Semi-automated left ventricular segmentation based on a guide point model approach for 3D cine DENSE cardiovascular magnetic resonance

Auger, Daniel A.; Zhong, Xiaodong; Epstein, Frederick H.; Meintjes, Ernesta M.; Spottiswoode, Bruce

doi:10.1186/1532-429x-16-8

Cited by 23 publications

(17 citation statements)

References 32 publications

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“…Furthermore, most of the current atlas-based methods use a limited number of cardiac atlases (less than 15) and it is not clear how many cardiac atlases are optimal. It is also essential to check if manual guide points [15] would improve performance. segmentation method for performing cardiac substructure dose calculations on a large number of patients as needed for epidemiological studies or clinical trials.…”

Section: Introductionmentioning

confidence: 99%

Automatic segmentation of cardiac structures for breast cancer radiotherapy

Jung

Lee

Mosher

et al. 2019

Physics and Imaging in Radiation Oncology

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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.

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Section: Introductionmentioning

confidence: 99%

Automatic segmentation of cardiac structures for breast cancer radiotherapy

Jung

Lee

Mosher

et al. 2019

Physics and Imaging in Radiation Oncology

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“…As of today, the clinical use of cardiovascular MRI is hampered by the amount of data to be processed (often more than 10 short axis slices and more than 20 phases per slice). Since the manual delineation of all 3D images is clinically impracticable, several semi-automatic methods have been proposed, most of which being based on active contours, dynamic programming, graph cut or some atlas fitting strategies [4,5,3,6,7]. Unfortunately, these methods are far from real time due to the manual interaction which they require.…”

Section: Introductionmentioning

confidence: 99%

GridNet with Automatic Shape Prior Registration for Automatic MRI Cardiac Segmentation

Zotti

Luo

Humbert

et al. 2018

Lecture Notes in Computer Science

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In this paper, we propose a fully automatic MRI cardiac segmentation method based on a novel deep convolutional neural network (CNN) designed for the 2017 ACDC MICCAI challenge. The novelty of our network comes with its embedded shape prior and its loss function tailored to the cardiac anatomy. Our model includes a cardiac centerof-mass regression module which allows for an automatic shape prior registration. Also, since our method processes raw MR images without any manual preprocessing and/or image cropping, our CNN learns both high-level features (useful to distinguish the heart from other organs with a similar shape) and low-level features (useful to get accurate segmentation results). Those features are learned with a multi-resolution conv-deconv "grid" architecture which can be seen as an extension of the U-Net. Experimental results reveal that our method can segment the left and right ventricles as well as the myocardium from a 3D MRI cardiac volume in 0.4 second with an average Dice coefficient of 0.90 and an average Hausdorff distance of 10.4 mm.

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“…Semiautomatic LV contour tracking has been used previously in displacement‐encoded images . Moreover, the concept of using velocity‐guided segmentation of the myocardium is not new .…”

Section: Discussionmentioning

confidence: 99%