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

New method for analysing spatial relationships of facial muscles on MRI: a pilot study

Dysfunction of the facial musculature can have significant physical, social, and psychological consequences. In surgeries such as cleft surgery or craniofacial bimaxillary osteotomies, the perioral facial muscles may be detached or severed, potentially altering their functional vectors and mimicry capabilities. Ensuring correct reconstruction and maintenance of anatomical sites and muscle vectors is crucial in these procedures. However, a standardized method for perioperative assessment of the facial musculature and function is currently lacking. The aim of this study was to develop a workflow to analyse the three-dimensional vectors of the facial musculature using magnetic resonance imaging (MRI) scans. A protocol for localizing the origins and insertions of these muscles was established. The protocol was implemented using the 3DMedX computer program and tested on 7 Tesla MRI scans obtained from 10 healthy volunteers. Inter- and intra-observer variability were assessed to validate the protocol. The absolute intra-observer variability was 2.6 mm (standard deviation 2.0 mm), and absolute inter-observer variability was 2.6 mm (standard deviation 1.5 mm). This study presents a reliable and reproducible method for analysing the spatial relationships and functional significance of the facial muscles. The workflow developed facilitates perioperative assessment of the facial musculature, potentially aiding clinicians in surgical planning and potentially enhancing the outcomes of midface surgery.

Bi-Graph Reasoning for Masticatory Muscle Segmentation From Cone-Beam Computed Tomography

Automated segmentation of masticatory muscles is a challenging task considering ambiguous soft tissue attachments and image artifacts of low-radiation cone-beam computed tomography (CBCT) images. In this paper, we propose a bi-graph reasoning model (BGR) for the simultaneous detection and segmentation of multi-category masticatory muscles from CBCTs. The BGR exploits the local and long-range interdependencies of regions of interest and category-specific prior knowledge of masticatory muscles by reasoning on the category graph and the region graph. The category graph of the learnable muscle prior knowledge handles high-level dependencies of muscle categories, enhancing the feature representation with noise-agnostic category knowledge. The region graph models both local and global dependencies of the candidate muscle regions of interest. The proposed BGR accommodates the high-level dependencies and enhances the region features in the presence of entangled soft tissue and image artifacts. We evaluated the proposed approach by segmenting masticatory muscles on clinically acquired CBCTs. Extensive experimental results show that the BGR effectively segments masticatory muscles with state-of-the-art accuracy.

Magnetic Resonance Imaging-Based Delineation of Organs at Risk in the Head and Neck Region

Purpose

The aim of this article is to establish a comprehensive contouring guideline for treatment planning using only magnetic resonance images through an up-to-date set of organs at risk (OARs), recommended organ boundaries, and relevant suggestions for the magnetic resonance imaging (MRI)-based delineation of OARs in the head and neck (H&N) region.

Methods and Materials

After a detailed review of the literature, MRI data were collected from the H&N region of healthy volunteers. OARs were delineated in the axial, coronal, and sagittal planes on T2-weighted sequences. Every contour defined was revised by 4 radiation oncologists and subsequently by 2 independent senior experts (H&N radiation oncologist and radiologist). After revision, the final structures were presented to the consortium partners.

Results

A definitive consensus was reached after multi-institutional review. On that basis, we provided a detailed anatomic and functional description and specific MRI characteristics of the OARs.

Conclusions

In the era of precision radiation therapy, the need for well-built, straightforward contouring guidelines is on the rise. Precise, uniform, delineation-based, automated OAR segmentation on MRI may lead to increased accuracy in terms of organ boundaries and analysis of dose-dependent sequelae for an adequate definition of normal tissue complication probability.

Validity and reliability of masseter muscles segmentation from the transverse sections of Cone-Beam CT scans compared with MRI scans

BACKGROUND

To evaluate the validity and reliability of cone-beam computed tomography (CBCT) masseter muscle segmentation by comparing with the magnetic resonance imaging (MRI) masseter muscle segmentation of the same patients.

METHODS

Seventeen volunteers were included in this study. CBCT and MRI scans of the volunteers were taken, respectively, within one month. The masseter muscles in the CBCT scans were segmented by a generative adversarial network (GAN)-based framework combined with manual check. The masseter muscles in the MRI scans were segmented manually. The segmentations were repeated by the first examiner and a second examiner. For cross-sectional area (CSA), paired t-test, intraclass correlation coefficient (ICC) and standard error of measurement (SEM) were calculated to evaluate the validity and reliability of the segmentations. The validity and reliability were also calculated by Dice similarity coefficient (DSC) and average Hausdorff distance (aHD) between different segmentations. Seventeen volunteers were included in this study. CBCT and MRI scans of the volunteers were taken, respectively, within one month. The masseter muscles in the CBCT scans were segmented by a generative adversarial network (GAN)-based framework combined with manual check. The masseter muscles in the MRI scans were segmented manually. The segmentations were repeated by the first examiner and a second examiner. For cross-sectional area (CSA), paired t-test, intraclass correlation coefficient (ICC) and standard error of measurement (SEM) were calculated to evaluate the validity and reliability of the segmentations. The validity and reliability were also calculated by Dice similarity coefficient (DSC) and average Hausdorff distance (aHD) between different segmentations.

RESULTS

Paired t-test showed that there was no significant difference in CSA between CBCT and MRI masseter segmentations. The ICCs were all larger than 0.95 and the SEM was less than 4.85 mm² for CSA. The DSC was all larger than 0.95 showing over 95% of similarity between CBCT and MRI masseter segmentations. The aHD was all smaller than 0.09 mm showing great consistency of the contour of CBCT and MRI segmentations.

CONCLUSION

Masseter muscle segmentation from CBCT scans was not significantly different from the segmentation from MRI scans. CBCT muscle segmentation showed great validity compared with MRI scans, and great reliability in retests.