Fast Automatic Detection of Calcified Coronary Lesions in 3D Cardiac CT Images

Mittal, Susheel K.; Zheng, Yefeng; Georgescu, Bogdan; Vega-Higuera, Fernando; Zhou, S. Kevin; Meer, Peter; Comanicìu, Dorin

doi:10.1007/978-3-642-15948-0_1

Cited by 24 publications

(24 citation statements)

References 13 publications

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“…1) Intensity stencil: We computed intensity stencils [12], [26] around each keypoint. As shown in Figure 2, we align the stencil with the keypoint orientation and measure the distance between the sampling points in units of scale instead of pixels.…”

Section: Keypoint Learningmentioning

confidence: 99%

Automatic Cell Detection in Bright-Field Microscope Images Using SIFT, Random Forests, and Hierarchical Clustering

Mualla

Scholl

Sommerfeldt

et al. 2013

IEEE Trans. Med. Imaging

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Abstract-We present a novel machine learning-based system for unstained cell detection in bright-field microscope images. The system is fully automatic since it requires no manual parameter tuning. It is also highly invariant with respect to illumination conditions and to the size and orientation of cells. Images from two adherent cell lines and one suspension cell line were used in the evaluation for a total number of more than 3500 cells. Besides real images, simulated images were also used in the evaluation. The detection error was between approximately zero and 15.5% which is a significantly superior performance compared to baseline approaches.

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Section: Keypoint Learningmentioning

confidence: 99%

Automatic Cell Detection in Bright-Field Microscope Images Using SIFT, Random Forests, and Hierarchical Clustering

Mualla

Scholl

Sommerfeldt

et al. 2013

IEEE Trans. Med. Imaging

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“…In the past, a variety of algorithms have been proposed for detection of coronary plaques in CCTA. However, most of this work focuses on the detection of calcified plaques only, e.g., see [14,11]. Fewer methods have been proposed for fully automatic detection of non-calcified plaques, which are usually harder to detect and grade with high confidence.…”

Section: Related Workmentioning

confidence: 99%

“…Therefore, we propose to use a learning based algorithm for automatic detection of non-coronary regions along the extracted centerlines. Similar to [11], a cylindrical sampling pattern for feature extraction, with its axis aligned to the coronary centerline, is employed. We then extract altogether 171 rotation invariant features along the entire length of the cylinder at varying radii.…”

Section: Centerline Verificationmentioning

confidence: 99%

“…The training set is constructed from semimanual lumen segmentations of coronary arteries by computing cross-sections and the corresponding radii r i at altogether N centerline points. At the same points, rotationinvariant features x i are extracted according to [11]. A regression function is learned by minimizing the squared loss…”

Section: Lumen Estimationmentioning

confidence: 99%

“…Finally, using probability scores obtained from two classifiers for the detection of calcified and non-calcified plaques similar to [11] (Fig. 3, cyan/orange), each accepted stenosis candidate is classified into one of three types, "calcified", "non-calcified" and "mixed"' (calcified as well as non-calcified parts).…”

Section: Stenosis Detection and Classificationmentioning

confidence: 99%

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Detection, Grading and Classification of Coronary Stenoses in Computed Tomography Angiography

Kelm

Mittal

Zheng

et al. 2011

Lecture Notes in Computer Science

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Abstract.Recently conducted clinical studies prove the utility of Coronary Computed Tomography Angiography (CCTA) as a viable alternative to invasive angiography for the detection of Coronary Artery Disease (CAD). This has lead to the development of several algorithms for automatic detection and grading of coronary stenoses. However, most of these methods focus on detecting calcified plaques only. A few methods that can also detect and grade non-calcified plaques require substantial user involvement. In this paper, we propose a fast and fully automatic system that is capable of detecting, grading and classifying coronary stenoses in CCTA caused by all types of plaques. We propose a four-step approach including a learning-based centerline verification step and a lumen crosssection estimation step using random regression forests. We show state-of-the-art performance of our method in experiments conducted on a set of 229 CCTA volumes. With an average processing time of 1.8 seconds per case after centerline extraction, our method is significantly faster than competing approaches.

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