NDLI: Automated anatomical landmark detection ondistal femur surface using convolutional neural network

Content Provider	IEEE Xplore Digital Library
Author	Dong Yang Shaoting Zhang Zhennan Yan Chaowei Tan Kang Li Metaxas, D.
Copyright Year	2015
Description	Author affiliation: Dept. of Comput. Sci., Univ. of North Carolina at Charlotte, Charlotte, NC, USA (Shaoting Zhang) \|\| Dept. of Ind. & Syst. Eng., Rutgers Univ., Piscataway, NJ, USA (Kang Li) \|\| CBIM, Rutgers Univ., Piscataway, NJ, USA (Dong Yang; Zhennan Yan; Chaowei Tan; Metaxas, D.)
Abstract	Accurate localization of the anatomical landmarks on distal femur bone in the 3D medical images is very important for knee surgery planning and biomechanics analysis. However, the landmark identification process is often conducted manually or by using the inserted auxiliaries, which is time-consuming and lacks of accuracy. In this paper, an automatic localization method is proposed to determine positions of initial geometric landmarks on femur surface in the 3D MR images. Based on the results from the convolutional neural network (CNN) classifiers and shape statistics, we use the narrow-band graph cut optimization to achieve the 3D segmentation of femur surface. Finally, the anatomical landmarks are located on the femur according to the geometric cues of surface mesh. Experiments demonstrate that the proposed method is effective, efficient, and reliable to segment femur and locate the anatomical landmarks.
Starting Page	17
Ending Page	21
File Size	942215
Page Count	5
File Format	PDF
ISBN	9781479923748
DOI	10.1109/ISBI.2015.7163806
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-04-16
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Three-dimensional displays Biomedical imaging Image segmentation Shape Training Bones Neural networks mesh curvature Deep learning anatomical landmark detection convolutional neural network graph cut
Content Type	Text
Resource Type	Article

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

Positioning of anatomical landmarks in orthopedics by MESH registration

Fully automatic and fast segmentation of the femur bone from 3D-CT images with no shape prior

A Bayesian formulation of graph-cut surface estimation with global shape priors

Anatomical Landmark Detection in Medical Applications Driven by Synthetic Data

Deep neural networks for anatomical brain segmentation

A deep-learning approach for direct whole-heart mesh reconstruction

Consistent Mesh Segmentation Using Protrusion Function and Graph Cut

Scene Cut: Class-Specific Object Detection and Segmentation in 3D Scenes

Unsupervised Segmentation and Quantification of Anatomical Knee Features: Data From the Osteoarthritis Initiative

Automated anatomical landmark detection ondistal femur surface using convolutional neural network

Similar Documents

Positioning of anatomical landmarks in orthopedics by MESH registration

Fully automatic and fast segmentation of the femur bone from 3D-CT images with no shape prior

A Bayesian formulation of graph-cut surface estimation with global shape priors

Anatomical Landmark Detection in Medical Applications Driven by Synthetic Data

Deep neural networks for anatomical brain segmentation

A deep-learning approach for direct whole-heart mesh reconstruction

Consistent Mesh Segmentation Using Protrusion Function and Graph Cut

Scene Cut: Class-Specific Object Detection and Segmentation in 3D Scenes

Unsupervised Segmentation and Quantification of Anatomical Knee Features: Data From the Osteoarthritis Initiative

Automated anatomical landmark detection ondistal femur surface using convolutional neural network