more reproducible and representative of the mucosal surface (67, 68). Data on tobacco and alcohol consumption were not available for patients in two out of six trials.…”

Section: Normal Tissue Complication Probability Assessmentmentioning

confidence: 99%

Radiomics and Machine Learning for Radiotherapy in Head and Neck Cancers

Giraud

Gasnier

et al. 2019

Front. Oncol.

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Introduction: An increasing number of parameters can be considered when making decisions in oncology. Tumor characteristics can also be extracted from imaging through the use of radiomics and add to this wealth of clinical data. Machine learning can encompass these parameters and thus enhance clinical decision as well as radiotherapy workflow. Methods: We performed a description of machine learning applications at each step of treatment by radiotherapy in head and neck cancers. We then performed a systematic review on radiomics and machine learning outcome prediction models in head and neck cancers. Results: Machine Learning has several promising applications in treatment planning with automatic organ at risk delineation improvements and adaptative radiotherapy workflow automation. It may also provide new approaches for Normal Tissue Complication Probability models. Radiomics may provide additional data on tumors for improved machine learning powered predictive models, not only on survival, but also on risk of distant metastasis, in field recurrence, HPV status and extra nodal spread. However, most studies provide preliminary data requiring further validation. Conclusion: Promising perspectives arise from machine learning applications and radiomics based models, yet further data are necessary for their implementation in daily care.

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Assessment of fully-automated atlas-based segmentation of novel oral mucosal surface organ-at-risk

Abstract: Fully-automated ABS of MSC is suitable for use in radiotherapy dose-response modelling.

Cited by 16 publications

References 28 publications

Hierarchical clustering applied to automatic atlas based segmentation of 25 cardiac sub-structures

Hierarchical clustering applied to automatic atlas based segmentation of 25 cardiac sub-structures

Deep learning vs. atlas-based models for fast auto-segmentation of the masticatory muscles on head and neck CT images

Radiomics and Machine Learning for Radiotherapy in Head and Neck Cancers

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