Semi-supervised anatomical landmark detection via shape-regulated self-training

Chen, Runnan; Ma, Yuexin; Liu, Lingjie; Chen, Nenglun; Cui, Zhiming; Wei, Guodong; Wang, Wenping

doi:10.1016/j.neucom.2021.10.109

Cited by 19 publications

(11 citation statements)

References 26 publications

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“…Both yielded promising results for deep learning (DL)-based methods, which outperformed previously proposed knowledge-based, atlas-based, or shallow learning-based methods. DL methods published in the past few years can localize 3D cephalometric landmarks with great accuracy, often under the 2-mm threshold of clinical acceptability (Lee et al 2019;Torosdagli et al 2019;Lang et al 2020;Ma et al 2020;Yun et al 2020;Zhang et al 2020;Bermejo et al 2021;Chen et al 2021;Kang et al 2021;Liu et al 2021;Chen et al 2022). The studies showing the best results usually formulate landmark detection as a regression problem, using landmark heatmap regression methods (Zhang et al 2020;Chen et al 2021).…”

Section: Introductionmentioning

confidence: 99%

Automatic 3-Dimensional Cephalometric Landmarking via Deep Learning

et al. 2022

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The increasing use of 3-dimensional (3D) imaging by orthodontists and maxillofacial surgeons to assess complex dentofacial deformities and plan orthognathic surgeries implies a critical need for 3D cephalometric analysis. Although promising methods were suggested to localize 3D landmarks automatically, concerns about robustness and generalizability restrain their clinical use. Consequently, highly trained operators remain needed to perform manual landmarking. In this retrospective diagnostic study, we aimed to train and evaluate a deep learning (DL) pipeline based on SpatialConfiguration-Net for automatic localization of 3D cephalometric landmarks on computed tomography (CT) scans. A retrospective sample of consecutive presurgical CT scans was randomly distributed between a training/validation set ( n = 160) and a test set ( n = 38). The reference data consisted of 33 landmarks, manually localized once by 1 operator( n = 178) or twice by 3 operators ( n = 20, test set only). After inference on the test set, 1 CT scan showed “very low” confidence level predictions; we excluded it from the overall analysis but still assessed and discussed the corresponding results. The model performance was evaluated by comparing the predictions with the reference data; the outcome set included localization accuracy, cephalometric measurements, and comparison to manual landmarking reproducibility. On the hold-out test set, the mean localization error was 1.0 ± 1.3 mm, while success detection rates for 2.0, 2.5, and 3.0 mm were 90.4%, 93.6%, and 95.4%, respectively. Mean errors were −0.3 ± 1.3° and −0.1 ± 0.7 mm for angular and linear measurements, respectively. When compared to manual reproducibility, the measurements were within the Bland–Altman 95% limits of agreement for 91.9% and 71.8% of skeletal and dentoalveolar variables, respectively. To conclude, while our DL method still requires improvement, it provided highly accurate 3D landmark localization on a challenging test set, with a reliability for skeletal evaluation on par with what clinicians obtain.

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Section: Introductionmentioning

confidence: 99%

Automatic 3-Dimensional Cephalometric Landmarking via Deep Learning

et al. 2022

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“…Six studies used an image dataset of CT, two studies CBCT and four studies used both. One study used as dataset both annotated CBCT images (labeled images) and not-annotated ones (unlabeled) [29] and three studies [30][31][32] used a dataset composed by labeled CT images and a landmark dataset of the 3D positions of landmarks from CT. One study reported the mean error in pixels dimension [33] instead of mm, and for this review, it was converted in mm using the pixel-to-mm conversion rate reported in the article. Studies detected a mean (± standard deviation) of 47(± 35) landmarks, with a 5-105 range.…”

Section: Study Selection and Qualitative Analysismentioning

confidence: 99%

“…Description of the sample characteristics (e.g., age, gender, craniofacial characteristics) was not provided by all the articles. Only eight studies [29,[34][35][36][37][38][39][40] indicated the execution time of Fig. 1 Prisma flowchart for the papers' selection process the identification system, and four of them [34,36,38,39] also reported the duration of the training phase.…”

Section: Study Selection and Qualitative Analysismentioning

confidence: 99%

Accuracy of automated 3D cephalometric landmarks by deep learning algorithms: systematic review and meta-analysis

et al. 2023

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Objectives The aim of the present systematic review and meta-analysis is to assess the accuracy of automated landmarking using deep learning in comparison with manual tracing for cephalometric analysis of 3D medical images. Methods PubMed/Medline, IEEE Xplore, Scopus and ArXiv electronic databases were searched. Selection criteria were: ex vivo and in vivo volumetric data images suitable for 3D landmarking (Problem), a minimum of five automated landmarking performed by deep learning method (Intervention), manual landmarking (Comparison), and mean accuracy, in mm, between manual and automated landmarking (Outcome). QUADAS-2 was adapted for quality analysis. Meta-analysis was performed on studies that reported as outcome mean values and standard deviation of the difference (error) between manual and automated landmarking. Linear regression plots were used to analyze correlations between mean accuracy and year of publication. Results The initial electronic screening yielded 252 papers published between 2020 and 2022. A total of 15 studies were included for the qualitative synthesis, whereas 11 studies were used for the meta-analysis. Overall random effect model revealed a mean value of 2.44 mm, with a high heterogeneity (I2 = 98.13%, τ2 = 1.018, p-value < 0.001); risk of bias was high due to the presence of issues for several domains per study. Meta-regression indicated a significant relation between mean error and year of publication (p value = 0.012). Conclusion Deep learning algorithms showed an excellent accuracy for automated 3D cephalometric landmarking. In the last two years promising algorithms have been developed and improvements in landmarks annotation accuracy have been done.

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“…Wen et al [20] used the ASM algorithm and 25 auricular subzones were divided according to the WFAM standard. While the deep learning-based methods have shown promising results in both landmark detection [29], [30] and area segmentation tasks [21], [42], one of the possible reasons for limited studies on auricle-related topics might be the missing of ear image datasets with annotated landmarks. One closely related research topic is facial landmark detection, and many studies localize landmark points by utilizing deep learning technology, including various of CNN-based networks [22], [24], [27], multitask learning [23], [25], and transform learning [28] etc.…”

Section: Related Workmentioning

confidence: 99%

Deep Learning-Based Auricular Point Localization for Auriculotherapy

Sun

Dong

Li³

et al. 2022

IEEE Access

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Auriculotherapy is one of the main forms of treatment in Traditional Chinese Medicine, whose potential as an alternative medicine for both health evaluation and disease treatment has been reported in many cases. However, its efficacy highly relies on the accurate localization of auricular points, which are not easy to be remembered due to their complexity. To explore an efficient way of locating auricular points, this study proposed a deep learning-based method of automatically locating auricular points from auricular images. A self-collected dataset named EID was created for TCM auriculotherapy research, with 91 auriculotherapy-related landmark points manually annotated according to the Chinese national standardization. A deep neural network structure was trained for landmark detection, and a direction normalization module was proposed to compensate for the detection error caused by the difference between the left and right ears. The trained model was validated on dataset EID. An average NME of 0.0514±0.0023 was achieved, which outperformed similar works. In addition, a certain auricular area corresponding to the digestive system was segmented based on the localized landmarks, and the results were tested in real-time video streaming. The proposed work for both auricular landmark and area identification can be widely used in auriculotherapy education and applications.

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Semi-supervised anatomical landmark detection via shape-regulated self-training

Cited by 19 publications

References 26 publications

Automatic 3-Dimensional Cephalometric Landmarking via Deep Learning

Automatic 3-Dimensional Cephalometric Landmarking via Deep Learning

Accuracy of automated 3D cephalometric landmarks by deep learning algorithms: systematic review and meta-analysis

Deep Learning-Based Auricular Point Localization for Auriculotherapy

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