Visual discrimination of clinical tissue types remains challenging, with traditional RGB imaging providing limited contrast for such tasks. Hyperspectral imaging (HSI) is a promising technology providing rich spectral information that can extend far beyond three-channel RGB imaging. Moreover, recently developed snapshot HSI cameras enable real-time imaging with significant potential for clinical applications. Despite this, the investigation into the relative performance of HSI over RGB imaging for semantic segmentation purposes has been limited, particularly in the context of medical imaging. Here we compare the performance of state-of-the-art deep learning image segmentation methods when trained on hyperspectral images, RGB images, hyperspectral pixels (minus spatial context), and RGB pixels (disregarding spatial context). To achieve this, we employ the recently released Oral and Dental Spectral Image Database (ODSI-DB), which consists of 215 manually segmented dental reflectance spectral images with 35 different classes across 30 human subjects. The recent development of snapshot HSI cameras has made real-time clinical HSI a distinct possibility, though successful application requires a comprehensive understanding of the additional information HSI offers. Our work highlights the relative importance of spectral resolution, spectral range, and spatial information to both guide the development of HSI cameras and inform future clinical HSI applications.
OralBBNet: Spatially Guided Dental Segmentation of Panoramic X-Rays with Bounding Box Priors
Department of Computer Science and Engineering（计算机科学与工程系）；School of IT and Engineering（信息与工程学院）；Indian Institute of Technology Kharagpur（印度理工学院Kharagpur分校）；Kazakh-British Technical University（哈萨克-英国技术大学）；Intelligent Devices Institute（智能设备研究所）；St. Petersburg Electrotechnical University "LETI"（圣彼得堡电力大学"LETI"）；Department of Automation and Control Processes（自动化与控制过程系）
AI-Dentify: Deep learning for proximal caries detection on bitewing x-ray -- HUNT4 Oral Health Study
SINTEF Digital（辛特夫数字研究院）；Boneprox A.B.（Boneprox公司）；Norwegian University of Science and Technology（挪威科技大学）；Kompetansesenteret Tannhelse Midt (TkMidt)（中部口腔健康 competence中心（TkMidt））；Boneprox A.S.（Boneprox公司）
DentalX: Context-Aware Dental Disease Detection with Radiographs
Centre for Oral, Clinical \& Translational Sciences, King’s College London, United Kingdom Surrey Institute for People-Centred AI, University of Surrey, United Kingdom
University of California, Los Angeles（加利福尼亚大学洛杉矶分校）
Panoramic X-ray (PX) provides a 2D picture of the patient's mouth in a panoramic view to help dentists observe the invisible disease inside the gum. However, it provides limited 2D information compared with cone-beam computed tomography (CBCT), another dental imaging method that generates a 3D picture of the oral cavity but with more radiation dose and a higher price. Consequently, it is of great interest to reconstruct the 3D structure from a 2D X-ray image, which can greatly explore the application of X-ray imaging in dental surgeries. In this paper, we propose a framework, named Oral-3D, to…
Radious: Unveiling the Enigma of Dental Radiology with BEIT Adaptor and Mask2Former in Semantic Segmentation
Gilan University of Medical Sciences（吉兰医科大学）；University of Tehran（德黑兰大学）；Hamedan University of Technology（哈马丹工业大学）；Shahid Beheshti University of Medical Sciences（沙希德·贝赫什提医科大学）
Chongqing University of Posts and Telecommunications（重庆邮电大学）；Stomatological Hospital of Chongqing Medical University（重庆医科大学附属口腔医院）；Macau University of Science and Technology（澳门科技大学）；Xi’an Jiaotong University（西安交通大学）；ShanghaiTech University（上海科技大学）；Shanghai United Imaging Intelligence Co., Ltd.（上海联影智能科技有限公司）；The University of Hong Kong（香港大学）
Precise segmentation of teeth from intra-oral scanner images is an essential task in computer-aided orthodontic surgical planning. The state-of-the-art deep learning-based methods often simply concatenate the raw geometric attributes (i.e., coordinates and normal vectors) of mesh cells to train a single-stream network for automatic intra-oral scanner image segmentation. However, since different raw attributes reveal completely different geometric information, the naive concatenation of different raw attributes at the (low-level) input stage may bring unnecessary confusion in describing and dif…
Yonsei University（延世大学）；National Institute for Mathematical Sciences（国家数学科学研究所）
Low-dose dental cone beam computed tomography (CBCT) has been increasingly used for maxillofacial modeling. However, the presence of metallic inserts, such as implants, crowns, and dental filling, causes severe streaking and shading artifacts in a CBCT image and loss of the morphological structures of the teeth, which consequently prevents accurate segmentation of bones. A two-stage metal artifact reduction method is proposed for accurate 3D low-dose maxillofacial CBCT modeling, where a key idea is to utilize explicit tooth shape prior information from intra-oral scan data whose acquisition do…
Object Detection for Caries or Pit and Fissure Sealing Requirement in Children's First Permanent Molars
Tsinghua-Berkeley Shenzhen Institute（清华-伯克利深圳学院）；Tsinghua Shenzhen International Graduate School（清华大学深圳国际研究生院）；Southern Medical University（南方医科大学）；Shandong University（山东大学）；Nanshan Center for Chronic Disease Control（深圳市南山区慢性病防治中心）
Artificial Intelligence to Assess Dental Findings from Panoramic Radiographs -- A Multinational Study
National Taiwan University（国立台湾大学）；National Taiwan University of Science and Technology（国立台湾科技大学）；Radboud University Medical Center（拉德堡德大学医学中心）；The Ohio State University（俄亥俄州立大学）；National Taiwan University Hospital（国立台湾大学附属医院）
FU-Mamba: A Frequency-Enhanced Dynamic Scanning Framework for Oralscan Image Segmentation
School of Computer Science and Technology, Zhejiang Gongshang University（浙江工商大学计算机科学与技术学院）；Tongxiang Hospital of Traditional Chinese Medicine（桐乡市中医院）；Prince Sattam Bin Abdulaziz University（萨塔姆·本·阿卜杜勒阿齐兹王子大学）；Qena University（奎纳大学）；Tampere University（坦佩雷大学）；Universidade Federal Fluminense（弗卢米嫩塞联邦大学）；Federal University of Ceara（塞阿拉联邦大学）；Firat University（菲拉特大学）；Systems Research Institute, Polish Academy of Sciences（波兰科学院系统研究所）；AGH University of Krakow（克拉科夫AGH大学）
Innovative Tooth Segmentation Using Hierarchical Features and Bidirectional Sequence Modeling
School of Computer Science and Technology, Zhejiang Gongshang University（浙江工商大学计算机科学与技术学院）；School of Computer and Cyber Sciences, Communication University of China（中国传媒大学计算机与 cyber 科学学院）；Center of Big Data, China Digital Culture Group Co., Ltd（中国数字文化集团大数据中心）；Department of Stomatology, Tongxiang Hospital of Traditional Chinese Medicine（同祥中医医院口腔科）；School of Mathematical and Computational Sciences, Massey University（马希大学数学与计算科学学院）；Shining3D Tech Co., Ltd.（Shining3D科技有限公司）；School of Art Design, Zhejiang Gongshang University（浙江工商大学艺术设计学院）；Zhejiang Key Laboratory of Big Data and Future E-Commerce Technology（浙江大数据与未来电子商务技术重点实验室）
ViT-NeBLa: A Hybrid Vision Transformer and Neural Beer-Lambert Framework for Single-View 3D Reconstruction of Oral Anatomy from Panoramic Radiographs
Artificial Intelligence and Image Processing Lab., Department of Information and Communication Engineering, Sun Moon University, South Korea（人工智能与图像处理实验室，信息与通信工程系，顺 Moon 大学，韩国）；Department of Physics and Engineering Physics, Tulane University, New Orleans, LA 70118, USA（物理系与工程物理系，路易斯安那大学，新奥尔良，路易斯安那州 70118，美国）
National Institute for Mathematical Sciences（国家数学科学研究所）；Yonsei University（延世大学）
This paper describes the mathematical structure of the ill-posed nonlinear inverse problem of low-dose dental cone-beam computed tomography (CBCT) and explains the advantages of a deep learning-based approach to the reconstruction of computed tomography images over conventional regularization methods. This paper explains the underlying reasons why dental CBCT is more ill-posed than standard computed tomography. Despite this severe ill-posedness, the demand for dental CBCT systems is rapidly growing because of their cost competitiveness and low radiation dose. We then describe the limitations o…
Geometric Deep Learning for Automated Landmarking of Maxillary Arches on 3D Oral Scans from Newborns with Cleft Lip and Palate
Stanford University School of Medicine（斯坦福大学医学院）；Children’s National Hospital（儿童国家医院）；George Washington University（乔治华盛顿大学）；Lucile Packard Children’s Hospital Stanford（露西尔帕卡德斯坦福儿童医院）
