New level set model in follow up radiotherapy image analysis

Roushanak Rahmat, William Henry Nailon, Allan Price, David Harris-Birtill, Stephen McLaughlin

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

1 Citation (Scopus)
31 Downloads (Pure)

Abstract

In cancer treatment by means of radiation therapy having an accurate estimation of tumour size is vital. At present, the tumour shape and boundaries are defined manually by an oncologist as this cannot be achieved using automatic image segmentation techniques. Manual contouring is tedious and not reproducible, e.g. different oncologists do not identify exactly the same tumour shape for the same patient. Although the tumour changes shape during the treatment due to effect of radiotherapy (RT) or progression of the cancer, follow up treatments are all based on the first gross tumour volume (GTV) shape of the tumour delineated before treatment started. Re-contouring at each stage of RT is more complicated due to less image information being available and less time for re-contouring by the oncologist. The absence of gold standards for these images makes it a particularly challenging problem to find the best parameters for any segmentation model. In this paper a level set model is designed for the follow up RT image segmentation. In this contribution instead of re-initializing the same model for level sets in vector-image or multi-phase applications, a combination of the two best performing models or the same model with different sets of parameters can result in better performance with less reliance on specific parameter settings.

Original languageEnglish
Title of host publicationMedical Image Understanding and Analysis
Subtitle of host publication21st Annual Conference, MIUA 2017, Edinburgh, UK, July 11–13, 2017, Proceedings
EditorsMaria Valdes Hernandez, Victor González-Castro
PublisherSpringer International Publishing
Pages273-284
Number of pages12
Volume723
ISBN (Electronic)978-3-319-60964-5
ISBN (Print)978-3-319-60963-8
DOIs
Publication statusE-pub ahead of print - 22 Jun 2017
Event21st Annual Conference on Medical Image Understanding and Analysis 2017 - Edinburgh, United Kingdom
Duration: 11 Jul 201713 Jul 2017

Publication series

NameCommunications in Computer and Information Science
Volume723
ISSN (Print)1865-0929

Conference

Conference21st Annual Conference on Medical Image Understanding and Analysis 2017
Abbreviated titleMIUA 2017
CountryUnited Kingdom
CityEdinburgh
Period11/07/1713/07/17

ASJC Scopus subject areas

  • Computer Science(all)

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