ESTRO 37 Abstract book

S1174

ESTRO 37

Conclusion Half-size amplitude binning reduced the reconstructed motion underestimation with no loss in 4DMRI quality compared with equal-size amplitude binning. EP-2127 Feasibility of Automatic Multi-Atlas Based Cardiac Segmentation in Planning CT R.N. Finnegan 1,2 , J.A. Dowling 1,3,4,5,6 , L. Holloway 1,2,7,8,9 , J. Otton 7 , E.S. Koh 7 , C. Luo 6 , A. Satchithanandha 6 , P. Atluri 10 , S. Tang 8,11 , G.P. Delaney 6,7,8,11 , V. Batumalai 2,7,8 , D.I. Thwaites 1 University of Sydney, School of Physics- Institute of Medical Physics, Sydney, Australia 2 Ingham Institute of Applied Medical Research, Medical Physics Research, Liverpool, Australia 3 The Australian e-Health and Research Centre, CSIRO Health and Biosecurity, Herston, Australia 4 University of Wollongong, Faculty of Engineering and Information Sciences, Wollongong, Australia 5 University of Newcastle, School of Mathematical and Physical Sciences, Newcastle, Australia 6 University of New South Wales, Faculty of Medicine, Sydney, Australia 7 University of New South Wales, South Western Sydney Clinical School, Sydney, Australia 8 Liverpool & Macarthur Cancer Therapy Centres, Department of Radiation Oncology, Liverpool, Australia 9 University of Wollongong, Centre for Medical Radiation Physics, Wollongong, Australia 10 University of Texas, Department of Molecular and Cell Biology, Dallas, USA 11 Ingham Institute of Applied Medical Research, Clinical Radiation Oncology, Liverpool, Australia Purpose or Objective Toxicity to cardiac and coronary structures is an important late morbidity for patients undergoing left- sided breast radiotherapy. Past studies [1] have relied on estimates of cardiac doses assuming standardised anatomy. Development of an automatic cardiac segmentation tool would allow individualised RT dose estimates to the whole heart and cardiac substructures. Material and Methods A dataset consisting of 20 planning computed tomography (CT) images were manually contoured by 3 independent observers; twenty structures (including whole heart, 4 chambers, coronary arteries and valves) were contoured for each image. Each observer followed a protocol based on a published reference atlas [2], and the contours were validated by a cardiologist before use in this study. To develop and validate an automatic multi-atlas- based segmentation framework a 'leave-one-out” cross- validation strategy was employed. Given a segmented atlas image it is possible to transfer the contours onto a given unsegmented target image by finding a transformation that registers the two images. This image registration is performed by first aligning the atlas and target image using a rigid transform and calculating a locally optimal transformation using deformable image registration. The manual segmentation is propagated using the same transformation and gives a potential segmentation of the target image. A comparison of two popular deformable image registration schemes: free- form (B-spline) and symmetric diffeomophic demons- based registration was investigated, with the demons producing superior registration accuracy as measured by image similarity metrics. After registration of a number of atlas images the set of propagated contours are combined using a patch-based locally weighted label fusion scheme. Measures of volume and surface accuracy (Dice similarity coefficient (DSC) and mean absolute surface distance (MASD), respectively) were used to compare automatic segmentation to the consensus segmentation from manual contours. Evaluation of the performance of the

automatic segmentation is performed relative to inter- observer variability in manually-derived contours. Results For the whole heart, 4 chambers and large vessels the automatic delineation performs well when compared to the inter-observer variability (see Table 1). However, for smaller structures such as coronary arteries, accurate delineation may not be feasible due to large segmentation uncertainties.

Conclusion The results show promise that larger structures can be accurately segmented automatically, however for smaller structures a model-based approach may be more feasible. [1] Darby SC, et al. (2013) Risk of ischemic heart disease in women after radiotherapy for breast cancer. N. Engl. J. Med. 368(11):987–998 [2] VB: Feng, M., et al., 2011. Development and validation of a heart atlas to study cardiac exposure to radiation following treatment for breast cancer. International Journal of Radiation Oncology* Biology* Physics, 79(1), pp.10-18. EP-2128 Investigating a new MR sequence for prostate delineation for radiotherapy J. Wyatt 1 , J. Frew 1 , A. Henry 2,3 , L. Murray 2 , R. Pearson 1 , E. Johnstone 2 , R. Speight 3 , H. McCallum 1 1 Newcastle upon Tyne Hospitals NHS Foundation Trust, Northern Centre for Cancer Care, Newcastle upon Tyne, United Kingdom 2 Leeds University, Leeds Institute of Cancer and Pathology, Leeds, United Kingdom 3 Leeds Teaching Hospitals NHS Trust, Leeds Cancer Centre, Leeds, United Kingdom Purpose or Objective MR is increasingly being used for prostate delineation in radiotherapy due to its superior soft-tissue contrast. However acquisition sequences affect the image contrast and the literature is scarce regarding the optimal sequence for prostate definition. A 3D T2-weighted turbo spin echo sequence (SPACE) is commonly used but in our institution a 2D T2-weighted combined multiple gradient echo sequence (MEDIC) is preferred for prostate radiotherapy. This study aimed to compare the variability and efficiency of prostate delineation using the MEDIC sequence to a SPACE sequence and CT. Material and Methods CT and MRI scans for radiotherapy planning were acquired in the treatment planning position in five patients. Two MR sequences were acquired in the same scanning session, the 3D SPACE and the 2D MEDIC. Four consultant oncologists from two institutions delineated the prostate and seminal vesicles on each image set independently, using the same treatment planning system and recording the time taken.A similarity measure of the intersection volume divided by the union volume of all four delineations was calculated for each image and patient. The mean volume of the delineations for each image and