Results 1 to 10 of about 48,274 (263)

Deep Learning-Based Automated Anatomical Landmark Detection and Saw Blade Size Prediction for Canine Tibial Plateau Leveling Osteotomy [PDF]

Animals
Objective: To develop and validate a fully automated deep learning workflow that localizes key anatomical landmarks on standard canine hindlimb lateral radiographs, derives the tibial plateau angle (TPA), and recommends a saw blade size for tibial ...
Tea Hyung Kim, Ji Yun Lee, Hwi Yool Kim
doaj   +2 more sources

Artificial Intelligence-based Automated Anatomic Landmark Detection and Image Deformation for Nonsmall Lung Cancer Cases [PDF]

Journal of Medical Physics
Background: Adaptive radiotherapy for non-small cell lung cancer (NSCLC) requires accurate image registration to account for anatomical changes during treatment.
Siti Zulaikho, Ilga Pradipta Dyah Prameswara, Nelly, Frasisca Yulia Dimitri, Erlinda Ratnasari Putri, Akbar Azzi   +5 more
doaj   +2 more sources

Siamese comparative transformer-based network for unsupervised landmark detection. [PDF]

PLoS ONE
Landmark detection is a common task that benefits downstream computer vision tasks. Current landmark detection algorithms often train a sophisticated image pose encoder by reconstructing the source image to identify landmarks.
Can Zhao, Tao Wu, Jianlin Zhang, Zhiyong Xu, Meihui Li, Dongxu Liu   +5 more
doaj   +2 more sources

Automated Bony Landmark Detection on Foot Radiographs: Facilitating Linear and Angular Measurements [PDF]

Foot & Ankle Orthopaedics
Research Type: Level 3 - Retrospective cohort study, Case-control study, Meta-analysis of Level 3 studies Introduction/Purpose: Accurate assessment of foot alignment on radiographs relies on precise identification of key bony landmarks. Manual annotation
Atta Taseh MD, Junjie Li MS, Shrestha Mishra, Carl Rai MD, Ronit Avadhuta, Matthias Peiffer MD, PhD, Bedri Karaismailoglu MD, FEBOT, PhDc, Gregory Waryasz MD, Lorena Bejarano-Pineda MD, Christopher Miller MD, Soheil Ashkani-Esfahani MD, MPH, PhDc   +10 more
doaj   +2 more sources

Multi-Scale 3D Cephalometric Landmark Detection Based on Direct Regression with 3D CNN Architectures [PDF]

Diagnostics
Background: Cephalometric analysis is important in diagnosing and planning treatments for patients, traditionally relying on 2D cephalometric radiographs.
Chanho Song, Yoosoo Jeong, Hyungkyu Huh, Jee-Woong Park, Jun-Young Paeng, Jaemyung Ahn, Jaebum Son, Euisung Jung   +7 more
doaj   +2 more sources

A Multi-Task Based Deep Learning Framework With Landmark Detection for MRI Couinaud Segmentation [PDF]

IEEE Journal of Translational Engineering in Health and Medicine
To achieve precise Couinaud liver segmentation in preoperative planning for hepatic surgery, accommodating the complex anatomy and significant variations, optimizing surgical approaches, reducing postoperative complications, and preserving liver function.
Dong Miao, Ying Zhao, Xue Ren, Meng Dou, Yu Yao, Yiran Xu, Yingchao Cui, Ailian Liu   +7 more
doaj   +2 more sources

HybridMatch: Semi-Supervised Facial Landmark Detection via Hybrid Heatmap Representations

IEEE Access, 2023
Facial landmark detection is an essential task in face-processing techniques. Traditional methods, however, require expensive pixel-level labels. Semi-supervised facial landmark detection has been explored as an alternative, but previous approaches only ...
Seoungyoon Kang, Minhyun Lee, Minjae Kim, Hyunjung Shim   +3 more
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Detecting Facial Region and Landmarks at Once via Deep Network

Sensors, 2021
For accurate and fast detection of facial landmarks, we propose a new facial landmark detection method. Previous facial landmark detection models generally perform a face detection step before landmark detection.
Taehyung Kim, Jiwon Mok, Euichul Lee
doaj   +1 more source

Design of a Facial Landmark Detection System Using a Dynamic Optical Flow Approach

IEEE Access, 2021
Many facial landmark methods based on convolutional neural networks (CNN) have been proposed to achieve favorable detection results. However, the instability landmarks that occur in video frames due to CNNs are extremely sensitive to input image noise ...
Bing-Fei Wu, Bo-Rui Chen, Chun-Fei Hsu
doaj   +1 more source

An Automatic Assessment Model of Adenoid Hypertrophy in MRI Images Based on Deep Convolutional Neural Networks

IEEE Access, 2023
Adenoid hypertrophy is a pathological condition characterized by the enlargement of the adenoids in children, which may lead to various problems. The conventional manual measurement is time-consuming and subject to subjective errors.
Ziling He, Yubin Xiao, Xuan Wu, Yanchun Liang, You Zhou, Guanghui An   +5 more
doaj   +1 more source