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

Abstract / Description of output

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 languageUndefined/Unknown
Title of host publicationCommunications in Computer and Information Science
Subtitle of host publicationMedical Image Understanding and Analysis. MIUA 2017.
Number of pages12
ISBN (Electronic)978-3-319-60964-5
ISBN (Print)978-3-319-60963-8
Publication statusPublished - 22 Jun 2017
EventMedical Image Understanding and Analysis (MIUA 2017) - Edinburgh, United Kingdom
Duration: 11 Jul 201713 Jul 2017


ConferenceMedical Image Understanding and Analysis (MIUA 2017)
Country/TerritoryUnited Kingdom

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