Feasibility of multi-atlas cardiac segmentation from thoracic planning CT in a probabilistic framework. (8th April 2019)

• Record Type: Journal Article
• Authors: Finnegan, Robert; Dowling, Jason; Koh, Eng-Siew; Tang, Simon; Otton, James; Delaney, Geoff; Batumalai, Vikneswary; Luo, Carol; Atluri, Pramukh; Satchithanandha, Athiththa; Thwaites, David; Holloway, Lois
• Is Part Of: Physics in medicine & biology. Volume 64:Number 8(2019:Apr.)
• Publication Date: 2019-04-08
• Subjects: atlas-based segmentation -- heart contouring -- whole heart segmentation -- medical image processing -- computed tomography -- image registration; Biophysics -- Periodicals; Medical physics -- Periodicals; 610.153
• DOI: 10.1088/1361-6560/ab0ea6
• Languages: English
• ISSNs: 0031-9155

Abstract

Abstract: Toxicity to cardiac and coronary structures is an important late morbidity for patients undergoing left-sided breast radiotherapy. Many current studies have relied on estimates of cardiac doses assuming standardised anatomy, with a calculated increase in relative risk of 7.4% per Gy (mean heart dose). To provide individualised estimates for dose, delineation of various cardiac structures on patient images is required. Automatic multi-atlas based segmentation can provide a consistent, robust solution, however there are challenges to this method. We are aiming to develop and validate a cardiac atlas and segmentation framework, with a focus on the limitations and uncertainties in the process. We present a probabilistic approach to segmentation, which provides a simple method to incorporate inter-observer variation, as well as a useful tool for evaluating the accuracy and sources of error in segmentation. A dataset consisting of 20 planning computed tomography (CT) images of Australian breast cancer patients with delineations of 17 structures (including whole heart, four chambers, coronary arteries and valves) was manually contoured by three independent observers, following a protocol based on a published reference atlas, with verification by a cardiologist. To develop and validate the segmentation framework a leave-one-out cross-validation strategy was implemented. Performance of the automatic segmentations was evaluated relative to inter-observer variability in manually-derived contours; 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. For the whole heart, the resulting segmentation achieved a DSC of, with a MASD of mm. Quantitative results, together with the analysis of probabilistic labelling, indicate the feasibility of accurate and consistent segmentation of larger structures, whereas this is not the case for many smaller structures, where a major limitation in segmentation accuracy is the inter-observer variability in manual contouring.