Automatic segmentation of cardiac structures for breast cancer radiotherapy

Abstract

A B S T R A C T Background and purpose:We developed an automatic method to segment cardiac substructures given a radiotherapy planning CT images to support epidemiological studies or clinical trials looking at cardiac disease endpoints after radiotherapy. Material and methods: We used a most-similar atlas selection algorithm and 3D deformation combined with 30 detailed cardiac atlases. We cross-validated our method within the atlas library by evaluating geometric comparison metrics and by comparing cardiac doses for simulated breast radiotherapy between manual and automatic contours. We analyzed the impact of the number of cardiac atlas in the library and the use of manual guide points on the performance of our method. Results: The Dice Similarity Coefficients from the cross-validation reached up to 97% (whole heart) and 80% (chambers). The Average Surface Distance for the coronary arteries was less than 10.3 mm on average, with the best agreement (7.3 mm) in the left anterior descending artery (LAD). The dose comparison for simulated breast radiotherapy showed differences less than 0.06 Gy for the whole heart and atria, and 0.3 Gy for the ventricles. For the coronary arteries, the dose differences were 2.3 Gy (LAD) and 0.3 Gy (other arteries). The sensitivity analysis showed no notable improvement beyond ten atlases and the manual guide points does not significantly improve performance. Conclusion: We developed an automated method to contour cardiac substructures for radiotherapy CTs. When combined with accurate dose calculation techniques, our method should be useful for cardiac dose reconstruction of a large number of patients in epidemiological studies or clinical trials. The purpose of the current study was to develop an automatic https://doi.

Application of an automatic segmentation method for evaluating cardiac structure doses received by breast radiotherapy patients

“…The automatic segmentation applied in the present study was the same as described in our previous publication [28] except that we used the segmentations of 30 RadComp patients as the cardiac atlas library, not diagnostic CT images.…”

“…However, these methods are often demonstrated on contrast-enhanced cardiac CT angiography or magnetic resonance imaging (MRI) datasets whereas the visibility of cardiac substructures on a radiotherapy planning CT can be considerably worse. To help bridge this gap, we previously developed a method to automatically segment the substructures of the heart using a most-similar atlas approach followed by a B-spline transformation [28] . Our segmentation method has the advantage that it depends only on the pre-contoured whole heart, not the quality of CT images.…”

Validation of separate multi-atlases for auto segmentation of cardiac substructures in CT-scans acquired in deep inspiration breath hold and free breathing

“…Jung et al [20] showed that the performance of their multi-atlas (based on contrast-enhanced FB scanned patients) for contouring of cardiac structures plateaued for libraries containing more than 10 atlases. First, an FB multi-atlas with consensus contours of 10 patients from the FB atlas group (i.e.…”

Towards spatial representations of dose distributions to predict risk of normal tissue morbidity after radiotherapy

“…Auto-contouring not only allows us to tackle a laborious task of delineating structures which are not routinely delineated in clinical practice, it may also improve consistency [21] . Additionally, auto-contouring tools included in retrospective analysis will pave the way to interrogating outcomes data and will further promote prospective use for selective regional sparing [22] .…”