NDLI: Groupwise Conditional Random Forests for Automatic Shape Classification and Contour Quality Assessment in Radiotherapy Planning

Content Provider	IEEE Xplore Digital Library
Author	McIntosh, C. Svistoun, I. Purdie, T.G.
Copyright Year	1982
Abstract	Radiation therapy is used to treat cancer patients around the world. High quality treatment plans maximally radiate the targets while minimally radiating healthy organs at risk. In order to judge plan quality and safety, segmentations of the targets and organs at risk are created, and the amount of radiation that will be delivered to each structure is estimated prior to treatment. If the targets or organs at risk are mislabelled, or the segmentations are of poor quality, the safety of the radiation doses will be erroneously reviewed and an unsafe plan could proceed. We propose a technique to automatically label groups of segmentations of different structures from a radiation therapy plan for the joint purposes of providing quality assurance and data mining. Given one or more segmentations and an associated image we seek to assign medically meaningful labels to each segmentation and report the confidence of that label. Our method uses random forests to learn joint distributions over the training features, and then exploits a set of learned potential group configurations to build a conditional random field (CRF) that ensures the assignment of labels is consistent across the group of segmentations. The CRF is then solved via a constrained assignment problem. We validate our method on 1574 plans, consisting of 17 579 segmentations, demonstrating an overall classification accuracy of 91.58%. Our results also demonstrate the stability of RF with respect to tree depth and the number of splitting variables in large data sets.
Sponsorship	IEEE Engineering in Medicine and Biology Society IEEE Nuclear and Plasma Sciences Society IEEE Signal Processing Society IEEE Ultrasonics, Ferroelectrics, and Frequency Control Society
Page Count	15
File Size	2081614
Starting Page	1043
Ending Page	1057
File Format	PDF
ISSN	02780062
Volume Number	32
Issue Number	6
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2013-01-01
Publisher Place	U.S.A.
Access Restriction	One Nation One Subscription (ONOS)
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Lungs Heart Radio frequency Shape Biomedical imaging Planning Cancer shape analysis Data mining decision forests machine learning pattern recognition and classification radiation therapy random forests
Content Type	Text
Resource Type	Article
Subject	Electrical and Electronic Engineering Computer Science Applications Radiological and Ultrasound Technology Software

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

Automatic contour definition on left ventriculograms by image evidence and a multiple template-based model

Large-scale Gaussian process classification using random decision forests

Automatic Tuberculosis Screening Using Chest Radiographs

Approach and assessment of automated stereotactic radiotherapy planning for early stage non-small-cell lung cancer

Heart Rate Variability Classification using Support Vector Machine and Genetic Algorithm.

Random forest for automatic assessment of heart failure severity in a telemonitoring scenario

GeoF: Geodesic Forests for Learning Coupled Predictors

Image Classification using Random Forests and Ferns

Fully Automatic Segmentation of the Proximal Femur Using Random Forest Regression Voting

Groupwise Conditional Random Forests for Automatic Shape Classification and Contour Quality Assessment in Radiotherapy Planning

Similar Documents

Automatic contour definition on left ventriculograms by image evidence and a multiple template-based model

Large-scale Gaussian process classification using random decision forests

Automatic Tuberculosis Screening Using Chest Radiographs

Approach and assessment of automated stereotactic radiotherapy planning for early stage non-small-cell lung cancer

Heart Rate Variability Classification using Support Vector Machine and Genetic Algorithm.

Random forest for automatic assessment of heart failure severity in a telemonitoring scenario

GeoF: Geodesic Forests for Learning Coupled Predictors

Image Classification using Random Forests and Ferns

Fully Automatic Segmentation of the Proximal Femur Using Random Forest Regression Voting

Groupwise Conditional Random Forests for Automatic Shape Classification and Contour Quality Assessment in Radiotherapy Planning