Automated segmentation of articular disc of the temporomandibular joint on magnetic resonance images using deep learning

ABSTRACT Temporomandibular disorders are typically accompanied by a number of clinical manifestations that involve pain and dysfunction of the masticatory muscles and temporomandibular

joint. The most important subgroup of articular abnormalities in patients with temporomandibular disorders includes patients with different forms of articular disc displacement and

deformation. Here, we propose a fully automated articular disc detection and segmentation system to support the diagnosis of temporomandibular disorder on magnetic resonance imaging. This

system uses deep learning-based semantic segmentation approaches. The study included a total of 217 magnetic resonance images from 10 patients with anterior displacement of the articular

disc and 10 healthy control subjects with normal articular discs. These images were used to evaluate three deep learning-based semantic segmentation approaches: our proposed convolutional

neural network encoder-decoder named 3DiscNet (Detection for Displaced articular DISC using convolutional neural NETwork), U-Net, and SegNet-Basic. Of the three algorithms, 3DiscNet and

SegNet-Basic showed comparably good metrics (Dice coefficient, sensitivity, and positive predictive value). This study provides a proof-of-concept for a fully automated deep learning-based

segmentation methodology for articular discs on magnetic resonance images, and obtained promising initial results, indicating that the method could potentially be used in clinical practice

for the assessment of temporomandibular disorders. SIMILAR CONTENT BEING VIEWED BY OTHERS ADVANTAGES OF DEEP LEARNING WITH CONVOLUTIONAL NEURAL NETWORK IN DETECTING DISC DISPLACEMENT OF THE

TEMPOROMANDIBULAR JOINT IN MAGNETIC RESONANCE IMAGING Article Open access 05 July 2022 MULTI-CLASS SEGMENTATION OF TEMPOROMANDIBULAR JOINT USING ENSEMBLE DEEP LEARNING Article Open access 16

August 2024 DEEP NEGATIVE VOLUME SEGMENTATION Article Open access 11 August 2021 INTRODUCTION Temporomandibular disorder (TMD) is a collective term covering a number of clinical

manifestations that involve pain and dysfunction of the masticatory muscles and temporomandibular joint (TMJ)1. The most common signs and symptoms of TMD are regional pain in the face and

preauricular area, malocclusion, limited range of jaw movement, and TMJ noises and locking2. According to a prospective cohort study of adults in the United States, the estimated annual

incidence rate of first‐onset TMD is 3.9%, and it is typically accompanied with mild to moderate levels of pain and disability3. In developed countries, it is considered a widespread

disorder affecting 5–12% of the population4. Magnetic resonance (MR) imaging (MRI) is recognized as the best imaging modality for assessment of TMJ because it allows visualization of the

anatomical and pathological features of all joint components5. Notably, MRI permits evaluation of the morphology and position of the articular disc, the presence or absence of reduction

during mouth or jaw opening, the morphology and surface characteristics of the mandibular condyle, abnormal bone marrow signal in mandible and temporal bone, and the presence or absence of

joint effusion. The most important subgroup of articular abnormalities in patients with TMD includes those with displacement and deformation of the articular disc6; this is an intracapsular

disorder involving the disc-condylar complex, with a prevalence of 30–60% in patients with TMD7. Importantly, an MRI examination is expected for confirmation of the displacement and

deformation of the articular disc, to ensure accurate diagnosis and prediction of treatment response. Articular disc displacements can be subdivided into those with and without reduction7.

Disc displacement with reduction is the most frequent type, and is characterized by the displaced disc returning to the normal position on mouth opening. In disc displacement without

reduction, the articular disc is in a displaced position in relation to the superior part of the condyle in both closed- and open-mouth positions. In patients with articular disc

displacement, the articular discs may be displaced when the mouth is open, or they may return to the correct position resulting in a significant change in the position of the disc between

closing and opening. Therefore, a comprehensive diagnosis using both closed- and open-mouth MR images is necessary. Artificial intelligence (AI) is gaining attention in various clinical

disciplines and the dental field is no exception, with AI-based applications having been studied to streamline dental and oral care and improve the health of more people at a low

cost8,9,10,11,12. Such AI-based applications should free dental professionals from time-consuming routine tasks and ultimately promote personalized, predictive, preventive, and participatory

dental care13. Among the variety of AI algorithms available, the convolutional neural network (CNN)-based deep learning approach has become popular because of its excellent ability for

object recognition when applied to medical images. Moreover, the increase in computational power and the pervasiveness of open-source frameworks have dramatically facilitated the development

of CNNs14. In these circumstances, deep learning has been widely implemented for detection and segmentation purposes, and has showed encouraging performance. The fully convolutional

network—a derivative form of the CNN—is the most widely used deep neural network in medical image segmentation, and several variants have been reported, including U-Net15 and SegNet16

architectures. The aim of this study was to construct deep learning-based semantic segmentation algorithms for automatic detection and segmentation of the articular disc of the TMJ on MR

images. The null hypothesis tested is that there is no difference in the performance of the three deep learning-based semantic segmentation algorithms for extracting articular discs from MR

images. MATERIALS AND METHODS DATASET This nonrandomized retrospective study was approved by the Ethical Committee for Epidemiology of Hiroshima University (Approval Number: E-2119). All

methods in this study were performed in accordance with the Ethical Guidelines for Medical and Human Research Involving Human Subjects, Japan. Because of the retrospective design of this

study, the requirement for informed consent was waived by the Ethical Committee for Epidemiology of Hiroshima University. The study included MR images of 10 patients with anterior

displacement of the articular disc aged between 19 and 39 years (mean age of 26.4; 8 women, 2 men), and 10 healthy control subjects aged between 18 and 41 years (mean age of 27; 8 women, 2

men). Each subject underwent MRI on an Ingenia 3.0-T CX Quasar Dual scanner (Philips Healthcare, Best, the Netherlands). Only proton density-weighted sagittal images were used in this study.

In total, 217 proton density-weighted sagittal images were used; 106 from the 10 patients and 111 from the 10 control subjects. These images included the left and right TMJ regions in mouth

closed (102 images in total, 51 of patients and 61 of controls) and mouth open (115 images in total, 55 of patients and 60 of controls) positions. The patients with anterior displacement of

the articular disc included those both with and without reduction. The number of consecutive MRI slices of the TMJ region in which the experts were able to visualize the articular disc

differed between patients and healthy subjects, which is the reason why the number of included images differs between the two subject groups. Two expert orthodontists (12 and 6 years of

experience) and one expert oral and maxillofacial radiologist (25 years of experience) independently identified and manually segmented all articular discs of the TMJ on the MR images using

ImageJ software (version 1.53, National Institutes of Health, Bethesda, MD; Fig. 1). The original MR data, saved in DICOM, were loaded into ImageJ and converted to JPEG files. The experts

manually segmented the articular discs directly on a graphic tablet using a stylus pen (Artist 12, XP-PEN, Shenzhen, China). The articular disc region of the JPEG image was then filled in

white (RGB value: 255, 255, 255), pixel-by-pixel, using the ‘pencil tool’ of Image J. Digital zooming was used to adjust the resolution as needed. After segmentation, the results were

reviewed by three experts skilled in reading the articular disc region on MR images, and those images for which the experts were in agreement were adopted as the dataset. The manually

segmented MR images (manual segmentation) were split into a training data set (80%) and test set (20%) for use in each of the following experiments. To derive a dataset showing the normal

position of articular discs, the 111 images from the 10 control subjects were randomly split into 88 training images and 23 test images. For a dataset showing displaced articular disc

positions, the 106 images from 10 patients were randomly split into 84 training images and 22 test images. For a dataset showing a mix of normal position and displaced articular discs, the

217 images were randomly split into 173 training images and 44 test images. DEEP LEARNING ALGORITHMS All procedures were performed using an Intel Core i7-9750H 2.60-GHz central processing

unit (Intel, Santa Clara, CA), 16.0 GB random access memory, and an NVIDIA GeForce RTX 2070 MAX-Q 8.0-GB graphics processing unit (NVIDIA, Santa Clara, CA). Deep learning algorithms were

constructed using Python17 and were implemented using the Keras framework for deep learning with TensorFlow as the backend. We adapted three convolutional semantic segmentation approaches:

an encoder-decoder CNN, U-Net15, and SegNet16, which are all well suited to segmentation tasks. The overall architectures are shown in Fig. 2. In this study, we propose an encoder-decoder

CNN model that we named 3DiscNet (Detection for Displaced articular DISC using convolutional neural NETwork), which has an asymmetric encoder-decoder architecture for the extraction of

features at different spatial fields of view (Fig. 2A). To reduce the overfitting of the network, the dropout layer is placed behind the convolutional layers and max-pooling layers18. All

the dropouts were given rates of 0.3 for the work described in this study. The final layer consists of a Sigmoid activation function that classifies each pixel as articular disc or

background. The U-Net was a fully connected convolutional network that consists of convolution and max-pooling layers in the encoder part, and convolution and transpose layers in the decoder

part. Encoder outputs were concatenated to the decoding layers to share spatial cues and to propagate the loss efficiently. The SegNet used a classical architecture for semantic pixel-wise

segmentation, with encoder layers using max-pooling indices to upsample the feature maps and convolve them with a trainable decoder network. The original architectures of U-Net and SegNet

are illustrated in Fig. 2B, C. The type of SegNet architecture used is currently termed SegNet-Basic19. The final layers are similar to the 3DiscNet, employing a sigmoid classifier instead

of the original soft-max classifier in U-Net and SegNet-Basic. The U-Net and SegNet have shown promise for semantic segmentation of organs and pathology on MR images20,21,22. First, regions

of interest (ROIs) around the articular discs were extracted from the datasets. The original image resolution was 512 × 512 pixels, and the ROIs, which were defined using a 161 × 184 pixel

bounding box, were automatically cropped from the images using Python algorithms. The ROI images were then resized to 224 × 256 pixels for input into the three types of convolutional

encoder-decoder network (Fig. 3). The 3DiscNet was trained using the Adam optimizer with a learning rate of 1.0 × 10–3, and the three algorithms were trained for a total of 2000 epochs.

PERFORMANCE METRICS The test data were used to validate the accuracy and computational efficacy of the models. The convolutional encoder-decoder network performance was assessed using the

Dice similarity coefficient, sensitivity, and positive predictive value (PPV) of the test dataset. The Dice similarity coefficient, which is a popular similarity metric, was calculated using

the following formula: $$ Dice = \frac{{2\left| {P \cap T} \right|}}{\left| P \right| + \left| T \right|} $$ where _P_ is the pixel area of the articular disc segmented with the

convolutional encoder-decoder network, and _T_ is the pixel area of the manually segmented ROI. The sensitivity is the percentage of the actual articular disc area correctly predicted as the

articular disc area, defined as: $$ Sensitivity = \frac{{\left| {P \cap T} \right|}}{\left| T \right|} $$ The PPV is a measure of the percentage of the correctly predicted articular disc

area over the actual articular disc area as follows: $$ PPV = \frac{{\left| {P \cap T} \right|}}{\left| P \right|} $$ A one-way analysis of variance (ANOVA) and post-hoc Tukey’s honestly

significant difference (HSD) tests were used to analyze differences between the mean values of individual models. Statistical analyses were performed using IBM SPSS Statistics 27 (IBM Corp.,

Armonk, NY, USA). RESULTS As shown in Fig. 4, the training loss of each of the models decreased and converged, which indicates that these models did not show overfitting. The training loss

dropped faster with the 3DiscNet model than with the U-Net and SegNet-Basic models, indicating faster convergence. Figure 5 shows representative examples of visual segmentation. The first

column shows test data for validating algorithm performance, the second column shows manual segmentations, and the third and fourth columns show the algorithmic segmentations. Each row

denotes a particular algorithm: 3DiscNet, U-Net, and SegNet-Basic, from the top downwards. Results obtained on the dataset containing only normal articular disc placement are shown in Fig. 

5A; 3DiscNet and SegNet-Basic made predictions that were in good agreement with the manual segmentation data. Figure 5B shows the results for the dataset containing only patients with

articular disc displacement. The results are similar to those shown in Fig. 5A, with 3DiscNet and SegNet-Basic making predictions that were in good agreement with the manual segmentation

data. Figure 5C shows results for both normal and displaced articular discs; 3DiscNet and SegNet-Basic again made segmentations that were in good agreement with the manual segmentation data.

However, the U-Net results showed a large number of false positives and false negatives with all of the datasets (Fig. 5). We used the test data to evaluate the performance of the

algorithms according to the three quantitative metrics of Dice coefficient, sensitivity, and PPV, computing the values for both normal and displaced articular disc segmentations. To reveal

their distributions, these metrics are shown as box plots in Fig. 6 for 3DiscNet, U-Net, and SegNet-Basic, and for both normal and displacement test images. The Dice coefficient for

segmentation performance was highest for SegNet-Basic, with the highest median accuracy and a small standard deviation in the dataset including both normal position and displaced articular

discs. U-Net showed significantly lower values (and large standard deviations) than 3DiscNet and SegNet-Basic (one-way ANOVA with Tukey HSD; _p_ < 0.001) for all three metrics, and the

results were unstable for both normal position and displaced articular discs (Table 1). DISCUSSION TMJ disc disorders caused by articular disc displacement, deformation, perforation, and

fibrosis are the most common pathological conditions in TMD. Although MRI can provide a definitive diagnosis of TMJ disc disorder, concerns have been raised about the reliability of MRI

nterpretations23. Previous studies have revealed that uncalibrated observers, even experienced experts, are unable to make accurate MRI assessments of disc disorders; the interpretation of

MRI of the TMJ typically shows poor reproducibility24,25,26. These studies concluded that more effort is needed to understand the changes detectable on MRI. In this study using MR images, we

demonstrated that a deep learning-based semantic segmentation approach can be applied to the detection and segmentation of TMJ articular discs. Our overall results showed that two deep

learning algorithms—3DiscNet and SegNet-Basic—performed good detection and segmentation, whereas the U-Net algorithm did not obtain satisfactory results. The mean Dice coefficients for the

dataset with both normal placement and displaced articular discs were 0.70 and 0.74 for 3DiscNet and SegNet-Basic, respectively. These are important results in that they show that the models

can not only detect the existence of articular discs, but can also successfully find the locations of articular discs, regardless of normal positioning or displacement. However, the

performance of U-Net was relatively poor, and its mean Dice coefficient was only 0.46. Indeed, the segmentation by U-Net revealed over-segmentation with the inclusion of irrelevant regions

in addition to the articular disc. Our results demonstrated significant differences between U-Net and the other two algorithms in the performance metrics evaluating the extraction of

articular discs from MR images, supporting rejection of the null hypothesis. The articular disc lacks a clear border on MR images and its position is often displaced in patients with TMD,

and therefore a great deal of variation in the shape and position of the disk is found among patients. Similarly, the prostate has also been reported as an organ with fuzzy boundaries on MR

images27. These conditions make it difficult to detect and segment the articular disc accurately. Although U-Net was originally proposed for the segmentation of biological images with a

limited quantity of training data15, studies have reported that it has a tendency for less accurate segmentation of objects with fuzzy boundaries27,28,29. Specifically, on very challenging

images, U-Net tends to over-segment, under-segment, make false predictions, and even completely miss the target objects29. A previous study aiming to achieve mandibular canal segmentation on

cone beam computed tomography (CBCT) images reported that U-Net mis- and over-segmented the mandibular canal region30. These reports are in accord with our results showing that the

performance of U-Net on articular disc detection and segmentation was poor, even though 3DiscNet and SegNet-Basic showed comparably good metrics (i.e., Dice coefficient, sensitivity, and

PPV) for all datasets. SegNet was originally proposed for outdoor and indoor scene segmentation at a pixel level16. Some studies compared SegNet and U-Net for tissue segmentation, including

that of Liu et al., who reported that SegNet showed more favorable performance than U-Net for cartilage and bone segmentation on musculoskeletal MR images31. Kwak et al. used SegNet and

U-Net to segment the mandibular canal on the CBCT images of 102 patients, and also obtained good performance with SegNet30. However, to the contrary, Zhang et al. found that U-Net performed

more favorable segmentation than SegNet when applied to breast MR images, which play a crucial role in diagnosis and the screening of those at high-risk of breast cancer20. Therefore, the

suitability of these two models depends on the specific segmentation task and dataset, and appropriate comparisons will continue to be required. Research on the application of AI to TMD has

recently been reported, although the studies are limited to the diagnosis of TMJ osteoarthritis (OA) using CBCT images. A group from Brazil developed a system using deep learning that allows

the staging of bony changes in TMJ OA32,33. Lee et al. tried to develop a system to detect TMJ OA on sagittal CBCT images using a deep learning method for object detection34. Two studies

reported by US groups successfully integrated high-resolution CBCT and biological markers from patients with TMJ OA, with one study performing staging of condylar morphology35 and the other

diagnosing the status of the disease36. While all these studies used CBCT, we have shown that AI can also be applied to MRI for TMD diagnosis. Given the results to date, including those from

our proposed algorithms, it can be expected that AI systems for the diagnostic imaging of TMJ will be further developed, and will contribute to establishing a comprehensive diagnostic

system for the maxillofacial region. Our study had several limitations. All MRI scans were acquired at a single institution, and our models do not account for variations in hardware

implementation and scanning techniques across institutions, which may bias the results. To increase the model robustness, evaluation of our concepts with a multicenter dataset is desirable.

Moreover, the ‘ground truth’ manual segmentation images that we used to train the algorithms were also created by a limited number of experts. We manually segmented the articular discs as

carefully as possible and then agreed on the ‘ground truth’ segmentation. Given the difficulty in interpreting MRI of the TMJ region, which was part of the motivation for performing this

study, we believe that the participation of further experts will allow us to build a more widely acceptable dataset. Another limitation is that our study only made comparisons between the

three models. Although AI has much potential, no algorithm can perform well on all possible problems. Therefore, the successful use of AI requires a great deal of effort by human experts37.

Further studies are needed to optimize the structure of the CNNs, including comparisons with other models. In future work, we will modify the SegNet and 3DiscNet to include segmentation of

other TMJ components (e.g., effusion, osteophytes) within the framework, and they will be trained and tested using a multicenter study. CONCLUSION We conclude that within the limitations of

this study, 3DiscNet and SegNet-Basic trained on manually segmented MR images can segment TMJ articular discs on MR images. This study provides a proof-of-concept for a deep learning-based

fully automated segmentation methodology for articular discs on MR images, and it obtained promising initial results, indicating that the methodology could potentially be used for the

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Download references ACKNOWLEDGEMENTS This study was partially supported by grants-in-aid from the Ministry of Education, Culture, Sports, Science and Technology of Japan to Y.M. (20K18604).

We thank Karl Embleton, Ph.D., from Edanz Group (https://www.jp.edanz.com/ac) for editing a draft of this manuscript. AUTHOR INFORMATION Author notes * These authors contributed equally:

Shota Ito and Yuichi Mine. AUTHORS AND AFFILIATIONS * Department of Orthodontics and Craniofacial Development Biology, Graduate School of Biomedical and Health Sciences, Hiroshima

University, Hiroshima, 734-8553, Japan Shota Ito, Yuki Yoshimi, Azusa Onishi & Kotaro Tanimoto * Department of Medical System Engineering, Graduate School of Biomedical and Health

Sciences, Hiroshima University, Hiroshima, 734-8553, Japan Yuichi Mine, Saori Takeda, Akari Tanaka & Takeshi Murayama * School of Dentistry, College of Dentistry, China Medical

University, Taichung, 404, Taiwan Tzu-Yu Peng * School of Dentistry, College of Oral Medicine, Taipei Medical University, Taipei, 11031, Taiwan Tzu-Yu Peng * Department of Oral and

Maxillofacial Radiology, Graduate School of Biomedical and Health Sciences, Hiroshima University, Hiroshima, 734-8553, Japan Takashi Nakamoto, Toshikazu Nagasaki & Naoya Kakimoto Authors

* Shota Ito View author publications You can also search for this author inPubMed Google Scholar * Yuichi Mine View author publications You can also search for this author inPubMed Google

Scholar * Yuki Yoshimi View author publications You can also search for this author inPubMed Google Scholar * Saori Takeda View author publications You can also search for this author

inPubMed Google Scholar * Akari Tanaka View author publications You can also search for this author inPubMed Google Scholar * Azusa Onishi View author publications You can also search for

this author inPubMed Google Scholar * Tzu-Yu Peng View author publications You can also search for this author inPubMed Google Scholar * Takashi Nakamoto View author publications You can

also search for this author inPubMed Google Scholar * Toshikazu Nagasaki View author publications You can also search for this author inPubMed Google Scholar * Naoya Kakimoto View author

publications You can also search for this author inPubMed Google Scholar * Takeshi Murayama View author publications You can also search for this author inPubMed Google Scholar * Kotaro

Tanimoto View author publications You can also search for this author inPubMed Google Scholar CONTRIBUTIONS Conceptualization: S.I., Y.M., Y.Y., and K.T.; data curation: S.I., Y.Y., S.T.,

A.O., Ta.N., and To.N.; formal analysis: Y.M., S.T., and A.T.; investigation: S.I., Y.M., Y.Y., and S.T.; methodology: Y.M., S.T., A.T., and T.M.; project administration: Y.M. and Y.Y.;

Resources: Y.M., N.K., and K.T.; software: S.I., and S.T.; supervision: N.K., T.M., and K.T.; validation: S.I., Y.M., Y.Y., S.T., and T.-Y.P.; visualization: S.I. and Y.M.; Writing—original

draft: S.I., Y.M., and K.N.; writing—review and editing: S.I., Y.M., Y.Y., S.T., A.T., A.O., T.-Y.P., Ta.N., To.N., N.K., T.M., and K.T; All authors have read and agreed to the published

version of the manuscript. CORRESPONDING AUTHOR Correspondence to Yuichi Mine. ETHICS DECLARATIONS COMPETING INTERESTS The authors declare no competing interests. ADDITIONAL INFORMATION

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Automated segmentation of articular disc of the temporomandibular joint on magnetic resonance images using deep learning. _Sci Rep_ 12, 221 (2022). https://doi.org/10.1038/s41598-021-04354-w

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