Denoised encoder-based residual U-net for precise teeth image segmentation and damage prediction on panoramic radiographs

Article Chemistry, Multidisciplinary

Deep Learning Based Detection of Missing Tooth Regions for Dental Implant Planning in Panoramic Radiographic Images

Jumi Park et al.

Summary: This study proposes an automated method for detecting missing tooth regions in panoramic radiographic images, assisting clinicians in diagnosing missing teeth regions for implant placement.

APPLIED SCIENCES-BASEL (2022)

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Article Multidisciplinary Sciences

A fully automatic AI system for tooth and alveolar bone segmentation from cone-beam CT images

Zhiming Cui et al.

Summary: This paper presents a deep learning system for efficient, precise, and fully automatic segmentation of dental cone-beam CT images. The system achieves comparable segmentation accuracy to experienced radiologists, while being 500 times faster. It consistently obtains accurate results on challenging cases with variable dental abnormalities, demonstrating its potential to boost clinical workflows of digital dentistry.

NATURE COMMUNICATIONS (2022)

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Article Radiology, Nuclear Medicine & Medical Imaging

Accuracy of advanced deep learning with tensorflow and keras for classifying teeth developmental stages in digital panoramic imaging

Norhasmira Mohammad et al.

Summary: This study proposes the use of image processing and machine learning model to segment the maturity development of mandibular premolars. By combining dynamic programming of active contour and convolutional neural networks, the proposed model achieves high accuracy in image segmentation and classification. The results show promising potential for semi-automated forensic dental staging in the future.

BMC MEDICAL IMAGING (2022)

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Article Computer Science, Information Systems

Tufts Dental Database: A Multimodal Panoramic X-Ray Dataset for Benchmarking Diagnostic Systems

Karen Panetta et al.

Summary: The application of Artificial Intelligence in dental healthcare has a very promising role, with expert analysis of X-ray images providing crucial clinical information. The Tufts Dental Database, containing 1000 panoramic dental radiography images, aims to help researchers incorporate human expertise into AI for improved abnormality detection accuracy. This dataset also includes eye-tracking and think-aloud protocol capturing the radiologist's expertise for further development in AI-powered dental care.

IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS (2022)

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Article Computer Science, Interdisciplinary Applications

Two-Stage Mesh Deep Learning for Automated Tooth Segmentation and Landmark Localization on 3D Intraoral Scans

Tai-Hsien Wu et al.

Summary: This research proposes a two-stage framework based on mesh deep learning for tooth labeling and landmark identification on intraoral scans. The framework achieves accurate segmentation and localization by labeling each tooth and selecting regions of interest on the original mesh. Experimental results demonstrate the potential application of this method in orthodontics.

IEEE TRANSACTIONS ON MEDICAL IMAGING (2022)

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Article Health Care Sciences & Services

A Tooth Segmentation Method Based on Multiple Geometric Feature Learning

Tian Ma et al.

Summary: A tooth segmentation method based on multiple geometric feature learning is proposed in this paper, which enhances the differences between geometric features by encoding and enhancing centroid coordinates and normal vectors, thereby improving the accuracy and efficiency of tooth segmentation.

HEALTHCARE (2022)

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Article Dentistry, Oral Surgery & Medicine

Artificial intelligence-driven novel tool for tooth detection and segmentation on panoramic radiographs

Andre Ferreira Leite et al.

Summary: The study evaluated the performance of a new AI-driven tool for tooth detection and segmentation on panoramic radiographs, demonstrating highly accurate and fast results compared to manual segmentation. The AI tool showed a sensitivity of 98.9% and a precision of 99.6% for tooth detection, with lower canines presenting the best segmentation results. The method also significantly reduced the time consumed for manual segmentation by 67%.

CLINICAL ORAL INVESTIGATIONS (2021)

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Article Computer Science, Artificial Intelligence

Accurate and automatic tooth image segmentation model with deep convolutional neural networks and level set method

Yunyun Yang et al.

Summary: An accurate and automatic active contour model is proposed for tooth segmentation in CBCT images, utilizing deep learning techniques. By applying deep convolutional neural networks, designing shape prior information, and defining prior constraint terms, the model is able to accurately segment tooth images. Experimental results demonstrate the effectiveness and superior performance of the proposed model compared to other segmentation models.

NEUROCOMPUTING (2021)

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Article Radiology, Nuclear Medicine & Medical Imaging

MIScnn: a framework for medical image segmentation with convolutional neural networks and deep learning

Dominik Mueller et al.

Summary: MIScnn is an open-source Python library designed to provide an intuitive API for fast construction of medical image segmentation pipelines, offering high configurability and multiple open interfaces for full pipeline customization. By running cross-validation on the Kidney Tumor Segmentation Challenge 2019 data set, MIScnn demonstrated powerful predictive capabilities based on the standard 3D U-Net model, showing that researchers can quickly set up a complete medical image segmentation pipeline with just a few lines of code.

BMC MEDICAL IMAGING (2021)

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Article Mathematical & Computational Biology

Dental Images' Segmentation Using Threshold Connected Component Analysis

Vincent Majanga et al.

Summary: Recent advances in medical imaging analysis, particularly the use of deep learning, have greatly improved performance in medical applications and systems. Deep learning techniques have shown great results in dental image segmentation, although the challenging features of dental carious lesions can restrain their performance.

COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE (2021)

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Article Computer Science, Artificial Intelligence

TSegNet: An efficient and accurate tooth segmentation network on 3D dental model

Zhiming Cui et al.

Summary: The study proposes a novel end-to-end learning method, TSegNet, for efficient and robust tooth segmentation on 3D scanned point cloud data of dental models. The method first detects all teeth using a distance-aware tooth centroid voting scheme, and then segments each tooth using a confidence-aware cascade segmentation module, achieving accurate results even in challenging cases with missing, crowding, or misaligned teeth.

MEDICAL IMAGE ANALYSIS (2021)

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Article Medicine, Legal