Semi-supervised segmentation using non-parametric snakes for 3D-CT applications in radiation oncology

Jayashree Kalpathy-Cramer, Umut Ozertem, William Hersh, Martin Fuss, Deniz Erdogmus

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations

Abstract

We present a semi-supervised protocol for segmentation of tumors and normal anatomy for applications in Radiation Oncology. A primary goal in radiation therapy in oncology is to deliver high radiation dose to the perceived tumor while sparing the surrounding non-diseased organs. Consequently, a critical task in the workflow of radiation oncologists is the manual delineation of normal and diseased structures on 3D-CT scans. In this paper, we compare the results using a non-parametric snake technique with a gold standard consisting of manually delineated structures. Structures include tumors as well as normal organs including lungs, liver and kidneys. This technique provides fast segmentation that is robust with respect to noisy edges. In addition, this algorithm does not require the user to optimize a variety of parameters unlike many segmentation algorithms. We provide results that show the improvement in overlap between the manually delineated gold standard and the output of the segmentation algorithms using the user input.

Original languageEnglish (US)
Title of host publicationProceedings of the 2008 IEEE Workshop on Machine Learning for Signal Processing, MLSP 2008
Pages109-114
Number of pages6
DOIs
StatePublished - Dec 1 2008
Event2008 IEEE Workshop on Machine Learning for Signal Processing, MLSP 2008 - Cancun, Mexico
Duration: Oct 16 2008Oct 19 2008

Publication series

NameProceedings of the 2008 IEEE Workshop on Machine Learning for Signal Processing, MLSP 2008

Other

Other2008 IEEE Workshop on Machine Learning for Signal Processing, MLSP 2008
CountryMexico
CityCancun
Period10/16/0810/19/08

ASJC Scopus subject areas

  • Artificial Intelligence
  • Software
  • Electrical and Electronic Engineering

Fingerprint Dive into the research topics of 'Semi-supervised segmentation using non-parametric snakes for 3D-CT applications in radiation oncology'. Together they form a unique fingerprint.

  • Cite this

    Kalpathy-Cramer, J., Ozertem, U., Hersh, W., Fuss, M., & Erdogmus, D. (2008). Semi-supervised segmentation using non-parametric snakes for 3D-CT applications in radiation oncology. In Proceedings of the 2008 IEEE Workshop on Machine Learning for Signal Processing, MLSP 2008 (pp. 109-114). [4685464] (Proceedings of the 2008 IEEE Workshop on Machine Learning for Signal Processing, MLSP 2008). https://doi.org/10.1109/MLSP.2008.4685464