Use of a novel atlas for muscles of mastication to reduce inter observer variability in head and neck radiotherapy contouring

Dyer

et al. 2020

Radiat Oncol

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.

Section: Introductionmentioning

confidence: 99%

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

Dyer

et al. 2020

Radiat Oncol

“…Several studies [7][8][9]17,18,36 previously evaluated the performance of different methods of autosegmentation for head and neck radiotherapy. Hague et al 18 developed a new contouring atlas to evaluate the reduction in interobserver variability for MP, LP, M, and T muscles. The authors found that an atlas reduced interobserver variability for all muscles and the mean DTA improved when the trainees used the atlas.…”

Section: Discussionmentioning

confidence: 99%

“…In recent years, deep learning-based methods 15,16 have shown great success for biomedical image segmentation and have been introduced to the eld of head and neck anatomy segmentation. However, the literature is limited in assessing masticatory muscles (MMs) autosegmentation 17,18 , which may be due to the lack of delineation guidelines for MMs.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

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

Dyer

et al. 2020

Preprint

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 auto-segmentation 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 auto-segmentation (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 (∆Dose) 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 ∆D98%, ∆D95%, ∆D50%, and ∆D2% 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.

“…Hague et al15 also found that auto-segmentation technique can achieve low COM values for temporalis with 0.45 AE 0.30 cm for X axis, 0.34 AE 0.28 cm for Y axis, and 0.21 AE 0.14 cm for Z axis.Previous studies have shown21,22 that contouring uncertainty/ variability has a higher impact on DSC for small/thin structures than on large structures. Additionally, image and identification of structure boundaries for OARs can impact contouring accuracy.…”

mentioning

confidence: 98%

Technical note: Atlas‐based Auto‐segmentation of masticatory muscles for head and neck cancer radiotherapy

Zhang

J Applied Clin Med Phys

et al. 2020

Purpose The study aimed to use quantitative geometric and dosimetric metrics to assess the accuracy of atlas‐based auto‐segmentation of masticatory muscles (MMs) compared to manual drawn contours for head and neck cancer (HNC) radiotherapy (RT). Materials and methods Fifty‐eight patients with HNC treated with RT were analyzed. Paired MMs (masseter, temporalis, and medial and lateral pterygoids) were manually delineated on planning computed tomography (CT) images for all patients. Twenty‐nine patients were used to generate the MM atlas. Using this atlas, automatic segmentation of the MMs was performed for the remaining 29 patients without manual correction. Auto‐segmentation accuracy for MMs was compared using dice similarity coefficients (DSCs), Hausdorff distance (HD), HD95, and variation in the center of mass (∆COM). The dosimetric impact on MMs was calculated (∆dose) using dosimetric parameters (D99%, D95%, D50%, and D1%), and compared with the geometric indices to test correlation. Results DSC mean ranges from 0.79 ± 0.04 to 0.85 ± 0.04, HD mean from 0.43 ± 0.08 to 0.82 ± 0.26 cm, HD95 mean from 0.32 ± 0.08 to 0.42 ± 0.16 cm, and ∆COM mean from 0.18 ± 0.11 to 0.33 ± 0.23 cm. The mean MM volume difference was < 15%. The correlation coefficient (r) of geometric and dosimetric indices for the four MMs ranges between −0.456 and 0.300. Conclusions Atlas‐based auto‐segmentation for masticatory muscles provides geometrically accurate contours compared to manual drawn contours. Dose obtained from those auto‐segmented contours is comparable to that from manual drawn contours. Atlas‐based auto‐segmentation strategy for MM in HN radiotherapy is readily availalbe for clinical implementation.