Feasibility of multi-atlas cardiac segmentation from thoracic planning CT in a probabilistic framework

Robert Finnegan, Jason Dowling, Eng Siew Koh, Simon Tang, James Otton, Geoff Delaney, Vikneswary Batumalai, Carol Luo, Pramukh Atluri, Athiththa Satchithanandha, David Thwaites, Lois Holloway

Research output: Contribution to journalArticlepeer-review

38 Citations (Scopus)

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 manuallyderived 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 0.944 +0.024, with a MASD of .726 + 1.363 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 interobserver variability in manual contouring.

Original languageEnglish
Article number085006
Pages (from-to)1-20
Number of pages20
JournalPhysics in Medicine and Biology
Volume64
Issue number8
DOIs
Publication statusPublished - 8 Apr 2019
Externally publishedYes

Fingerprint

Dive into the research topics of 'Feasibility of multi-atlas cardiac segmentation from thoracic planning CT in a probabilistic framework'. Together they form a unique fingerprint.

Cite this